i 

An Introduction to 

Advanced Diagnostics and Prognostics 

For NRC Staff 

Prepared for 

U. S. Nuclear Regulatory Commission 

Office of Nuclear Regulatory Research 

Washington DC 

Prepared by 

SoHaR Incorporated 

Culver City, California 

August 2014 
ii 

 
iii 

Contents 

List of Figures ............................................................................................................................................. vii 

List of Tables ............................................................................................................................................. viii 

Foreword ...................................................................................................................................................... ix 

Acknowledgement ........................................................................................................................................ x 

Executive Summary ..................................................................................................................................... xi 

List of Abbreviations .................................................................................................................................. xii 

1 Introduction ................................................................................................................................................ 1 

1.1 Purpose .......................................................................................................................................... 1 

1.2 Definitions ..................................................................................................................................... 2 

1.3 Scope ............................................................................................................................................. 4 

Part 1: Overview of Advanced Diagnostics and Prognostics ........................................................................ 5 

2 The Environment for Advanced Diagnostics and Prognostics ............................................................. 6 

2.1 An Introduction to the Cause of Failures ...................................................................................... 6 

2.1.1 The Mathematical Model ...................................................................................................... 6 

2.1.2 Failure – Beyond Mathematical Models ............................................................................... 8 

2.1.3 Time Progression of Failure Probability ............................................................................... 9 

2.1.4 End of Life (EOL) ............................................................................................................... 10 

2.2 Assessing the Plant and Equipment State ................................................................................... 12 

2.3 Prevention and Detection of Instrumentation Failures ................................................................ 15 

2.3.1 Self-monitoring (“Smart”) Instrumentation ........................................................................ 15 

2.3.2 Physical Redundancy .......................................................................................................... 16 

2.3.3 Analytical Redundancy ....................................................................................................... 18 

2.3.4 Typical Assessment Questions for Failure Mitigation ........................................................ 18 

2.4 Economics of Diagnostics and Prognostics in the Plant Environment ....................................... 19 

2.5 Summary ..................................................................................................................................... 21 

3 Diagnostic and Prognostic Techniques and Methodologies ............................................................... 22 

3.1 Canaries ....................................................................................................................................... 22 

3.2 Performance Monitoring Techniques .......................................................................................... 23 

3.3 Self-Test ..................................................................................................................................... 24 

3.3.1 Scope of Testing.................................................................................................................. 25 
iv 

3.3.2 Implementation of Self-Test ............................................................................................... 25 

3.4 Multivariate Analysis .................................................................................................................. 26 

3.5 Life Consumption Monitoring .................................................................................................... 26 

3.6 Vibration Analysis ...................................................................................................................... 27 

3.7 Acoustic Techniques ................................................................................................................... 27 

3.8 Targeted Micro/Macroscopic Measurements .............................................................................. 27 

4 Example Applications of AD&P Methods and Techniques ............................................................... 29 

4.1 Introduction ................................................................................................................................. 29 

4.2 AD&P Applied with a Model-Based Approach .......................................................................... 31 

4.2.1 General Attributes ............................................................................................................... 31 

4.2.2 Applicability to Levels in the System Hierarchy ................................................................ 32 

4.2.3 Model-based AD&P applied to a Pneumatic Valve ............................................................ 32 

4.2.4 Typical Assessment Questions that Arise in the Model-Based Methodology ........................... 34 

4.3 AD&P Applied Using a Data-Driven Approach ......................................................................... 35 

4.3.1 General Attributes ............................................................................................................... 35 

4.3.2 Applicability to Levels in the System Hierarchy ............................................................... 36 

4.3.3 Data-Driven Example for Computer Hard Disk Drives ..................................................... 36 

4.3.4 Typical Assessment Questions that Arise in the Data-Driven Methodology ...................... 39 

4.4 Example of AD&P Using a Hybrid Approach ............................................................................ 40 

4.4.1 General Attributes ............................................................................................................... 40 

4.4.2 Applicability to Levels in the System Hierarchy ................................................................ 40 

4.4.3 Hybrid Approach for Rotating Element .............................................................................. 40 

4.4.4 Typical Assessment Questions that Arise in the Hybrid Methodology .............................. 45 

4.5 Example of AD&P Implemented in NPP Internals ..................................................................... 46 

4.6 Summary ..................................................................................................................................... 53 

5 Introducing Diagnostics and Prognostics to the Nuclear Power Environment ................................... 54 

5.1 Deliberative Pathway .................................................................................................................. 54 

5.1.1 Example 1 ........................................................................................................................... 56

5.1.2 Example 2 .......................................................................................................................... 57 

5.2 Opportunistic Pathway ................................................................................................................ 57 

5.3 Uses of AD&P Data .................................................................................................................... 59 

5.4 Quality and Performance Metrics ............................................................................................... 60 
v 

5.4.1 False Positives and False Negatives .................................................................................... 61 

5.4.2 Cost of False Positives and False Negatives ....................................................................... 64 

5.4.3 Prognostic Metrics .............................................................................................................. 65 

6 The Future of AD&P in Nuclear Power Plants and Nuclear Fuel Cycle Facilities............................. 66 

6.1 Passive Safety Features ............................................................................................................... 66 

6.2 Increased Electronic/Digital Capabilities .................................................................................... 67 

6.3 The Field of Prognostics & Health Management ........................................................................ 67 

Part 2: Regulatory Considerations .......................................................................................................... 69 

7 Nuclear Regulatory Commission Documents ..................................................................................... 70 

7.1 AD&P and Plant Equipment ....................................................................................................... 70 

7.2 Requirements Impacting Diagnostics and Prognostics ............................................................... 71 

7.2.1 Single Failure and Independence Criteria ........................................................................... 72 

7.2.2 Regulatory Guide 1.153 ............................................................................................................ 73 

7.3 Technical Specifications ............................................................................................................. 74 

7.4 Modification of Surveillance Frequencies .................................................................................. 75 

7.4.1 Precedents for Modification of Surveillance Frequencies .................................................. 75 

7.4.2 NEI 04-10 Risk Informed Technical Specification Task Force and Regulatory Guides 

(1.177, 1.175, 1.174 and 1.200) .......................................................................................................... 77 

7.4.3 Summary of Surveillance Frequency Requirements ........................................................... 78 

7.5 Documents Specific to Nuclear Fuel Cycle Facilities ................................................................. 79 

8 Industry Standards .............................................................................................................................. 81 

8.1 Body of IEEE Std. 603 ................................................................................................................ 81 

8.2 Annex A of IEEE Std. 603 .......................................................................................................... 83 

8.3 Provisions of IEEE Std. 7-4.3.2 “Standard Criteria for Digital Computers in Safety Systems of 

Nuclear Power Generating Stations” ...................................................................................................... 84 

8.4 Applications of Self-Test and Built-In Test ................................................................................ 85 

8.4.1 Lack of correspondence between a failure and its manifestation. ....................................... 86 

8.4.2 Latent Faults ........................................................................................................................ 86 

8.4.3 Developers (particularly for software) come from a different culture than NPP engineers 

and operators. ...................................................................................................................................... 87 

8.4.4 Visibility of self-test performance. ..................................................................................... 87 

8.4.5 Conclusions ......................................................................................................................... 87 
vi 

8.5 Other Industry Standards and Guides ......................................................................................... 88 

9 Potential of AD&P to Support Plant Safety ........................................................................................ 89 

9.1 AD&P Contributions to Plant Safety Assurance ........................................................................ 89 

9.2 Pathways for the Introduction of AD&P ..................................................................................... 90 

9.2.1 Replacement of Analog by Digital Instrumentation .......................................................... 91 

9.2.2 Introduction into TS Application after Use in Non-TS Application ................................... 91 

9.2.3 Introduction into TS Application without Prior Experience .............................................. 92 

9.3 Evaluation of the Effects of AD&P Operation on Plant Safety ................................................. 93 

10 Conclusions ..................................................................................................................................... 95 

Appendix A Taxonomies and Their Application ........................................................................................ 97 

A.1 Taxonomy of Technologies and Methods Used in Advanced Diagnostics and Prognostics ...... 97 

A.1.1 Research Taxonomy ............................................................................................................ 98 

A.1.2 Review Taxonomy ............................................................................................................. 99 

A.2 Applications: Annotated bibliography of taxonomy entries ..................................................... 101 

A.2.1 Canaries ............................................................................................................................. 101 

A.2.2 Performance Monitoring Techniques ................................................................................ 103 

A.2.3 Self-Test ........................................................................................................................... 113 

A.2.4 Multivariate Analysis ........................................................................................................ 114 

A.2.5 Life Consumption Monitoring .......................................................................................... 116 

A.2.6 Vibration Analysis ............................................................................................................ 118 

A.2.7 Acoustic Techniques ......................................................................................................... 119 

A.2.8 Targeted Micro/Macro-scopic Measurements .................................................................. 121 

Appendix B ............................................................................................................................................... 128 

B.1 Organizations, Centers and Communities ................................................................................. 128 

B.2 Conferences ............................................................................................................................... 129 

Bibliography ............................................................................................................................................. 130 
vii 

List of Figures 

Figure 2-1 Probability density of load and strength ...................................................................................... 7 

Figure 2-2 Overlap of load and strength functions ....................................................................................... 8 

Figure 2-3 Increase in failure probability over time ................................................................................... 10 

Figure 2-4 Economic end of life ................................................................................................................. 11 

Figure 4-1 Pneumatic Valve ....................................................................................................................... 33 

Figure 4-2 Prognostic architecture (Daigle & Goeble, 2011) ..................................................................... 34 

Figure 4-3 Parts of rolling element bearing ................................................................................................ 41 

Figure 4-4 Amplitude modulated signal (http://encyclopedia2.thedictionary.com/antifriction+bearing) .. 44 

Figure 4-5 Noise map of control rod vibration at 1.1Hz (Thie, 1981) ........................................................ 51

Figure 4-6 Ex-core ion chambers in a PWR ............................................................................................... 52 

Figure 4-7 Detector tube in a BWR core .................................................................................................... 52 

Figure 4-8 Data acquisition for Neutron Noise Measurement (Fry, et al., 1984) ....................................... 53 

Figure 5-1 The deliberative pathway .......................................................................................................... 55 

Figure 5-2 Opportunistic pathway for adoption of AD&P ......................................................................... 58 

Figure 5-3 Distributions of diagnostic measurement: (top) a low threshold will incur more False Positive 

(FP) results; (bottom) a high threshold will incur more False Negative (FN) results. ................................ 62 

Figure 5-4 Definitions for False Positive and False Negative interpretations ............................................ 63 

Figure 5-5 ROC curve ................................................................................................................................. 64 

Figure 8-1 Classification in IEEE Std. 603 ................................................................................................. 82 
viii 

List of Tables 

Table 2-1 Immediate Action on Indications ............................................................................................... 14 

Table 4-1 Characteristics of the Three Categories ...................................................................................... 30 

Table 4-2 Smart Attributes (ATA) .............................................................................................................. 38 

Table 4-3 PWR Damage Mechanisms ........................................................................................................ 49 

Table 4-4 BWR Damage Mechanisms ........................................................................................................ 50 

Table 7-1 Equipment Subject to the Maintenance Rule .............................................................................. 71 

Table A-1 Research Taxonomy. ................................................................................................................. 98 

Table A-2 Review Taxonomy ................................................................................................................... 100 

Table A-3 Expansion of the Hybrid Analysis for Electronics .................................................................. 100 
ix 

Foreword 

The term “advanced diagnostics and prognostics” (AD&P) generally refers to the application of 

automated monitoring, incipient failure detection, condition assessment, and prognosis. Properly 

designed and implemented AD&P technologies could be beneficial for safe and reliable operation of 

nuclear power plant (NPP) and nuclear fuel cycle facilities (NFCFs). The goal of this research project 

was to prepare information for reviewing and evaluating licensing applications that propose to integrate 

AD&P into NPPs and NFCFs in the United States. 

This report surveys the technical and regulatory landscape with respect to AD&P techniques and 

methodologies. It is presented in two parts. Part 1 provides a general review of AD&P techniques and 

methodologies. Part 2 identifies regulations, regulatory guidance, and standards that may impact AD&P 

designs and implementations for NPP and NFCF applications. 

Part 1 is intended to provide a general understanding of the types of AD&P technologies that are available 

or may become available in the near future. The discussion does not constitute a comprehensive listing of 

AD&P technologies and methodologies. Example implementations are provided to highlight how AD&P 

methodologies have been implemented and the types of technical questions that might arise in reviewing 

an AD&P implementation in an NPP or NFCF. 

The regulations, regulatory guidance, and standards discussed were also considered for purposes of 

providing an overview. No information was found to indicate that any existing regulations in the United 

States prohibit the safe use of AD&P in NPPs and NFCFs. Digital Instrumentation and Controls Interim 

Staff Guidance-4, however, states that functions that are not necessary for safety, even if they enhance 

reliability, should be executed outside of the safety system [Agencywide Documents Access and 

Management System Accession Number ML083310185]. No information was found to indicate that 

intensive AD&P processes need to be executed within safety systems. More research may be needed to 

determine if additional or changed requirements and guidance is needed for safe implementations of 

AD&P in NPPs and NFCFs. 

This report may be useful to NRC staff as a basic knowledge reference on AD&P to support staff exercise 

of technical judgments during regulatory reviews of applications involving the use of AD&P technology. 

Mauricio Gutierrez Paul Rebstock Russell Sydnor 

 Project Manager Technical Monitor Branch Chief 

Instrumentation and Controls and Electrical Engineering Branch 

Division of Engineering 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission. 
x 

Acknowledgement 

This report benefited greatly from inputs from the sponsors at the Nuclear Regulatory Commission 

(NRC). They reviewed several drafts, pointed out passages that needed clarification or correction and 

guided us to an organization that is expected to meet the needs of the intended audience at the NRC. 

Particular thanks to Mauricio Gutierrez, Paul Rebstock and Russell Sydnor. 

Many thanks are due to our consultants Dr. Xuegao An and Mr. Terry Herrmann who contributed 

substantially to this volume. 

The authors also wish to express their thanks to individuals and organizations that were contacted in the 

preparation of the report and contributed their insights and pointed us to references (in alphabetical 

order): Royce Beacom (NRC), David Berko (INPO), Dan Beverly (AMS Corporation), Dr. Leonard Bond 

(Iowa State University), Dara Cummins (AMS Corporation), Michael Fallin (Fleet Nuclear Engineering), 

Rob Frank (EPRI), Dr. Kai Goebel (NASA Ames), Dr. Hash Hashemiam (AMS Corporation) , Darrell 

Mitchell (AMS Corporation), Dr. Ann Patterson Hine (NASA Ames), Prof. Wesley Hines (Univ. of 

Tennessee), Dr. Gary Johnson (IAEA), Steve Johnson (AMS Corporation), Ryan O’Hagan (AMS 

Corporation), Dr. Pradeep Ramuhalli (PNNL), Richard (Rick) Rusaw (EPRI), Dr. Ramesh Shankar 

(EPRI), Brent Shumaker (AMS Corporation), Richard Stattel (NRC) Debbie Williams (INPO). 
xi 

Executive Summary 

This report on Advanced Diagnostics and Prognostics (AD&P) is composed of two parts. Part 1 provides 

a background to the current field of Advanced Diagnostics and Prognostics and discusses 

implementations that are currently available or are under development and are likely to be proposed for 

use in domestic nuclear facilities. The second part is concerned with regulatory considerations and 

focuses on existing regulations, regulatory guidance, and industry standards that may impact the 

application and use of AD&P in nuclear power plants and fuel cycle facilities. 

The main difference between diagnostics and prognostics lies in their objective: The goal of diagnostics is 

to identify and isolate failed equipment, while the goal of prognostics (also termed predictive diagnostics) 

is to warn of impending failure, and when possible quantify the Remaining Useful Life (RUL) with a 

defined confidence level for a specified set of conditions. 

The body of the report starts with an examination of the failure process; as part of this it is found that in 

many situations empirical criteria are used to define end of life of a component or system. An indication 

of an abnormal plant state can be due to either a failure of the plant or a failure of the instrumentation. 

Techniques for resolving this ambiguity are discussed. Maintenance is usually the largest expense item 

for nuclear power plants, and within this personnel cost are the largest component. AD&P holds promise 

for reducing maintenance labor and this will be an incentive for its adoption by plant operators. 

A variety of AD&P techniques are introduced and their classification and maturity level are discussed. 

There are a few applications in non-safety areas of nuclear power plants that may become the starting 

point for their introduction into functions subject to license. 

Part 2 of the report discusses statutory requirements that may affect the use of AD&P, regulatory 

guidance documents and pertinent industry standards. Self-test and built-in test are the only applications 

currently covered. If the regulatory environment for these and for another AD&P related area, remote 

monitoring, is taken as an example there will be few significant obstacles to future use of AD&P. 

Important additional information is contained in two appendices: 

Appendix A – Taxonomies: two taxonomies for classifying diagnostic and prognostic techniques as well 

as an annotated bibliography of diagnostic and prognostic techniques. 

Appendix B – Resources for Additional Information: a listing of organizations and conferences dealing 

with diagnostics and prognostics 
xii 

List of Abbreviations 

AD&P Advanced Diagnostics and Prognostics 

ASME American Society of Mechanical Engineers 

AEC Atomic Energy Commission 

BIST Built In Self-Test 

BIT Built In Test 

CALCE Center for Advanced Life Cycle Engineering 

CDF Core Damage Frequency 

COTS Commercial Off The Shelf 

DOE Department of Energy 

EDAC Error Detection and Correction 

EOL End Of Life 

EOUL End Of Useful Life 

EPRI Electric Power Research Institute, Inc. 

IAEA International Atomic Energy Agency 

IASCC Irradiation Assisted Stress Corrosion Cracking 

I&C Instrumentation and Control 

ICFD In-Core Flux Detector 

IDP Independent Decision-making Panel 

IEEE Institute of Electrical and Electronics Engineers 

IGSCC Inter-Granular Stress Corrosion Cracking 

INPO Institute of Nuclear Power Operations 

IROFS Items Relied On For Safety 

IST In Service Testing 

ISG Interim Staff Guidance 

LER Licensee Event Reports 

LERF Large Early Release Frequency 

NEI Nuclear Energy Institute 

NFCF Nuclear Fuel Cycle Facility 

NPP Nuclear Power Plant 

NRC U.S. Nuclear Regulatory Commission 

OLM Online Monitoring 

O&M Operation and Maintenance 

OTS Off-the-Shelf 

PHM Prognostics and Health Management 

PRA Probabilistic Risk Assessment 

RG Regulatory Guide 

RI-IST Risk Informed In Service Testing 

RUL Remaining Useful Life 

SFCP Surveillance Frequency Control Program 

SR Surveillance Requirements 

SRP Standard Review Plan (SRP) 
xiii 

SSC Systems, Structures, Components 

TS Technical Specification 

TSTF Technical Specification Task Force 

UUT Unit Under Test 

V&V Validation and Verification 

 
1 

1 Introduction 

This report on Advanced Diagnostics and Prognostics (AD&P) consists of two parts: Part 1 describes 

methods and techniques in current use or expected to be available in the foreseeable future, including but 

not restricted to Nuclear Power Plants (NPPs) or Nuclear Fuel Cycle Facilities (NFCFs). Part 2 deals with 

regulatory considerations relevant to the implementation of AD&P in NPPs and NFCFs. 

The information and recommendations in this document are intended to help the NRC determine whether 

regulations, guidance, or Technical Specification provisions should be created or updated to address 

proposed uses of AD&P in domestic nuclear power plants. The NRC may also use the content in this 

document to determine if review procedures and acceptance criteria for the evaluation of proposed 

implementations of AD&P should be developed. 

The field of diagnostics and prognostics of engineered equipment covers many engineering disciplines 

and has given birth to diverse technologies that are applied to the monitoring of different types of 

equipment. The technologies and methodologies sampled in this report were chosen because they are 

currently applicable or may become applicable to the nuclear field. The technologies and methodologies 

discussed do not constitute a comprehensive listing. 

The main difference between diagnostics and prognostics lies in their objective: The goal of diagnostics is 

to identify and isolate failed equipment, while the goal of prognostics (also termed predictive diagnostics) 

is to warn of impending failure, and when possible quantify the Remaining Useful Life (RUL) with a 

defined confidence level for a specified set of conditions.1

 

Although implementations of specific methods for diagnostic goals are often much more mature than 

implementations directed towards prognostic goals, this report acknowledges the common physical and 

algorithmic roots by including them under the same umbrella. Where appropriate the maturity of 

diagnostic and prognostic techniques is assessed separately. In general, the field of prognostics is 

evolving with much research and experimentation but with few current implemented solutions in industry, 

and fewer yet that are reported for NPPs or NFCFs. 

 

1.1 Purpose 

The first part of this report presents an overview of the field of advanced diagnostics and prognostics 

along with a collection of representative methods. It discusses the goals and reasons for use of AD&P and 

the safety and cost ramifications of its use in nuclear facilities. For this purpose examples of AD&P 

 

1 Self-Test and Built In Test are internal implementations of prognostics and diagnostics. 
2 

techniques are presented and some are described in detail to show the reasons for their adoption as well as 

the many steps that were necessary for their development. Throughout this report generic assessment 

questions for AD&P methodologies are included to alert the reader to possible pitfalls in their 

implementation. 

1.2 Definitions 

The term “Advanced Diagnostics and Prognostics” (AD&P) refers generally to the application of 

automated monitoring, condition assessment, and incipient failure detection. It also includes self-testing 

of digital systems and calibration verification at digital system interfaces. In particular the term 

“advanced” is used here to include diagnostics and prognostics that will be employed in advanced nuclear 

power plants and fuel treatment facilities, and will often rely on sophisticated monitoring capabilities and 

requirements. The terms diagnostics and prognostics (D&P) are interpreted as the methodologies for 

performing diagnoses and prognoses, respectively. Therefore definitions of diagnosis and prognosis are 

also presented. 

An early definition of Diagnosis as it is applied to engineered equipment can be found in MIL-STD 

1309D (MIL-STD-1309D, 1992) clauses 3.1.161 and 3.1.166: 

3.1.161 Diagnosis. The functions performed and the techniques used in 

determining and isolating the cause of malfunctions. 

3.1.166 Diagnostic test. A test performed for the purpose of isolating a 

malfunction in the Unit Under Test (UUT) or confirming that there actually is a malfunction. 

Advanced diagnostics will often rely on multiple tests and methods and may be applied to conditions that 

cannot be fully diagnosed by conventional methods. The multiplicity of tests and evidence raise it from a 

task to an engineering discipline or art. A definition that more accurately describes the current and future 

field of diagnostics is: 

DEFINITION 1 

Diagnostics – The art of identifying and correlating the results of multiple tests to 

determine system condition and status and to generate the input required for optimal 

maintenance. 

According to the International Organization for Standardization, the term “failure prognostics” 

corresponds to the “estimation of the time to failure and the risk for one or more existing and future 

failure modes” (AFNOR, 2005). 
3 

The definition for generating a prognosis in MIL-STD 1309D is given by clause 3.1.477: 

3.1.477 Prognosis. The use of test data in the evaluation of a system or equipment for 

determining the potential of impending faults. 

This definition does not refer to a quantitative time estimate for the impending faults, only to the 

possibility of them occurring. Prognostic goals include the notion of Remaining Useful Life (RUL): 

Prognostics, or Predictive Diagnostics – The process of predicting the occurrence of failures to a 

system, device or process based on predictable time domain failures (AFNOR, 2005)

Remaining Useful Life – The predicted time to a failure beyond which the system can no longer be 

used to meet desired performance. 

A definition of prognostics that allows for a more open and general description of both the process of 

generating a prognosis as well as the resulting prognostic information is given by Henry C. Pusey (Pusey, 

1996) as “the art or act of predicting future conditions on the basis of present signs and symptoms”. 

Advanced prognostics, in much the same way as advanced diagnostics, will rely on multiple tests and 

methods and may be applied to conditions that cannot be fully identified and diagnosed by conventional 

methods. The multiplicity of tests and evidence raise it from a task to an engineering discipline or art. A 

definition that more accurately describes the current and future field of prognostics is: 

DEFINITION 2 

Prognostics – The art of identifying and correlating the results of multiple tests to 

determine system condition and status and to generate a prediction of future status. 

In subsequent chapters to this report, Definition 1 and Definition 2 are the basis for the discussion of 

diagnostics and prognostics. 

Within the spectrum of diagnostics and prognostics we also include self-test or built-in-test. These can be 

diagnostic in nature, prognostic, or both: 

3.1.543 Self-test. A test or series of tests, performed by a device upon itself, which shows 

whether or not it is operating within designed limits. This includes test programs on 

computers and diagnostic tests (MIL-STD-1309D, 1992) 

Built-in test (BIT). An integral capability of the mission system or equipment which provides an 

automated test capability to detect, diagnose or isolate failures. (MIL-STD-2165, 1985)

Self-test is usually carried out by means of a digital processor and its scope is sometimes restricted to the 

digital portions of the equipment. This restriction does not apply to BIT. 
4 

The ability to effectively utilize advanced diagnostics and prognostics depends on a multitude of 

technological advances which may be specific to each diagnostic or prognostic methodology. However, 

the requirement for Online Monitoring (OLM) is shared by many if not a majority of advanced 

diagnostics and prognostics as they rely on intensive trending of temporal data. 

The term On-line Monitoring in the context of nuclear plants has been used for two separate functions 

which are both covered in the definition that was given in the Light Water Reactor Sustainability 

Workshop on On-Line Monitoring Technologies (Baldwin, et al., 2010): 

On-line monitoring: Automated methods of 1) monitoring instrumentation and assessing instrument 

calibration and, 2) assessing the health condition of components, equipment, and systems, including 

both active and passive parts of the plant, while the facility is operating. 

 

This report is concerned only with on-line monitoring that is required to enable AD&P (the second 

definition). OLM for the purpose of calibration was covered in an earlier report (Hines, et al., 2008),

(NUREG/CR-6895). The performance and safety requirements of OLM techniques, as they are applied to 

AD&P, are essentially similar to the requirements for the purpose of online calibration. Since these have 

been treated comprehensively in NUREG/CR-6895 they are not discussed in this document. These 

requirements include technological as well as management issues: band width, resolution, as well as 

redundancy policies. 

1.3 Scope 

This report investigates AD&P as they are currently, or may be in the future, applied to equipment found 

at nuclear facilities. This includes both nuclear generating stations and commercial fuel cycle facilities 

involved in conversion, enrichment, and fuel fabrication. This effort addresses all types of equipment that 

support production and processing in nuclear facilities. 

Although the list of applications included in this report cannot be exhaustive of the field, an attempt was 

made to include a sample that covers broadly the prominent techniques, particularly those believed to 

become relevant in licensing requests in the decades to come (e.g. items used in control systems, various 

sensors, transformers, structures and electromechanical motors, valves and pumps). 

Safety concerns influence the scope and manner of utilization of AD&P in nuclear facilities. This report 

also discusses the issues of monitoring the monitor and defense in depth in this context. 

 
5 

Part 1: Overview of Advanced Diagnostics and Prognostics 

Part 1 of this report starts with Chapter 2 “The Environment for Advanced Diagnostics and Prognostics”. 

Chapter 2 discusses the cause and time progression of failures, the economic backdrop for AD&P, and the 

interaction of diagnostics and prognostics with the instrumentation and control of nuclear power plants 

and nuclear fuel cycle facilities independent of the type of diagnostics or prognostics that are used. 

Chapter 3 “Diagnostic and Prognostic Methodologies and Techniques” provides a sampling of existing 

and proposed techniques that cover a broad spectrum of applications. In Chapter 4 “Example 

Applications” five of these techniques are described in detail so that the steps necessary for their 

development and use become visible. Chapter 5 “Introducing Diagnostics and Prognostics to the Nuclear 

Power Environment” discusses ways by which AD&P may currently or in the future find its way into 

NPPs and NFCFs. Chapter 6, “The Future of AD&P” attempts to outline the way in which the field may 

develop. Appendix A provides more detail on the example techniques as well as taxonomies that permit 

generalizing some of their essential features. Appendix B lists organizations, publications and conferences 

in the growing field of diagnostics and prognostics. 

 
6 

2 The Environment for Advanced Diagnostics and Prognostics 

This chapter addresses factors that affect selection and deployment of advanced diagnostics and 

prognostics in nuclear power plants and fuel cycle facilities regardless of the techniques employed. 

Diagnostics requires an assessment of the current state of the plant equipment, and prognostics involves 

using a history of equipment states to generate an estimate of a future equipment state. Understanding the 

failure process and its manifestations is essential for all activities in this field and that topic is addressed 

in section 2.1 and its subsections. All diagnostic and prognostic techniques depend on information about 

how the plant and equipment state is acquired. This topic is addressed in Section 2.2 and points to the 

crucial role of instrumentation. Nuclear facilities of the next generation will make increased use of 

electronic instrumentation and Section 2.3 discusses techniques for prevention and detection of failures in 

electronic instruments. Section 2.4 discusses the economic motivation that may lead the industry to 

adoption of advanced diagnostics and prognostics. A summary paragraph concludes this chapter. 

2.1 An Introduction to the Cause of Failures 

The discussion of cause of failures begins with an idealized case where there is only one failure mode that 

affects plant operations and where the distributions of load and strength are known. Dynamic elements of 

the load (cycling vibrations, etc.) are initially neglected. The approach can be applied directly to 

mechanical structures but is also a conceptual basis for failures in other areas. 

2.1.1 The Mathematical Model 

In general, failure occurs when the load (with respect to the failure mode) exceeds the strength. This can 

be caused by an increase in the load (induced failure) or by a decrease in strength (due to fatigue or time 

related wear-out). 

Hypothetical load and strength distributions are shown in Figure 2.1. Arbitrary units of load or strength 

are plotted along the horizontal axis, and the vertical axis represents the probability density of the load or 

strength taking the values along the x-axis. The probability for failure at any point is the product of two 

factors 

• The probability that the strength has the value noted at the x-axis and 

• The cumulative probability that the load has at least that value 

Typically, because the centers of the strength and load distributions are separated and the tails of the 

distributions are small one can visualize this product by means of the “overlap area”. In this instance there 

is indeed an area of load and strength overlap at about 1900 x-axis units as shown in the enlarged view of 

Figure 2-2: The section from around 1700 x-axis units to approximately 2100 x-axis units will generate 

the main contribution to the overall failure probability. In the left part of the figure (near 1700 x-axis 

units) the failure probability is low because only a very small part of the strength distribution lies there: 

the system is unlikely to be at a state of such low strength. Likewise, towards the right side of the figure 

(near 2100 units), the failure probability is small because only a very small portion of the load distribution 
7 

reaches this value: it is unlikely that the system will be loaded to this extent. These curves represent a 

specific state of the system. They reflect the load profile (which will vary between installations and usage 

patterns) and they reflect the system history (simply put the age, experience and wear-out reflected in the 

strength distribution). This diagram can represent a young system that will fail only under extreme 

(continuous or random/transient) loads or an older system that due to wear will show a significant low 

strength tail in the distribution. For any equipment there will be a shift of the strength curve downwards 

and there may be transient shifts of the load upward. Hence with time, the product of these distributions 

will increase with potentially transient large fluctuations due to load variations. Aside from these 

generalities, it is difficult to extract from this representation failure probabilities that can be the source for 

a reliable diagnostic or prognostic warning. 

FIGURE 2-1 PROBABILITY DENSITY OF LOAD AND STRENGTH

-0.0005

0

0.0005

0.001

0.0015

0.002

0.0025

0 500 1000 1500 2000 2500 3000 3500

Load

Strength
8 

FIGURE 2-2 OVERLAP OF LOAD AND STRENGTH FUNCTIONS

 

2.1.2 Failure – Beyond Mathematical Models 

If the types of distributions and their parameters are known, a failure probability can be calculated by 

established methods. The precision with which the result can be displayed may be mistaken for accuracy 

of a safety prediction. This invitingly easy procedure masks several difficulties that affect all approaches 

to AD&P. These include the physical (real world) definition of failure, the uncertainties in the load and 

strength parameter estimation, as well as multiplicity of failure modes and their possible interactions. In 

order to implement a diagnostic procedure one must know how to infer the load and strength distributions 

from the available measurements. In order to implement this knowledge in a prognostic procedure, one 

must also know how the curves evolve with time and usage. 

In diagnostics the central problem is the identification of a failed or degraded element and in prognostics 

it is to estimate the time to failure on an element. Both depend on a definition of failure. Does an item fail 

when it can no longer perform a specified function, perhaps even destroys itself, or does it fail as soon as 

it deviates in any way from its specification? It is useful to shift the terminology and focus from failure to 

the end of useful life: the point at which the system requires maintenance attention or replacement (either 

in anticipation of failure or after failure). The end of useful life threshold of an item may depend on the 

application, its importance to safety, the required availability and other factors. The best recognized end 

of life used to be burn-out of an incandescent light bulb. But in many commercial settings bulbs are 

replaced when the light output drops below a given fraction of the original value, thus making even this 

end of life event subject to empirical criteria, In addition, the selection of an end of useful life criterion 

1700 1800 1900 2000 2100 2200

Load

Strength
9 

will have administrative and resource implications (state of plant required for maintenance, personnel and 

spares availability). Thresholds that are set for very early recognition of failure result in unnecessary 

maintenance or replacement; if failure is recognized very late the reliability of the system will suffer as 

the approach to EOL may be missed. The economic implications of this are presented in subsection 2.4. 

 

In most cases an appropriate definition of end of useful life can be arrived at by restricting it to a specific 

failure mode and accepting a threshold that is obtained from empirical data. Thus, even where there is a 

validated mathematical model for a failure process there will be a subjective element in setting the 

criterion for end of useful life. This issue is discussed in more detail in section 2.1.4. 

 

The task of generating a credible model for a given failure mode involves selecting suitable statistical 

distributions either for load and strength or, more frequently, for time to failure as a random variable and 

then estimating parameters from the available data. There is no lack of statistical tools to help with this 

task: goodness of fit tests (Taylor, 1997) can validate the model selection, and regression techniques (Fan 

& Gijbels, 1996) can be used to estimate parameters. However, most statistical techniques will evaluate 

the distribution over its entire range. Naturally they are weighted towards the center of the distribution 

which is the high probability region. However, to estimate the end of life, one is actually interested in 

accuracy of the tails of the distributions as this is where the significant contributions to the failure 

probability reside. In the region of high probability of the strength distribution the load distribution will 

be small, therefore offering only an uncertain contribution to the overall failure probability. Conversely, 

in the area of high probability of the load distribution, the strength probability will be very small, leading 

to the same uncertainty, The conventional goodness of fit tests and parameter estimators can provide no 

assurance of validity at the extremes, the area where the probability of failure for components of nuclear 

plants is typically evaluated, e. g., at 10-3 or less. There are also non-parametric methods (Tukey, 1977) 

available but they either require large amounts of data in the range of interest, or else result in very wide 

confidence intervals. Hence they are less useful than parametric methods in generating reliable diagnostic 

and prognostic results. 

In spite of their limited accuracy, statistical failure models are useful in the evaluation of alternatives and 

in outlining rather than pinpointing failure probabilities. Defense in depth which is mentioned in several 

places in this report is one way of accommodating the limitations of these models. 

2.1.3 Time Progression of Failure Probability 

Over time the failure probability introduced in section 2.1.1 may change. Statisticians may view this as 

primarily due to a decrease in the mean strength and an increase in the standard deviation of strength. 

Materials scientists may view it as a result of fatigue, stress corrosion or similar processes. In either view, 

the failure probability will exhibit an increasing trend as shown in Figure 2-3. 
10 

FIGURE 2-3 INCREASE IN FAILURE PROBABILITY OVER TIME

The horizontal axis represents arbitrary units of duty time of an element; the vertical axis represents 

failure probability. The figure is hypothetical and the scale markings are provided to facilitate reference to 

segments of the curve. Eventually the failure probability will level out and not increase beyond 1. In this 

representation the end of useful life may be indicated by the maximum acceptable failure probability. As 

an example, if 0.0002 is selected, the useful life is slightly under 15 units of time. If the equipment has 

operated for 10 units of time the remaining useful life would be estimated as 5 units of time. 

A refinement of this methodology recognizes that the progression to failure is not simply a matter 

of time but also depends on the stress experienced during the interval. A very common use of this 

approach is when a used car is evaluated on both age and miles driven. In NPPs an overall 

indicator of the stress experienced by the components is the output power level. The relationship 

between output power and stress on components may not be linear and may vary among 

components. The remaining life of the turbine and generator are much more likely to be affected 

by the output power level of the reactor than the control room ventilation system. Other factors 

that may affect the remaining useful life are temperature cycling and load transients. These 

considerations of the time progression of the failure probability are applicable to all AD&P 

approaches. 

2.1.4 End of Life (EOL) 

The previous discussion has been concerned with the processes leading to failure or EOL. The 

current heading looks at management issues and how the EOL definition impacts the validity of 

diagnostic or prognostic analyses. In this context the term EOL includes three possible events 
11 

• Physical failure; 

• Reaching a condition where economic factors indicate replacement or repair (see below); 

• Reaching a condition where possible damage to associated equipment makes replacement 

or repair desirable. 

For each system, EOL should be defined while considering: criticality of equipment, the timeprogression to failure, the specific maintenance procedures and resources. EOL is defined as a 

measurable state or condition of the equipment. Once it is established, it pertains both to load 

induced failures and time-related wear out, which identify the two ends of the spectrum of failure 

dynamics. Whether the strength distribution moves to lower values, or increases in variance; or 

the load distribution increases, the overlap of non-negligible areas of the distributions will lead to 

measurable conditions that have the same effect on EOL. 

 

An economic criterion for end of life is shown in Figure 2-4 and may be constructed as shown on 

the following page. 

FIGURE 2-4 ECONOMIC END OF LIFE

• Deferring the repair or replacement of an element will in general increase the cost of 

maintenance (included in this may be the cost of plant shut-down due to complete failure 

of the element). This is shown as the Cost of Deferral in Figure 2-4 and it is related to the 

increase in failure probability shown in Figure 2-3. 

• Conducting repair or replacement early will deprive the operator of remaining useful life 

of the element. This is shown as Cost of Removal from Service in Figure 2-4 and is 

0

2

4

6

8

10

12

14

16

18

20

Selected End of Life

Cost

Cost of Removal

Cost of Deferral

Total Cost
12 

assumed to be a linear function of time up to the point where complete failure of the 

element is a certainty (at the right in the figure). 

The minimum of the total cost curve may be termed the economically optimum end of life. The 

minimum of the total cost curve is shallow (this is a general and important characteristic of these 

trade-off representations), meaning that the cost will not significantly increase by moving the 

selected end of life a small distance to the left or right. This is fortunate because of the difficulty 

of determining the parameters for this economic EOL model. Although it is unlikely that 

economic criteria will be rigorously applied in this regard, the concepts developed here will enter 

into making EOL selection or assumptions, particularly in non-safety critical plant functions. 

In the NPP environment there are other significant factors entering into the decisions regarding 

the time to repair or replace equipment, including opportunistic access during plant shut-down to 

locations that are inaccessible during normal plant operation. 

Some other factors entering into EOL selection may include 

• A given percentage reduction in the original strength or output of an element; the 

percentage may be in accordance with an accepted standard 

• Deviation from the equipment specification 

• Output degradation that causes performance degradation of the next higher level in the 

equipment hierarchy. 

As these considerations indicate there are no universally accepted rules for EOL definition. 

However, EOL assumptions in connection with diagnostics and prognostics should meet the 

following criteria: 

1. Is the EOL definition clearly stated and is its basis described? 

2. Is there experience with the use of this EOL definition? 

3. Does the definition require additional instrumentation or computing? What is the 

experience with these additional capabilities? 

4. Will equipment operating at the EOL threshold still permit the next higher levels in the 

hierarchy to meet their performance specification under all design basis conditions, 

including failure of a redundant element? 

2.2 Assessing the Plant and Equipment State 

One of the key questions for plant operators is: “Is the plant in an operational state?” This 

question is asked with respect to each attribute required for safe and effective operation. If the 

answer is yes for the current attribute, the question is repeated for the next attribute of plant 

operation. If the answer is no, the next question is “What action needs to be taken to bring the 

plant to an operational state?” This highly simplified diagnostic loop is diagrammed in Figure 2-

5. The term plant in this and the following figures should be understood as plant and/or 

equipment. The attributes to be observed are items taken from the plant or equipment 

specification, shown in the figures as “Plant Specification Item”. The process is visualized as 

sequential but in practice some observations may be taken at the same timhhe. 
13 

FIGURE 2-5 SIMPLIFIED DIAGNOSTIC LOOP

To be in an operational state the plant or equipment must meet every item of its specification. The 

terms “operational” and ”non-operational” have regulatory implications that are not addressed 

here; the interaction of AD&P and the plant Technical Specification is discussed in Section 7.3. 

The action to be taken when there is a deviation from the specification is termed ‘mode change’ 

in the figure, and this can mean maintenance action, reduction in output, complete shutdown, or 

switching from the primary to a stand-by channel. 

This report is concerned with AD&P as it is applied to all types of equipment in NPP and NFCF. 

However, because AD&P usually depend on automated, and often high frequency data collection, 

one will often find they are supported by plant instrumentation. When discussing AD&P even of 

purely mechanical structures this must be taken into account, and one should also analyze the 

failure behavior of the supporting instrumentation. Knowledge of the plant state is obtained 

through instrumentation as shown in Figure 2-6. This introduces ambiguity into the assessment: is 

a deviation from normal indication the result of a plant state or of an instrumentation problem? 

Even worse, can an abnormal plant state be masked by an instrumentation failure? The detection 

of instrumentation failures will be further explored in section 2.3. 

To assess the operational or safety consequences of potential failure modes it is convenient to 

consider two types of failure modes of the instrumentation separately: 

1. Masking failure modes in which the failed instrumentation mimics a well performing 

plant, regardless of the actual state 

2. All other instrumentation failure modes (non-masking). 
14 

FIGURE 2-6 THE CRUCIAL ROLE OF INSTRUMENTATION

The combinations of plant or equipment and instrumentation states and the immediate actions 

based on the indications are summarized in Table 2-1. 

Table 2-1 Immediate Action on Indications 

Instrumentation State

Operational Failed 

Masking 

Failed 

Non-Masking 

Plant or

Equipment

State

Operational None 

required 

Non-immediate 

maintenance 

required 

Intervention required 

Failed Required 

and taken 

Required but not 

taken 

Required and taken 

As long as the plant and the instrumentation are operational, there is no action required. When the 

plant is operational and the instrumentation fails in a masking mode there is still no immediate 

safety related action required but the instrumentation has to be repaired. When the plant is 

operational and the instrumentation fails without masking, the immediate action the result will be 

an unnecessary repair of plant equipment. Therefore, instrumentation should be provided with 

diagnostics that resolve the ambiguity. When the plant fails and the instrumentation is 

operational, maintenance is properly directed at the plant. Instrumentation diagnostics can 

confirm that this is the appropriate action. 
15 

However, if the plant fails and the instrumentation fails in a masking mode, a potentially much 

more dangerous condition arises. This represents a latent plant or equipment failure where 

mitigation provisions are bypassed because the failure is not observed. The prevention of this 

condition for critical functions must be a primary focus of any diagnostic effort. A latent failure 

in a normally unused function, e. g., one that is used only for mitigation or recovery, represents a 

particularly dangerous state because it can disable the entire mitigation or recovery process. The 

prevention of such failures is discussed in the next section. 

The last combination in Table 2-1 represents a plant failure and a non-masking instrumentation 

failure. The immediate efforts are the same as for any detected plant failure and intervention is 

required. Instrumentation diagnostics should be provided to present a true picture of this 

condition. 

2.3 Prevention and Detection of Instrumentation Failures 

As already mentioned, the implementation of AD&P programs involves use of instrumentation. 

The preceding section has shown the importance of monitoring instrument operation so that 

failure of the instrumentation can be distinguished from a plant failure, and particularly to prevent 

masking instrumentation failures. Even though the probability of an instrumentation failure in the 

masking mode may be very low (and may be further reduced by AD&P efforts) it must be 

protected against, because it can mask unsafe conditions. 

The following discussion looks at measures to prevent the effects of instrumentation failures, 

particularly those that lead to potentially catastrophic latent plant or equipment failures. The 

material is particularly aimed at electronic instrumentation where random failures predominate. 

The topics are: 

• Self-monitoring (“smart”) instrumentation 

• Physical redundancy 

• Analytical redundancy 

These measures that are individually described below are not mutually exclusive and can be 

combined in any manner. This furnishes an opportunity for defense-in-depth. 

2.3.1 Self-monitoring (“Smart”) Instrumentation 

Smart instrumentation includes a monitoring function that ideally signals any deviation from the 

specified performance and that may be considered as a diagnostic provision. For the system 

designer a major advantage is that this is a self-contained solution to the instrumentation failure 

problem. When the monitor detects a deviation from the specified performance, the instrument 

output is inhibited; thus masking failures are made much less likely. The operation of smart 

instrumentation can be diagrammed as shown in Figure 2-7. 
16 

While this may be viewed as just moving the ambiguity problem to the next lower level, there are 

several factors that can reduce the probability of an undetected masking failure by at least an 

order of magnitude. The monitor is usually much less complex than the instrumentation and it is 

designed to have a much lower failure probability. In addition the design will specifically aim at 

avoiding masking failure modes, e. g., by making the OK indication a pattern (e. g., three evenly 

spaced pulses) rather than a single line going high or low. 

FIGURE 2-7 SMART INSTRUMENTATION

2.3.2 Physical Redundancy 

Physical redundancy of instrumentation channels is frequently employed in current NPPs and 

NFCFs. It is here discussed as a tool for detecting masking instrumentation failures. A masking 

instrumentation failure is a rare event and thus it should be possible to transform this into a highly 

unlikely event by using two separate instrumentation functions in a logical AND configuration 

(both must indicate the plant to be operational to yield a YES output). Such an arrangement is 

shown in Figure 2-8. For the system designer this is attractive if the existing instrumentation has 

been shown to be dependable so that installation of an additional component does not represent 

undue risk of false alarms or maintenance requirements. 
17 

FIGURE 2-8 PHYSICAL REDUNDANCY

If the AND gate receives unequal inputs it indicates an instrumentation failure. If it receives two 

NO inputs it indicates a plant failure. The Instrumentation blocks in Figure 2-7 include not only 

the measurement and analysis component but also the communication paths up until the point that 

they enter the “&” gate. If instrumentation is more than doubly redundant, the above scheme can 

be applied to each combination of two of the channels. In case of an instrumentation failure this 

will permit identification of the failed channel. 

For masking failures in particular, an additional monitoring step can be useful: sampling the 

difference of the output of two or more independent instrumentation channels. In the case of a 

masking failure the AND gate may accept the two signals and miss small variations in the output. 

However, monitoring the difference between the two signals will highlight even small variations. 

If these small variations either exceed a certain threshold (can be much smaller than the threshold 

of the AND gate) over a certain period of time, or show certain patterns it can be an indication of 

instrumentation wear-out or degradation. 
18 

2.3.3 Analytical Redundancy 

In analytical redundancy the equivalent of an essential instrumentation item is constructed from 

one or several other item(s) based on an analytical relationship between them. The output of the 

analytical redundant configuration can be used as a redundant element to prevent fault masking 

failures similar to the configuration shown in Figure 2- 8. A simple analytical equivalent is 

shown in Figure 2-9 in which the critical data item is the pressure at the output of User A (PA). 

An analytically redundant data item can be constructed from the pressure at the output of User B 

(PB) plus the differential pressure (DP), treating the latter as an arithmetic signed quantity. From 

the system designer’s point of view this appears as a very advantageous solution since all the 

major pieces are already in place and the only new requirement is a combining algorithm. 

FIGURE 2-9 ANALYTICAL EQUIVALENT OF PA 

2.3.4 Typical Assessment Questions for Failure Mitigation 

Applications of advanced diagnostics and prognostics will often require the use of 

instrumentation for rapid assessment of equipment states. Confidence in this instrumentation is a 

necessary prerequisite for the implementation of the AD&P. As discussed in the introduction to 

this document, this report does not address online monitoring for the extension of safety critical 

sensor calibration intervals. These are covered by NUREG/CR-6895 (Hines, et al., 2008). 

Therefore OLM is not discussed in detail. That said, online monitoring is usually a requirement 

for an AD&P program, and therefore it is always in the background of such applications. 

Concern with the instrumentation suitability and dependability for AD&P can give rise to 

questions such as the following (these are by no means intended to exclude others): 
19 

Questions when smart instruments are being considered include: 

1. Complexity – does the inclusion of the monitor raise the complexity of the 

instrumentation to an unacceptable level? 

2. How dependable is the operation of the monitor? Can the operation of the monitor be 

observed and tested by a supervisory program? 

3. Will the expected frequency of shut-down of a given instrument present a plant safety 

problem? In particular will the rate of false positive alarms (events where a positive 

diagnosis of failure is false and the equipment has not failed, or is not close to failure) 

affect plant safety? 

Questions when physical redundancy or sampling is being considered include: 

4. Correlated or common mode failures – are there design features that can cause both 

components to fail in the masking mode under some external stimulus (earthquake, fire, 

etc.)? This can be alleviated by using diverse designs but that negates the benefit of using 

one trusted component. 

5. Can failures in the combining algorithm or its implementation permit a single masking 

output to be interpreted as a double masking output? 

6. Can an undetected failure in one of the instrumentation channels permit a failure in the 

second channel to propagate unchecked? This concern can be substantially alleviated by 

periodic testing of the individual channels through a supervisory program. 

7. Can failures in the sampling or modulation circuits permit a single masking output to be 

interpreted as a double masking output? 

Questions when analytical redundancy is being considered include but are not limited to: 

8. Will the combining algorithm hold under all conditions? Consider specifically the 

transient response of all elements involved. 

9. Will a failure of the combining algorithm permit either the primary or the analytical 

channel alone to furnish output? 

10. Will a single channel failure go undetected? This concern can be substantially alleviated 

by periodic testing of the individual channels through a supervisory program. 

2.4 Economics of Diagnostics and Prognostics in the Plant Environment 

The benefits of diagnostics and prognostics are generally more widely publicized than their cost 

(Feldman, et al., 2008), with the latter particularly difficult to estimate where compliance with 

industry standards or regulatory approval is required. For this reason it is expected that AD&P in 

nuclear power plants and fuel cycle facilities will initially be introduced in plant systems that are 

not safety related. 

 

In manufacturing, transportation and process industries diagnostics are an accepted part of plant 

and vehicle operations because they offer the following economic benefits, among others: 

• Permit component failures to be isolated at a local level before they can propagate to 

global effects (e. g., replacement of a turbine blade rather than the turbine). 
20 

• Provide information to maintenance personnel that permits them to arrive with the right 

tools and replacement parts; an important associated benefit is the early resumption of 

operations. 

• Facilitate the collection of fleet-wide data that can lead to detection of failure trends 

affecting a universe of components (more about this later). 

Prognostics offer the following additional benefits, again not restricted to the nuclear industry and 

without claiming to be a comprehensive listing: 

• Reducing the need for very expensive find-and-fix maintenance. 

• Permitting the conversion of necessarily conservative fixed schedule maintenance into 

condition based maintenance. 

• Avoiding the need for a large spare parts inventory by facilitating just-in-time ordering. 

The term prognostics usually means a process for estimating remaining useful life or determining 

an economically optimal replacement time. Most current literature assumes that the decision 

making is at least partly automated but this is not necessary to reap substantial benefits. Experts 

are performing what may be termed manual prognostics when they decide that a feedwater pump 

needs to be replaced during the current refueling outage to avoid the very large cost of an 

unscheduled plant shutdown. The manual data taking and trending involved in this work are 

usually highly labor-intensive and therefore significant economic benefits can be anticipated from 

automating it. These benefits can be achieved without most of the R&D investment that is 

required for establishing a basis for conventional prognostics (special sensors, computers, etc.). 

The change to an automated system will usually require a change in the TS and NRC agreement 

if it involves altering a regulated inspection schedule. 

At this point some fundamentals of NPP economics may be informative. Operations and 

maintenance costs account for 60 – 70% of the total cost of generating nuclear power in the 

United States (Coble, et al., 2012), and 80% of the O&M cost is accounted for by labor (Wacker, 

et al., 2007). Thus each plant operator can be expected to be motivated to automate maintenance 

and thus to reduce the labor expense. On a wider scale it has been estimated that savings of over 

$1 billion per year are possible in the United States when prognostics and health management are 

applied to all key equipment in legacy power plants (Bond, et al., 2011). 

In 2008 Exelon, one of the largest operators of NPPs in the United States, installed InStep's 

PRiSM software for Centralized Performance Monitoring (a form of fleet-wide data collection 

and analysis) (Newswire, 2008). With 500 deployed plant equipment computer models, it is 

estimated that Exelon avoided over $500,000 in equipment and production losses in the first two 

months of operation from early warning notification of potential equipment problems. 

So far the economic factors all seem to favor adoption of advanced diagnostics and prognostics. 

But of course their application to NPPs and NFCFs cannot be at the expense of safety. Therefore 

all assessment issues, sometimes presented as questions, in the previous headings remain valid. In 

addition, whenever one considers the automation of an activity that had been carried out by a 

human being, it must be asked whether collateral surveillance is not being jeopardized. Do the 
21 

maintainers only read a dial and enter a value in a data sheet, or do they also look for leaks on the 

floor or an unusual smell? The continued need for unstructured surveillance must be considered 

in the economic analysis when automation replaces human activities. 

2.5 Summary 

This chapter has introduced models that explain the failure process and several definitions of 

failure or end of life. Diagnostics and prognostics are concerned with observing and assessing the 

current state of the plant (diagnostics) and interpreting the time history of observations in terms of 

time to required maintenance or remaining useful life (prognostics). The observations are mostly 

carried out by means of instrumentation, and this implies that an indication of an abnormal plant 

state can be due to either a failure of the plant or of the instrumentation. Moreover, unusual 

instrumentation failures may mask plant failures and may thus impede the timely implementation 

of corrective measures. These ambiguities can be resolved by use of “smart” (self-checking) 

sensors or by employment of redundant sensors but these techniques have limitations and 

introduce new vulnerabilities. Assessment questions have been proposed to identify the most 

effective measures 

Advanced diagnostics and prognostics promise savings in maintenance expense and reductions in plant 

down time. These economic incentives can be expected to be important factors leading to the adoption of 

AD&P in the new generation of nuclear power plants and fuel cycle facilities and in the life extension of 

existing ones. Tentative steps in that direction are being taken by the adoption of digital instrumentation 

and remote monitoring networks in the non-safety related aspects of plant operation. The success and 

operating experience of these will lead to guidelines for adoption in safety related applications. 

 
22 

3 Diagnostic and Prognostic Techniques and Methodologies 

This chapter surveys AD&P techniques and methodologies compiled from a review of hundreds 

of published documents and information obtained from interviews with academic and industry 

experts. Methods are grouped into categories that use similar instrumentation or models. This 

survey is a representative (rather than comprehensive) list of state-of-the-art techniques used in 

AD&P. Other techniques and methodologies may exist or be in development or may be outside 

the group headings chosen for this report. 

All technologies listed in this chapter are discussed in more detail in Appendix A. Appendix A 

also provides two alternate groupings that provide insight into the equipment in which these 

techniques have been or could be implemented. 

3.1 Canaries 

The canary subgroup includes techniques that provide warning that conditions are approaching 

limits at which an item will fail. These are mostly used in electronic circuits and are implemented 

by a sacrificial “cell” with lower threshold vulnerabilities than the protected item. The limitation 

of a canary cell as a technique for AD&P is that it offers little lead time for corrective action. Its 

strength is that it is simple and will protect a costly system from failure. Examples include: 

• Fuses: These are traditionally used as warning that current through an electronic circuit is 

approaching an upper limit. As a diagnostic tool a fuse is rather non-specific in that it only 

diagnoses an unhealthy condition but usually will not indicate the cause of the condition. It is not 

a prognostic predictor as it can only indicate that failure has occurred. (D-1 in Appendix A) 

• CMOS canary cells: these cells are designed to be slightly more sensitive to the same stressors 

(voltage, current, temperature, humidity, radiation and vibration) as the operational cells, 

therefore failing earlier as conditions approach the operational limits. (D-2 in Appendix A) 

• Solder joint canary cells: these are often implemented at the perimeter of a multi pin contact 

(edge connectors). The edge experiences failure inducing conditions such as vibration, humidity 

and contamination more intensely and therefore will provide an indication of worsening of 

conditions and approaching failure. As a canary cell, this type of implementation offers a 

prognostic window of time to repair the component. It is usually implemented in such a way that 

a short will not affect the functionality of the rest of the contact. (D-3 in Appendix A) 

• Corrosion canary cells: used in electronics and designed to experience corrosion stressors and 

preempt part failure due to corrosion effects. (D-4 in Appendix A) 

 
23 

3.2 Performance Monitoring Techniques 

This group of techniques is characterized by a focus on the actual performance of the equipment. 

These techniques monitor various performance metrics measured either internally or externally. 

Trends in the performance offer insight into the condition of the item which can be used both for 

diagnostics and prognostics. One advantage of these techniques lies in the fact that one is usually 

not required to add new measurement equipment to a component: the indicator is often an input 

to other equipment, and therefore easy to monitor without design change. Another advantage is 

that monitoring the performance can often provide a large prognostic window, which could allow 

for better maintenance planning and operations. In some instances parameter thresholds are used 

to identify degradation and in some cases signature patterns (e.g. amplitude trend dynamics or 

fluctuation patterns) are used to identify degradation. In particular “marginal checking” of very 

small changes to performance metrics provide information on degradation. Examples of 

performance monitoring techniques include: 

• Built In Current Sensors (BICS): these are applied to CMOS primitives and single die ICs where 

they monitor supply current. Increase in current above a threshold is used to identify fault 

conditions. (D-5 in Appendix A) 

• MOSFET performance signature: multiple performance signatures are trended and compared with 

prior measurements. (D-6 in Appendix A) 

• Marginal checking of cables and connectors: Impedance changes are monitored. (D-7 in 

Appendix A) 

• Marginal checking of voltage controlled oscillators: monitoring of several types of performance 

parameters such as output frequency, power losses and efficiency, phase distortion and noise. (D8 in Appendix A) 

• Marginal checking of Field Effect Transistors: monitoring of leakage currents (drain-source and 

gate) and drain-source resistance. (D-9 in Appendix A) 

• Marginal checking of ceramic chip capacitors: monitoring of leakage current, resistance, 

dissipation factors as well as RF noise. (D-10 in Appendix A) 

• Marginal checking of diodes: monitoring of reverse leakage current, forward voltage drop, 

thermal resistance, power dissipation and RF noise. (D-11 in Appendix A) 

• Marginal checking of electrolytic capacitors through measurement of leakage current and 

resistance, dissipation factor and RF noise. (D-12 in Appendix A) 

• Marginal checking of RF power amplifiers: monitoring of performance through Voltage 

Standing Wave Ratio (VSWR), power dissipation and leakage current. (D-13 in Appendix A) 

• Marginal checking of CMOS ICs: monitoring of supply current and leakage, operational 

signature, current noise and logic level variations. (D-16 in Appendix A) 

• Load cycle monitoring of electronic assemblies: features of the load cycles include cyclic range, 

mean load, rate of load change, dwell time. (D-15 in Appendix A) 

• Monitoring of pulse width modulation of Voltage Source Inverters (VSI). (D-18 in Appendix A) 

• Resistance spectroscopy of Ball Grid Arrays (BGA) and other connectors with multiple solder 

joints: resistance monitored for pre-failure signatures. (D-19 in Appendix A) 

• Monitoring of error code use in digital memories: frequency of triggering of error detecting code 

indicates physical aging of memory. (D-20 in Appendix A) 

• Monitoring of error signal in closed loop control systems: error signals increase in magnitude, 

and frequency spectrum changes with physical damage processes. (D-20 in Appendix A) 
24 

• Monitoring of Pulse Width Modulation (PWM) of power devices in inverter induction motors: 

monitoring of output inverter currents to detect intermittent loss of firing pulses that are trended 

over time. (D-21 in Appendix A) 

• Self-Monitoring Analysis and Reporting Technology (SMART) applied to hard drives: monitors 

multiple performance metrics including: head flight height, error statistics, spin down time, 

temperature and data transfer rates. (D-22 in Appendix A) 

• Circuit as a sensor: performance monitoring of CMOS RF devices through C-V and I-V 

characteristics of transistor which change prior to breakdown.(H-1 in Appendix A) 

• Model based performance monitoring of electro-mechanical, mechanical, electro-hydraulic 

Actuators: Actuator response/performance are monitored and compared with models simulating 

failures. (H-9 in Appendix A) 

• Motor Current Signature Analysis (MCSA)/ Motor Power Signature Analysis (MPSA): the 

current signature as it reacts to load indicates damage. (H-10 in Appendix A) 

• Monitoring of electrical signal of electromechanical generators: both frequency and phase 

changes are indicative of degradation. (H-11 in Appendix A) 

• Measurement of quality of service of communication equipment: degradation of quality of service 

indicative of failure mechanisms. This can make use of standard quality of service protocols in 

the industry. (H-13 in Appendix A) 

• Performance monitoring of pneumatic valves: monitoring of physical performance 

measurements: opening degree, pressure, friction coefficient, and response time. (M-6 in 

Appendix A) 

• Measurement of response to electrical surge: these are applied to stator windings on motors to 

indicate short or open coils. (M-8 in Appendix A) 

3.3 Self-Test 

Self-test is an important diagnostic technique that can sometimes be expanded to modest prognostic 

capabilities. Its use is acceptable for some NPP applications because IEEE Standard 7-4.3.2 is addressed 

in RG 1.152. The regulatory basis and scope of regulation are discussed in Part 2 of this report. 

Strictly speaking, self-test is only applicable to digital processors with built-in correct functionality 

verification tests (NRC, 2007).2

 When the test covers other components, such as auxiliary memory, 

communication channels, sensors and actuators it is more properly termed built-in-test (BIT). In this latter 

mode it is not restricted to digital components. Self-test may comprise check sums of read-only 

memories on which the operational programs are stored; execution of a number of processor steps and 

comparison with stored results; and operational testing of registers that are used for communicating with 

other components. 

BIT (or BIST for built-in self-test) in addition checks for response to stimuli sent to other components, 

including timing checks on the responses, and can incorporate specific steps to find typical malfunctions 

in sensors and actuators. Assertions embedded in software that are only met if the function performs as 

 

2 This is consistent with the definition in BTP 7-17 “Guidance on Self-Test and Surveillance Test 

Provisions” (2007): A self-test is a test or series of tests performed by a device upon itself. 
25 

intended can be used to protect against many failure mechanisms. For printed circuit boards that contain a 

processor, BIT frequently includes boundary scan procedures (Texas Instruments, 1997) in which each 

input/output pin that can accept input or can be accessed for results is associated with a register. Features 

of this methodology were standardized by the Joint Test Action Group (JTAG) and it is sometimes just 

referred to as JTAG test. 

Both self-test and BIT are usually performed on a channel basis. If they fail, the affected channel can be 

declared non-operational and this does not normally result in loss of a function because regulations 

require an adequate amount of redundancy. It is also usually required that physical replacement of failed 

items can be performed without powering down other channels. 

3.3.1 Scope of Testing 

Electronic components, and particularly digital ones, are subject to random failures and built-intest is very effective in finding these. Design failures in digital hardware and software usually 

manifest themselves only under rare conditions and are much less likely to be detected by builtin-test, partly because assumptions that caused the faulty design may have been propagated into 

the design of the built-in-test. Depending on the criticality of the function served by the digital 

system it is desirable or essential that there be defense-in-depth provisions for detecting design 

flaws and if possible for recovering from them. 

BIT can be performed continuously, on a device initiated condition, or on operator request. The term 

“continuously” should be interpreted as periodically at a sufficiently high frequency to detect the onset of 

a hazardous condition in sufficient time to permit managing it. The device initiated conditions usually 

include power-on testing. The tests are usually diagnostic in nature although trending of their 

performance (how long they take to process) can provide modest prognostic capability. 

3.3.2 Implementation of Self-Test 

BIT can be implemented on all hierarchical system levels (Moore & Damper, 1986). When applied 

system-wide BIT may be centralized controlling all tests and functions at all hierarchical levels; or it may 

be composed of several connected control centers. System-wide, centralized, BIT usually requires 

dedicated hardware. 

The benefits of BIT are straightforward and have been recognized for over half a century: 

• Faster fault localization and hence reduced down-time (Montieth, 1982) 

• Fewer removals of operational units 

Both lead to a significant reduction in lifecycle cost (Lappin, 1989). 

• Built In Test (BIT)for hardware and software is common in logical devices as well as control 

systems, power supplies and other large scale systems. BITs are categorized according to their 

operation as: Interruptive, Continuous and Periodic. (D-14 in Appendix A) 
26 

• Self-Monitoring Analysis and Reporting Technology (SMART) is comprised of a set of self-tests 

applied mainly at start-up to hard disk drives. Tests focus on performance metrics and this 

technology is also categorized under the performance monitoring techniques of Section 3.2 

above. (D-22 in Appendix A) 

 

3.4 Multivariate Analysis 

The group under this title consists of techniques that require analysis of many variables. This category 

focuses on methods that either require large scale pattern classification because a diagnosis requires 

analysis of many data features, or methods that rely on different features as evidence for a single 

diagnostic or prognostic. Examples include: 

• Large scale pattern recognition based on multivariate features such as: Support Vector Machines 

(SVM), Support Vector Classification (SVC), Support Vector Regression (SVR). These are 

mainly applied to electronic systems but mechanical applications exist as well. (D-24 in 

Appendix A) 

• Distance Metrics are used to reduce multivariate data to univariate data (D-25 in Appendix A) 

• Projection Pursuit Analysis is another means of multivariate analysis of large systems generating 

very diverse measurements for AD&P. (D-26 in Appendix A) 

• Continuous System Telemetry Harness (CSTH) is a multivariate diagnostic method applied to 

computer systems (both hardware and software) that combines many types of measurements 

(performance, errors, canary cells). (D-27 in Appendix A) 

• Bayesian model-based diagnostics can be applied to electrical power systems using data collected 

from multiple types of sensors. (M-10 in Appendix A) 

3.5 Life Consumption Monitoring 

Life consumption monitoring techniques build on the availability of data that is directly related to the 

conditions leading to life consumption, rather than the condition of the actual equipment. These can be 

either indirect measurements of material consumption (e.g. particles) or they can be direct measurements 

of various known stressors (such as temperature or vibrations). The data is then applied to life 

consumption models that may estimate degree of degradation as well as rate of degradation. The 

technique is used wherever such measurements are possible both in electronic and mechanical systems. 

Examples include:

• Solder joints: Temperature and vibrations are known stressors and these are monitored for RUL 

estimate. (H-2 in Appendix A) 

• Electronic/digital modules: temperature and vibration are monitored to predict RUL. (H-8 in 

Appendix A) 

• Flow accelerated corrosion can be detected via electrochemical and vibration sensors that identify 

flow accelerated corrosion at pipe elbows. (M-1 in Appendix A) 

• Smart oil analysis provides information on type and size of contaminants indicating corrosion and 

life consumption. (M-4 in Appendix A) 

• Dissolved Gas Analysis is applied to insulating mineral oil in transformers to identify faults and 

degradation. (H-6 in Appendix A) 
27 

• Pneumatic valves: the application of a damage based model to pneumatic valves draws on life 

consumption data such as friction and degree of opening. (M-6 in Appendix A) 

 

3.6 Vibration Analysis

Vibration analysis is a mature and widely used diagnostic technique. It is mainly applied to mechanical 

(both static and dynamic) systems as well as to electromechanical components. Examples of the 

application of vibration analysis include: 

• Rotating elements: vibration sensors can measure displacement, velocity and/or acceleration; 

each offering information on different frequencies and amplitudes of vibration. (H-4 in Appendix 

A) 

• Transformers: measurements of vibration of magnetic cores and windings are used to diagnose 

structural degradation. (H-5 in Appendix A) 

3.7 Acoustic Techniques 

Sound waves have been used for decades to diagnose the condition of both passive and active 

components. Although more suitable for mechanical systems, sound is also used in electromechanical 

systems. The application to diagnostics and prognostics can proceed in one of two ways: passively 

through monitoring of emitted waves, or actively by monitoring the reflection or transmission of pulses 

emitted by test equipment: 

• Passively, acoustic monitoring is mainly applied to active mechanical and electromechanical 

systems such as actuators and valves, but is also applied to static equipment such as piping. 

Examples of major systems in nuclear power plants include reactor vessels and piping, feedwater 

systems, transformers, steam lines and coolant loops. Both the sonic and ultrasonic ranges are 

used to detect changes in condition and degradation. (M-2 in Appendix A) 

• Actively, ultrasound test is performed with very short ultrasonic pulse-waves with frequencies 

ranging from 0.1-15 MHz and occasionally up to 50 MHz. These are directed into materials to 

detect and characterize internal flaws. It is mainly applied to passive equipment/structures. (M-3 

in Appendix A) 

 

3.8 Targeted Micro/Macroscopic Measurements

The techniques included under this heading are based on a variety of very specialized measurements and 

are designed for a very specific type of equipment. Their common property is that they do not fit well 

into any of the categories identified above. Of interest are the following methods: 

• Molecular test equipment applied to CMOS ICs is based on designing microscopic sensors into 

the IC. These are based on carbon nanotube technology and are able to sense voltage, currents 

and certain chemical changes which indicate progression to failure. (D-17 in Appendix A) 

• Sensor diagnostics: through multi-level flow modeling (MLM) sensor failures can be detected. 

(D-23 in Appendix A) 
28 

• Line Resistance Analysis (LIRA) is applied to cables to detect small changes of wire electric 

parameters, specifically the insulation permittivity, which is a significant indicator for the 

condition of the cable state. Tiny insulation cracks can be detected. (H-3 in Appendix A) 

• Circuit Voltage Analysis is applied to small circuits. Measuring points are designed into the die 

based on failure mode analysis. (H-7 in Appendix A) 

• Tribology covers multiple measurement types and sensor technologies to identify and quantify 

contaminants in the fluids contained in moving/rotating equipment. These can be indicative of 

damage, or conditions leading to damage. (H-12 in Appendix A) 

• Coil Current Monitoring Systems (CCMS) are designed to monitor control rod driving system 

(CRDS) and control element driving mechanism controls (CEDMC). They sample currents at 

very high frequency and can detect wave form mismatch patterns that are indicative of a variety 

of faults in these systems. (M-5 in Appendix A) 

• Neutron Noise Monitoring is mainly used for performance monitoring but can diagnose a variety 

of structural faults in core internals, control rods, fuel channels and detector tubes. (M-7 in 

Appendix A) 

• Channel mismatch detection of resolver position sensors: voltage measurements can indicate 

faults in the windings for this type of sensor. (M-9 in Appendix A) 

 
29 

4 Example Applications of AD&P Methods and Techniques 

4.1 Introduction 

This chapter discusses five examples of AD&P applications. The examples detailed here are categorized 

as either 

• Model-driven 

• Data-based or 

• Hybrid 

Essential differences between the categories are shown in Table 4-1 and are demonstrated in the 

following subsections. This categorization refers to the methods used to establish a failure diagnosis 

based on measurement/tests Model-based algorithms rely on a quantitatively defined relationship between 

failure modes (or equipment states in general) and observables. Data-driven methods are on the other end 

of the spectrum where observables cannot be clearly linked to equipment states. Rather the algorithms use 

empirical data to find patterns that provide evidence to support a diagnostic or prognostic result. The 

availability of historical data models is therefore essential. Despite the nomenclature, model-based 

methods also rely on significant amounts of data to validate the model and to estimate its parameters, and 

most data-driven techniques utilize models to account for external effects (temperature, vibration, output) 

on equipment life. The hybrid category includes examples that are neither purely model-based nor only 

data-driven. Rather they use some form of model, sometimes a conceptual model, and may rely on 

artificial intelligence to analyze measured data to generate a diagnosis or prognosis. 

Four categories can be used to distinguish between levels in the equipment hierarchy: primitive (e.g. 

beam, pipe, solder joint), assembly (e.g. valve, diode), component (e.g. gear box, circuit board), and 

system (e.g. feed water, ventilation, digital storage). Equipment at the higher levels of the hierarchy 

usually has more failure modes than those at the lower levels. It is therefore not surprising that modelbased techniques are primarily found at the primitive and assembly levels whereas hybrid and data-driven 

techniques are dominant at the component and system level. 

The body of this chapter provides an example of each of the above categorized AD&P techniques. 

Because of the lack of publications on NPP applications most of the examples are taken from non-nuclear 

environments. A fourth example is presented in which observations currently taken in an NPP for 

operational purposes can be extended to serve as a basis for AD&P. 
30 

TABLE 4-1 CHARACTERISTICS OF THE THREE CATEGORIES

Model-based Hybrid Data-driven 

a. Diagnostics 

Measurements are linked 

directly to physical damage 

and used to support a 

diagnosis. Physical damage 

model for each failure mode 

can be fully expressed 

mathematically. Model 

parameters are obtained either 

from observation or physical 

principles. A model is not 

required to be on a 

microscopic level or cover 

entire time progression to 

failure. It is necessary that it 

directly links measurable 

variables with the state of the 

equipment. 

Techniques that are neither based 

purely on models nor only on 

data. Hybrid damage model may 

be based on physical model with 

empirical parameters or a 

conceptual model that may cover 

multiple failure modes. The 

conceptual model presents a 

process that is understood but not 

necessarily mathematically 

formulated. (Tamilselvan & 

Wang, 2012) 

Model generation is impractical 

either because of multiple failure 

modes, lack of knowledge, or 

inability to measure observables 

linked to a model. Hence, the 

equipment condition is observed 

externally, through indirect 

features (e. g., noise). Changes in 

equipment performance are used 

to support a diagnosis. 

Algorithms used to go from 

observables to a diagnosis are not 

model-based but may use 

artificial intelligence to link data 

patterns with equipment states. 

b. Prognostics 

Models used for diagnostics 

are propagated to End of Life 

(EOL) to estimate remaining 

useful life (RUL). Life 

consumption is estimated 

from past stresses and is 

propagated to expected 

stresses. 

Models used for diagnostics are 

empirically propagated to EOL to 

estimate RUL. (Katipamula & 

Brambley, 2005) 

RUL empirically estimated from 

comparison of past and current 

performance. The progression to 

failure in these approaches is 

estimated from statistics and 

pattern classification rather than a 

damage model (Coble et al., 

2012). 

Appendix A presents two taxonomies that are based on the model vs. data categorization. All AD&P 

techniques, including those not covered in this document (either in the examples below or in the sampling 

of Chapter 3), can be placed in a category within these taxonomies. This association allows a reader to 

gather an indication of the benefits and potential pitfalls of an associated technique. The taxonomies are 

backed-up with a set of descriptions of several dozen methods that are either currently in use or in varying 

stages of R&D. 
31 

4.2 AD&P Applied with a Model-Based Approach 

4.2.1 General Attributes 

 

The model-based terminology refers to the direct relationship between the measurements and the 

diagnostic/prognostic result: Diagnostic and Prognostic information is derived from measurements that 

are directly linked to physical models that are understood, are mathematically tractable, and their 

quantitative parameters can be assigned with a statistical confidence interval (either from theory or 

experiment). In that respect the system is fully transparent and implements the failure process described in 

Chapter 2. 

The requirement for a fully transparent system can be eased. The concern is usually with the progression 

from some initial stage of failure to inoperable conditions. For example, there may not be a good model 

for the dynamics of crack initiation, but a model for crack propagation may be sufficient to provide timely 

prognostics. The model need not be derived directly from first principles, though it is based on a physical 

understanding of both the progression of failure and the physical parameters that are affected by this 

progression. 

The technical challenges involved in applying a model-based approach include: 

1. Obtaining a physical model of the failure process suitable for diagnostics and prognostics (failure 

here can be induced or the result of use-related wear and tear). 

2. The ability to identify the quantitative changes in a set of physical properties that are affected by 

the progression to failure of the item. The item can be a primitive item, an assembly or 

component. The features in the examples presented below are operational features in the case of a 

valve, and neutron noise levels in the case of neutron dynamics. 

3. The ability to translate the physical model to a tractable mathematical model. 

4. The ability to identify, with sufficient confidence, quantitative parameters for the mathematical 

model based either on first principles or measurements. 

5. A reliable and logistically effective definition of end of useful life for the item. 

The above challenges determine what types of equipment can be diagnosed and prognosticated using a 

model-based approach. For instance, most realistic systems suffer from more than just several failure 

modes which can complicate physical models and their mathematical presentation. The model-based 

methodology is appropriate when one is able to clearly distinguish between wear-out and other failure 

mechanisms and their measurable effects. 

As a result, the mathematical tools that are often common to model-based applications are statistical 

techniques that offer a means of determining the most likely of a known set of states (healthy or failed, 

and by which failure mode). These tools include 

• Bayesian models applied to observations for identifying wear-out processes and their stage of 

progression 

• Maximum Likelihood Estimation (MLE) of model parameters 
32 

• Simulation techniques (e.g. particle filters; Monte Carlo) that can be used to solve a model (such 

as Bayesian network), or fit parameters 

• Statistical regression to estimate parameters and solve simple Bayesian models 

• Neural networks for solving complex Bayesian models 

• Frequency analyses such as Fast Fourier Transform applied to measured data for feature 

extraction and noise tracking 

Measurement challenges are mostly in the area of collecting and transmitting high frequency and low 

amplitude signatures. Digital implementations are often a good solution to overcome these challenges. 

4.2.2 Applicability to Levels in the System Hierarchy 

The model-based approach addresses one failure mode at a time and is therefore most effective when 

applied to the lower levels in the system hierarchy. As shown in the Taxonomy in Appendix A, no 

example of a model-based approach could be found at the system level. Because model-based 

applications are often very closely linked to the physical model there is much variation between one 

application and another. This is illustrated by comparing the example of section 4.2.3 with the example of 

section 4.5. 

4.2.3 Model-based AD&P applied to a Pneumatic Valve 

An example of a model-based application for a component is a pneumatically operated valve for 

controlling fluid flow for the Space Shuttle. This application has been selected because of the detail with 

which each step of the methodology has been explained. A schematic diagram of the valve is shown in 

Figure 4.1 (Daigle & Goeble, 2011). The valve is open as long as the force of the air at the bottom 

pneumatic port is greater than the combined force due to the spring and the air at the top pneumatic port. 

A detailed mathematical model is developed for the normal case as well as for each of the following five 

wear-out mechanisms: 

• Friction between the moving element and the housing 

• Reduction in the spring force 

• Internal leakage (around the piston) 

• External leakage from the top chamber 

• External leakage from the bottom chamber. 
33 

FIGURE 4-1 PNEUMATIC VALVE

The separate damage models are developed because the degree of wear-out (damage estimation) is more 

easily determined when a comparison between the normal state and a damaged state can be made, rather 

than relying exclusively on deviation from a normal state. The end of life (failed) state is defined in all but 

one of the wear-out mechanisms as open or close time in excess of 15 seconds, and this criterion is 

acknowledged as being empirical. The way in which the end of life state is approached depends on the 

damage mechanisms and leads to the differential diagnosis. 

The exception to the 15 second criterion is the reduction in spring force. The spring is only required for 

closing the valve when there is a total failure of the air supply. Therefore a separate decision point is 

defined as the spring constant that will reliably close the valve without assist from the air supply. Once 

that value of spring constant had been validated, the end of life criterion was set as the spring constant 

dropping below it. 

 

The overall approach for a given failure mechanism is shown in Figure 4-2. The output of damage 

estimation is an estimation of the current state (xk) and the parameter vector (θk), given the output history 

(yo:k). Examples of items in the parameter vector are piston and valve friction coefficients, rate of piston 

diameter reduction, and mass of gas above the piston. The prediction makes the strong assumption that 

the processes responsible for damage in the past will continue into the future and that the processes do not 

affect each other. Both damage estimation and prediction are subject to noise and several methods for 

estimating and filtering noise in the prediction results are given. Particle filters, a simulation technique for 

deducing input based on observed output are considered a particularly effective approach because they do 

not depend on assumption of Gaussian noise. The output of the Prediction box is the probability that the 

process indicates end of life (which should be low) and an estimate of the remaining useful life. 
34 

FIGURE 4-2 PROGNOSTIC ARCHITECTURE (DAIGLE & GOEBLE, 2011) 

The mathematical development is rigorous and lengthy, but is based directly on the damage mechanisms 

and therefore it does not require artificial intelligence constructs that are used by some authors to infer 

causes from effects. Several limitations are recognized: the empirical definition of the end of life and the 

assumption that past damage processes will continue into the future. The value of this approach depends 

on complete knowledge of all damage mechanisms and the ability to verify that the estimated parameters 

of the damage process correspond to those observed in operation. Both of these conditions are satisfied 

when there is extensive operating experience with the item under investigation. 

4.2.4 Typical Assessment Questions that Arise in the Model-Based Methodology 

 When a model-based analysis is used in a critical nuclear facility application, suitable assessment 

questions include but are not limited to: 

1. What is the source and validation of the model(s)? 

2. Were the model boundaries validated to ensure that operational conditions and usage are 

within these boundaries? 

3. How were model parameters selected and validated? What is the associated confidence 

level? 

4. Were studies with alternate models and parameters conducted? How do these compare? 

5. Do the features selected for measurements cover all failure modes including potential 

overlap in effects? 

6. At the EOL decision point, will the item still permit the next higher level to operate within 

its specification under all design basis events? And what is the time scale from that decision 

point to the time that the higher level does not operate within its specification? 

7. Was simultaneous damage in several failure modes considered in the analysis? If not, is it 

justified? 

8. If the system under observation is redundant, are the redundancy provisions (detection and 

switching) included in the analysis? If so, what data are available to assess the success 

probability for switching to an alternate? Also, how is the alternate known to be 

operational? 

9. For redundant systems, are the components that need to be changed capable of being 

removed and installed under power? If not, what provisions are there to remove power? 

10. Have prognostic windows been validated to be useful for time intervals of importance to 

NPPs and NFCFs? 

11. Are there sufficient trained maintenance personnel on staff at all times? 
35 

12. What level of spares will be maintained? 

4.3 AD&P Applied Using a Data-Driven Approach 

4.3.1 General Attributes 

The data-driven approach owes its name to its exclusive reliance on input and output measurements; 

particular, it does not need a model for each failure mode or even a complete enumeration of all failure 

modes. It does not require complete knowledge of failure processes, but relies heavily on data that is in 

some way (not necessarily well understood) linked to degraded item state or conditions, and material 

damage. The methodology is based on the fact that there are some externally observable effects that can 

be used to support a diagnosis and/or prognosis. The science or art of identifying these measurable 

features and optimizing their measurement through sensor placement and proper signal processing is 

based on a combination of science, experience, and trial and error. To make up for the lack of a clear link 

between the features and the actual conditions or state of the item, much historical data is required. The 

data is not used to fit a model or solve a Bayesian assumption. Rather the data is used as a basis for 

comparison with current measurements. Current features, feature patterns and trends are compared with 

stored data and trend patterns that were previously associated with an actual diagnosis or RUL. Based on 

this comparison a current diagnosis and/or prognosis are generated. The “comparison” is not a simple one 

as the multiplicity of failure modes usually implies many patterns and trends, and this task generates not 

an absolute result but rather an indication of the most likely current state and conditions, with a specified 

confidence level. The analysis usually utilizes pattern recognition, and artificial intelligence. 

 

Because there is dependence, in the data-driven approach, on externally observed progression of damage 

effects to an empirically selected end of life criterion, the prognostic information is typically based on 

performance at a higher level in the equipment hierarchy. 

The technical challenges involved in applying a data-driven approach include: 

1. Identifying measurable features (effects) that are reliably linked to the damage process or 

conditions. This challenge includes identifying a sufficient number of such features so as to 

be sure that failure modes that may present similar effects but have a different progression to 

EOL are separable. 

2. Collecting/accessing a sufficiently large database of feature patterns for statistical 

comparison. This is usually the most significant challenge given that there is no physical 

model to rely on. Moreover, in practice one usually maintains equipment in such a way that it 

is treated or replaced before EOL, and therefore there is little operational data showing that 

limit. 

3. The ability to develop a pattern/trend recognition algorithm that will support a diagnosis 

and/or prognosis at a sufficiently early stage. This challenge can be found also in modelbased applications but more so in the data-driven methodology because of the lack of 

transparency as to the exact relationships between the tracked features and the underlying 

physical process. 

4. The ability to measure and analyze with sufficient accuracy the physical features that are the 

basis for the analysis. 

5. A reliable and logistically effective definition of end of useful life for the item. 
36 

The above challenges determine what types of systems can be analyzed for diagnostics and prognostics 

with a purely data-driven approach. The main challenge for realistic systems is the access to sufficient 

historical data. This is especially true for mission critical systems that are not “allowed” to reach actual 

end of useful life but are replaced/maintained much earlier. 

The mathematical tools that are common to data-driven applications are derived from artificial 

intelligence and machine learning, specifically those utilized for pattern classification: 

• State Vector Machines and their variants. 

• Neural Networks. 

• Frequency analyses such as Fast Fourier Transform applied to measured data for feature 

extraction and noise separation. 

Every application may have unique measurement challenges. However, measurement challenges that are 

common to data-driven applications are mostly in the area of collecting and transmitting high frequency 

and low amplitude signatures. Digital implementations are often a good solution for overcoming these 

challenges. 

 

4.3.2 Applicability to Levels in the System Hierarchy 

The data-driven approach has fewer a priori limitations to its use, although its implementation is at least 

as challenging as that of model-based applications. Data-driven applications can be found at high levels of 

the system hierarchy and such an example is provided below in section 4.3.3, which discusses the 

application of SMART to Hard Drives. However, because this methodology can deal with items with 

many failure modes it is also useful when approaching lower complexity items with complex degradation 

processes. 

 

Data-driven analysis is most widely used for electronics because of the many failure modes that are 

encountered there. The data-driven approach is suitable for all levels in the equipment hierarchy. 

 

4.3.3 Data-Driven Example for Computer Hard Disk Drives 

The example of data-driven AD&P application is the SMART monitoring system for computer hard disk 

drives, the purpose of which is to detect indicators that warn of impending failure. 

SMART is a quintessential example of the data-driven approach and it collects data for both diagnostics 

and prognostics. The measurements are symptomatic par excellence and their interpretation is 

phenomenological and does not rely on physical models of material or degradation. SMART does 

provide predictive information but it does not generate a RUL prediction: the monitoring system will 

provide an alarm that either indicates the hard drive condition is less than optimal or will send an alarm 

that indicates that a failure is imminent (depending on the indicator). 
37 

The predecessor to SMART was originated by IBM in 1992. PFA, the Predictive Failure Analysis, was 

developed and applied to 3.5 inch hard disk drives (IBM, 1992). The monitor periodically measured 

certain attributes (such as head-to-disk flying height) and sent a warning when a threshold was exceeded. 

Subsequently, computer manufacturer Compaq and disk drive manufacturers Seagate, Quantum, 

and Conner created IntelliSafe (Seagate, 2008). The disk’s "health parameters" were measured by the 

disk drive and the values would be transferred to the operating system and user-space monitoring 

software. Each disk drive vendor could decide which parameters were to be included for monitoring, and 

what their thresholds should be. The unification was at the protocol level with the host. 

The Compaq implementation was submitted to a Small Form Factor committee3 for standardization in 

early 1995 (Compaq, 1995). Supported by IBM, Compaq's development partners Seagate, Quantum, and 

Conner, and by Western Digital, which did not have a failure prediction system at the time, the 

Committee chose IntelliSafe's approach, as the standard. The resulting jointly developed standard was 

named S.M.A.R.T., now usually spelled SMART. 

The SMART standard described a communication protocol for an ATA (Advanced Technology 

Attachment) host to use for control monitoring and analysis in a hard disk drive. The standard did not 

specify particular metrics, measurements, or values. Currently SMART does imply a variety of specific 

indicators although these are not formally specified in the standard. The term is also understood to refer to 

protocols that are not related to ATA but are used to communicate self-monitoring metrics. 

Currently all disk drive manufacturers apply SMART technology to their products. The measurements 

used by different manufacturers vary but they typically include variations of: 

• Mechanical indicators, for example: fly height of the head above magnetic media; vibrations. 

• Damage indicators, for example: ECC (Error Control Coding) circuitry on the hard drive card and 

soft error rates. 

• Thermal indicators, for example: ambient temperatures, rate of cooling airflow. 

A list of over 200 attributes can be found in ATA/ATAPI Command Set (ATA8-ACS), working draft 

revision 6a (current to 2008) (SMART, 2008). A sample of the first five of these is provided in Table 4-2. 

The table includes the attribute description, whether it is a critical attribute (which would indicate that 

backup should be called upon immediately), and in some cases the manufacturer that has implemented the 

attribute. The attribute ID follows the AT Attachment (ATA) Standard. Manufacturers are not required to 

adhere to this list and it should be viewed as a guide only. Of these many attributes one can find many 

that are very similar and may only differ in the manner in which they are quantified. The list is inclusive 

in that respect. 

 

3 The Small Form Factor committee is an ad hoc electronics industry group formed to quickly 

develop interoperability specifications 

http://en.wikipedia.org/wiki/Small_Form_Factor_committee 
38 

TABLE 4-2 SMART ATTRIBUTES (ATA) 

ID Critical Attribute 

name 

Value for 

healthy 

component Description 

01 Yes Read Error 

Rate Low 

Vendor specific raw value. Stores data 

related to the rate of hardware read errors that 

occurred when reading data from a disk 

surface. The raw value has different structure 

for different vendors and is often not 

meaningful as a decimal number. 

02 Throughput 

Performance High 

Overall (general) throughput performance of 

a hard disk drive. If the value of this attribute 

is decreasing there is a high probability that 

there is a problem with the disk. 

03 Spin-Up Time Low 

Average time of spindle spin up (from zero 

RPM to fully operational [millisecs]). 

04 Start/Stop 

Count 

A tally of spindle start/stop cycles. The 

spindle turns on, and hence the count is 

increased, both after hard disk is turned 

entirely off (disconnected from power 

source) and when the hard disk returns from 

sleep mode. 

05 Yes Reallocated 

Sectors Count Low 

Count of reallocated sectors. When the hard 

drive finds a read/write/verification error, it 

marks that sector as "reallocated" and 

transfers data to a special reserved area (spare 

area). The raw value normally represents a 

count of the bad sectors that have been found 

and remapped. Thus, the higher the attribute 

value, the more sectors the drive has had to 

reallocate. This allows a drive with bad 

sectors to continue operation; however, a 

drive which has had any reallocations at all is 

significantly more likely to fail in the near 

future. While primarily used as a metric of 

the life expectancy of the drive, this number 

also affects performance. As the count of 

reallocated sectors increases, the read/write 

speed tends to become worse because 

the drive head is forced to seek to the 

reserved area whenever a remap is accessed. 
39 

The measurements and analysis performed by SMART applications provide primarily diagnostic 

information together with some prediction of the remaining margins. Thresholds are proprietary to the 

manufacturers. Quantitative prognostics can be constructed from this information by trending, although 

currently this is rarely implemented. 

SMART is also an example of self-test as monitoring takes place both by monitoring active hard drive 

activities (data retrieved by the hard drive) as well as automatic off-line scan of additional operations (all 

data and all sectors) during periods of drive inactivity. 

The special characteristics of the data-driven approach are clearly visible in this example. The first entry 

in the table, read error rate, is a failure effect that can be caused by any number of failure modes, such as 

damage to the disk, damage to the read head, degradation of the decoding circuitry and fluctuations in the 

spin rate. In spite of the absence of detailed failure models the information obtained furnishes a good 

assessment of the health of some attributes of the disk. Together with external trending it may even 

indicate remaining useful life. 

4.3.4 Typical Assessment Questions that Arise in the Data-Driven Methodology 

When an AD&P implementation based on a data-driven approach is used in a critical NPP 

application, pertinent assessment questions include but are not limited to: 

1. What is the source for data and how is the relation of the data to damage mechanisms 

validated? 

2. Can site-specific challenges affect continuous data availability? 

3. How were the algorithms and artificial intelligence tools used for pattern recognition 

chosen? Have they been used in other applications, and if so with what success? 

4. Were studies with alternate algorithms and parameters conducted? How did these 

compare? 

5. Do the features selected for measurements cover all significant failure modes including 

potential overlap in effects? 

6. At the EOL decision point, will the item still permit the next higher level to operate 

within its specification under all design basis events? And what is the time scale from 

that decision point to the time that the higher level does not operate within its 

specification? 

7. Was simultaneous damage in several failure modes considered in the analysis? If not, is the 

omission justified? 

8. If the system under observation is redundant, are the redundancy provisions (detection 

and switching) included in the analysis? If so, what data are available to assess the 

success probability for switching to an alternate? Also, how is the alternate known to be 

operational? 

9. For redundant systems, are the components that need to be changed capable of being 

removed and installed under power? If not, what provisions are there to remove power? 

10. Have prognostic windows been validated to be useful for time intervals of importance to 

NPPs and NFCFs? 

11. Are there sufficient trained maintainers on staff at all times? 

12. What level of spares will be maintained? 
40 

4.4 Example of AD&P Using a Hybrid Approach 

4.4.1 General Attributes 

Real world applications are usually located somewhere between the extremes of purely model-based and 

purely data-driven categorization. The hybrid approach covers all applications that do not fit in the 

extremes. In this spectrum one finds applications that benefit from both data-driven algorithms and 

physical models. 

In the typical situation one does not have complete physical models and measurable variables to perform 

a purely model-based analysis but there is also not enough historical data or good enough algorithms to 

use a purely data-driven analysis. In some cases hybrid analysis can include a physical model of the 

degradation process but there will be missing elements connecting the model to the observable load and 

specific degradation levels. Overcoming these challenges will require use of data-driven artificial 

intelligence methods. In other cases one uses the physical models to provide input to data-driven 

algorithms so as to enable reaching a reliable solution (for example the physical model can restrict the 

pattern space which is explored by the algorithm so that non-physical results are eliminated a priori, e.g. 

rolling or sliding friction). As in the model-based case, the level of degradation that constitutes end of life 

needs to be defined by criteria that are specific to each application. 

The technical challenges to realizing a hybrid diagnostic or prognostic application include a list combined 

from the previous two categories (see sections 4.2.1 and 4.3.1 ). However not all challenges listed in 

purely data-driven or purely model-based methodologies are found in the hybrid methodology. Use of the 

hybrid methodology is often based on a requirement to eliminate some of the challenges found in the two 

extremes. 

The RUL is usually comp uted by using the degradation model, estimates of the current degradation and 

of model parameters, and the defined end of life criterion. 

4.4.2 Applicability to Levels in the System Hierarchy 

The hybrid approach can be used at all levels of the system hierarchy, from primitive to system. The 

example in section 4.4.3 describes a hybrid application at an intermediate level. 

4.4.3 Hybrid Approach for Rotating Element 

The vibration analysis of rotating elements4

 will be used as an example. The method detailed here is the 

application of vibration analysis to a rolling element bearing (Harris, 2000) an example of which is shown 

in Figure 4-3 

 

4 H-4 in Appendix A 
41 

Vibration may be measured using displacement sensors, velocity sensors or accelerometers, with the last 

being the most common. Broadband measurement, which can range from 10-10,000Hz, will identify 

defects through high values of the peak/RMS ratio (Crest Factor); severe defects are indicated by highlevel of RMS vibration (Lacey, 2008). The actual range that is monitored depends on the specific 

geometry of the bearing, as the frequency depends on the location and type of defect. In general the broad 

band measurement detects defects only in their advanced stage. More sensitive detection is possible by 

concentrating on narrower frequency bands as will be described below. Researchers in industry and 

academia have utilized many algorithms and features for pattern recognition (machine learning) both for 

diagnostics and prognostics (Porotsky & Bluvband, 2012). 

FIGURE 4-3 PARTS OF ROLLING ELEMENT BEARING5

At present the following sensor types are being utilized: 

• Airborne Ultrasound: dB readings are used to detect high frequency acoustic emission. 

• Shock Pulse Method (SPM) developed in the 60’s relies on a sensor that resonates at a specific 

high frequency. This method can be used to detect the high frequency noise developing in the 

very early stages of damage when brief, random, metal-on-metal contact create shocks through 

the bearing metal. Analysis can provide information on the type and severity of damage. This 

sensor is used to specifically detect the onset of vibration due to an incipient defect. 

• Envelope analysis and demodulation, the spectral analysis that removes the low frequency noise 

and analyzes the remaining high frequency spikes, typically uses high frequency tri-axial 

accelerometers. 

• PeakVue6

 which is based on rapid sampling of the sensor signal and captures the short duration 

stress waves also uses high frequency tri-axial accelerometers7

. 

 

5 Taken from (http://encyclopedia2.thefreedictionary.com/antifriction+bearing)

6 Product Datasheet 

http://www.icareweb.com/ressources/pdf/products/portables/2130/CSI_2130_emerson_EN.pdf 
42 

 

In general the sensors of choice for most applications of vibration data collection are piezoelectric or 

piezo-ceramic accelerometers. The output of a tri-axial accelerometer cluster (Azima, Undated) is usually 

integrated directly into velocity units. The accelerometers used to measure vibration are preferably 

mounted directly on the bearing, as close as possible to the bearing load zone (Mobius, 2012). High 

frequency accelerometers are available for harsh environments, such as nuclear power plants, involving 

high temperatures and radiation8

. 

The principal damage mechanisms can be allocated to three main parts of the bearing: 

• Inner Raceway: Eccentricity, waviness, surface roughness, discrete defects 

• Outer Raceway: Waviness, surface roughness, discrete defects 

• Rolling Element: Diameter variations, waviness, surface roughness, discrete defects 

The damage is caused by contamination, temperature and load extremes, as well as imperfect 

maintenance and installation. These conditions will each have their own signature vibration profile. 

The main mechanism for advanced diagnostics and prognostics of bearing damage is spectral analysis of 

the vibrations. This permits: 

• Recognition of specific frequency signatures of defects and damage 

• Isolation of such signatures, in the frequency domain, from ambient machine noise that is due to 

other machine components (e.g. electric motors, gears, belts, hydraulics, structural resonances 

etc.) 

Poor lubrication as well as surface roughness (due to either poor manufacturing processes or later 

damage) will eventually lead to metal on metal contact which sends shock waves through the bearing. 

This leads to broadband noise at very high frequencies. As the roughness increases or lubrication 

decreases the noise becomes more significant and moves to somewhat lower frequencies. Eventually this 

type of damage will result in localized defects that generate noise spikes with well-defined frequencies 

depending on the location of the defect. There are guides for allowable vibration values9

. These are 

primarily useful for basic alarms for a condition based maintenance (CBM) program. 

More specific narrowband analysis is applied to spectral information obtained usually by Fast Fourier 

Transform (FFT) which translates the time domain vibration amplitudes to an amplitude vs. frequency 

plot. 

 7Product Datasheet 

http://www2.emersonprocess.com/siteadmincenter/PM%20Asset%20Optimization%20Documen

ts/ProductDataSheets/2130_ds_txtriaxialacce.pdf

8 For a commercial example see 

http://www.bksv.com/Products/transducers/vibration/accelerometers/accelerometers/8347C.as

px

9 For example see charts at http://www.usedvibration.com/vibration_analysis_severity_charts.htm
43 

There are four forcing frequencies in bearing vibration: ball pass outer race (BPFO), ball pass inner race 

(BPFI), ball spin (BSF) and cage or fundamental train frequency (FTF). These are determined by the 

number of balls/rollers and the shaft rotation speed10. In addition, each of the parts will have its own 

resonant frequencies based on shape and material. The appearance of these frequencies (and their 

combinations) in the frequency spectrum of the vibrations will provide diagnostic information on the 

type, location and severity of a defect. The severity of a defect can be assessed from the number of higher 

harmonics that appear as well as the “noise floor”. As long as the noise floor does not exceed the normal 

and there are not too many harmonics the defect is not severe. As degradation progresses there will be 

more harmonics visible and when the defect becomes severe the noise floor will also exceed the normal. 

The following are examples of spectral analysis techniques primarily aimed at diagnosis: 

Envelope spectral analysis is one of the features that can be used to isolate the effects of deteriorating 

parts from the surrounding noise. The “envelope”, an example of which is shown in Figure 4-4, is a 

periodic amplitude modulation (AM) due to a budding defect that is periodically causing vibrations as 

surfaces come into contact. The envelope frequency and sidebands can provide us with information on the 

location of the incipient defect (e.g. outer raceway, inner raceway or rolling element). The advantage of 

inspecting the envelope is that it can be measured even when the actual vibration due to the still very 

small defect is much smaller than the overall vibration noise in the machine. In order to get the envelope 

information one must first filter out the low frequency vibrations. This method is termed High Frequency 

Resonance Technique (HFRT) or “Envelope Spectrum”. 

To select the part of the spectrum to be filtered the Spectral Kurtosis (Sawalhi & Randall, 2004) technique 

can be employed. Spectral Kurtosis (SK) is a statistical parameter based on the fourth moment of the 

signal. SK will be close to zero for Gaussian noise, but large for impulsive signals. As described above, a 

developing fault will generate periodic impulses which are reflected in the vibration noise. SK is therefore 

well suited for not only finding the demodulating envelope frequency but also for identifying and filtering 

the parts of the signal that are most “impulsive” and reducing the background noise. 

 

10 For example a bearing with 22 balls/rollers with shaft rotating at 1800 RPM will have the 

following four forcing frequencies: BPFO=294 Hz; BPFI=366Hz; BSF=129Hz and FTF=13.35Hz 
44 

FIGURE 4-4 AMPLITUDE MODULATED SIGNAL 

(http://encyclopedia2.thedictionary.com/antifriction+bearing) 

Cepstrum analysis (Sawalhi, 2012) is particularly well suited for use in evaluating the patterns that 

emerge in the vibration spectrum of a damaged bearing as it highlights periodic spectrum structures such 

as the envelope frequency and accompanying sidebands. 

In general the severity of a defect is judged by a combination of three metrics: 

1. The number of higher harmonic peaks visible in the spectrum. 

2. The amount by which each of these peaks exceeds a standard criterion. 

3. The absolute amplitude of each of these peaks. 

For prognostics applied to bearings, studies of the relationship of load ratings and the useful life can be 

used. These provide a “ Life Formula for Rolling Bearings” (Lundberg & Paimgren, 1947) as well as a 

basis for standardizing bearing life as it depends on load (e.g. ISO 281: 1990(E) (ISO, 2007)). These 

“experience based methods” (Jammu & Kankar, 2011) can be useful for very general planning but neglect 

any stochastic behavior which is usually responsible for step-like changes in bearing condition. 

Trending the severity of a defect can lead to prognostic information (Khan, 1991), (Succi, 1991), 

(Randall, 1985). For instance, if the severity of a defect levels off at a reasonably low damage level over a 

certain time period we can deduce a longer prognostic horizon. A monotonous progression will generate a 

warning. In general the step-like dynamics of defect progression and the resulting vibration noise, make 

trending relatively useless as a prognostic method. 

It is worth noting that there have been attempts to develop purely physical models (i.e. model-based) that 

will generate estimates for RUL, for example, using stress models (YU & Harris, 2001) to estimate 

fatigue and life. However, these model-based efforts have very narrow application as most systems 

contain non-linear elements and degradation is stochastic. 

For quantitative prognostics it is required to use more advanced algorithms that identify repeatable 

patterns that offer not only qualitative information on progression to failure but also quantitative estimates 
45 

of RUL. Current efforts to implement pattern classification and machine learning algorithms for this 

difficult task offer various levels of success and accuracy (Jammu & Kankar, 2011). In particular the use 

of Artificial Neural Networks (ANN) and Fuzzy Logic has been successful at learning pre-failure 

patterns. However, these methods often require extremely large sets of data in order to train and validate 

the algorithms as they require repetition of patterns. Another complication is the correct choice of model 

“features” which will contain the most effective information in the data sets. 

Bearings are convenient examples for AD&P using a hybrid approach, as much is known on the physical 

degradation processes as well as their manifestations. However, the models are complex and therefore 

they end up in the hybrid category rather than model-based. The following two examples illustrate how 

data-based information and model-based knowledge are fused to offer a viable AD&P method. These two 

examples take different approaches but are both based on a hybrid approach: 

• Recently there have been efforts to combine both the sophisticated data and pattern analysis 

involved in a data-driven effort with the knowledge that comes from understanding the 

degradation process in a physical model. Porotsky and Bluvband (2012) have proposed a databased method that incorporates the physical understanding that bearing degradation over time is 

related to the accumulated vibration noise. By evaluating accumulation functions they were able 

to de-noise a small data set that was a priori non-trendable and non-periodic. This method 

allowed them to combine data from bearings operating under very different load conditions as it 

cleverly normalizes the bearing history into the accumulated “integral vibration”. The RUL was 

determined assuming a threshold for the accumulated degradation. 

• Other hybrid methods make use of the physical models or experience-based models to augment 

models based on data-driven features in such a way that a better pattern classification process is 

achieved even with small data sets. Goebel et al propose a bearing prognostics reasoner (Goebel, 

et al., 2005) that integrates information from oil analysis with the information from vibration 

analysis to get a more accurate status of bearing damage. Their approach, designed for avionics, 

works in a two-mode system (in-flight and post-flight) to provide both advanced diagnostics and 

RUL. 

 

4.4.4 Typical Assessment Questions that Arise in the Hybrid Methodology 

When a hybrid analysis is used in a critical application, review questions will mostly follow from 

both the model-based and data-driven methodologies as outlined in sections 4.2.4 and 4.3.4: 

1. What is the source and validation of the model(s)? 

2. How were model parameters selected and validated? 

3. Were studies with alternate models and parameters conducted? How do these compare? 

4. At the EOL decision point, will the item still permit the next higher level to operate 

within its specification under all design basis events? And what is the time scale from 

that decision point to the time that the higher level does not operate within its 

specification? 

5. What is the source for data and how is the data validated? 

6. Are there any site-specific challenges that could affect continuous data availability? 
46 

7. How were the algorithms and Artificial Intelligence tools used for pattern recognition 

chosen? Have they been used in other applications, if so with what success? How are the 

algorithms validated for performance reliability? How were their reliability/repeatability 

tested and ensured? 

8. Were studies with alternate algorithms and parameters conducted? How do these 

compare? 

9. Do the features selected for measurements cover all failure modes including potential 

overlap in effects? 

10. Was simultaneous damage in several failure modes considered in the analysis? If not, is it 

justified? 

11. Are there conditions/states when the physical models can overly restrict the hybrid tools 

and lead to misdiagnosis? 

13. If the system under observation is redundant, are the redundancy provisions (detection 

and switching) included in the hybrid approach? If so, what data are available to assess 

the success probability for switching to an alternate? Also, how is the alternate known to 

be operational? 

14. For redundant systems, are the components that need to be changed capable of being 

removed and installed under power? If not, what provisions are there to remove power? 

15. Are there sufficient trained maintainers on staff at all times? 

16. What level of spares will be maintained? 

4.5 Example of AD&P Implemented in NPP Internals 

 

The final example in this chapter relates the details of an implementation that is model based 

and has been the outgrowth of technology that has been applied and useful in 

operation/calibration monitoring in NPPs. In that respect this is significant as it draws on 

existing technology that has been implemented, validated and trusted in the nuclear 

environment for other tasks. It is believed that this may be a common pathway to introducing 

other AD&P applications, a topic that is more fully discussed in the next chapter. 

The example chosen under this heading is the measurement of neutron noise as an indicator 

of damage processes in fuel rods and structural reactor components. Neutron noise 

measurement is part of a larger group of methods that track and analyze reactor noise (Thie, 

1963), (Williams, 1974) (others are temperature fluctuations, flow fluctuations and pressure 

anomalies). Physical models that link degraded states of these components with a resulting 

neutron noise pattern (both spatial form and amplitude) are well understood. Measurements 

of the noise are directly linked to geometrical aspects of degradation (e.g. rod movement or 

core oscillations). Therefore this is an example of a model-based application. However, the 

models are on a macroscopic level and do not involve the physical degradation process, only 

its manifestation as a failure mode at the part level. Neutron flux detectors are part of the 

plant instrumentation, and the attractive feature is that existing sensors can be used to provide 

diagnostic and prognostic information. The primary purpose of in-core neutron flux detectors 

is to measure the neutron flux distribution and reactor power for: in-core fuel management 

purposes, for control actions, and for initiating reactor protection (trip) functions in the case 

of an abnormal event. Coolant flow and parameters such as Moderator Temperature 

Coefficients can be calculated based on neutron measurement. To accomplish this, the direct 
47 

current (DC) output of the detectors’ current signal is measured and calibrated to indicate the 

neutron flux or the reactor power. The same output also contains small fluctuations (noise) 

that can be analyzed to collect information on the various processes taking place in the core. 

For example, the noise components of ex-core neutron detectors in pressurized water reactors 

(PWRs) can measure the vibration of the reactor vessel and the reactor vessel internals. 

Furthermore, through cross-correlation of neutron signals and other existing sensors such as 

the core exit thermocouples or the reactor vessel level sensors, the flow through the reactor 

can be characterized to detect flow anomalies. 

Neutron noise monitoring is useful as a diagnostic for mechanical degradations and failures 

of the core internals and the core support structures. Based on commercial reactor service 

experience, pertinent laboratory data, and relevant experience from other industries, the 

degradation/ageing mechanisms and sources for reactor vessel internals are (Damiano & 

Kryter, 1990), (IAEA, 2007) 

1. Embrittlement: There are two types of embrittlement. The first is irradiation 

embrittlement, which is caused by neutron bombardment and may affect core region 

internals. The second is thermal ageing embrittlement, which is a time and 

temperature dependent degradation mechanism and may affect the cast stainless steel 

parts and parts manufactured from martensitic stainless steel. The embrittlement 

reduces the margin on crack tolerances and design rules and affects the rate of crack 

propagation. 

2. Fatigue: Defined as structural deterioration that occurs as a result of repeated 

stress/strain cycles caused by fluctuating loads and temperatures such as system 

cycling, thermal cycling and flow induced vibration. Fatigue may cause crack 

initiation and crack propagation. 

3. Corrosion: The electrochemical reaction of steel with the environment that causes a 

detectable change which can lead to deterioration in the function of the component or 

structure. A variety of possible chemical and physical variables lead to a large 

number of types of corrosion such as: corrosion without mechanical loading (general 

corrosion and local corrosion attack, selective corrosion attack such as inter-granular 

corrosion); corrosion with mechanical loading (stress corrosion cracking, corrosion 

fatigue) and synergistic effects of neutron irradiation (irradiation assisted stress 

corrosion cracking); flow induced corrosion attack (e.g. erosion corrosion). 

4. Radiation induced creep, relaxation and swelling. Neutron irradiation creates a large 

number of interstitials and vacancies. Diffusion controlled processes can cause these 

to annihilate on sinks such as dislocations, grain boundaries and surfaces. If 

interstitials are eliminated rapidly, the excess vacancies coalesce into voids or 

bubbles inside the metal leading to swelling of the structure. If a significant stress is 

applied, interstitials can migrate towards locations perpendicular to the applied stress 

creating an irradiation creep or irradiation relaxation phenomena. 

5. Mechanical wear: This degradation type is broadly characterized as mechanically 

induced or aided degradation mechanism. Degradation from small amplitude, 

oscillatory motion, between continuously rubbing surfaces, is generally termed 

fretting. Vibration of relatively large amplitude, resulting in intermittent sliding 

contact between two parts, is termed sliding wear, or wear. Wear generally results 

from concurrent effects of vibration and corrosion. The major stressor in fretting and 

wear is flow induced vibration. 
48 

6. Handling: Internal structures have to be removed periodically, either partially or 

completely, for refueling or in-service inspection. Great care has to be exercised in 

the handling of these internals since some parts (pins, etc.) are easily distorted. This 

can cause degradation of the parts. 

From the operating and maintenance experience of commercial reactors around the world, the 

majority of events reported about problems in internals of PWR are related to stress corrosion or 

fatigue failures, both with and without neutron irradiation effects as summarized in Table 4-3 

(IAEA, 2007). 

Although they come from the same source, damage mechanisms for BWRs are classified in a 

different format and in some cases different nomenclature as shown in Table 4-4 (IAEA, 2005). 

They include corrosion, stress corrosion cracking (SCC), inter-granular stress corrosion cracking 

(IGSCC), irradiation-assisted SCC (IASCC), fatigue, embrittlement, and creep and stress 

relaxation. 

As these tables indicate, a number of damage mechanisms have been identified for each of the 

major reactor types. Although the number is larger than in the case of the pneumatic valve it is 

finite and therefore can form the basis for the techniques described in subsection 4.2. The 

modeling takes the form of a transfer function for the neutron flux that is based on the diffusion 

and scattering properties of the neutrons in the medium. 

The application of noise analysis techniques in nuclear power plants started with neutron noise 

analysis in zero power systems and research reactors (Weinberg & Schweinler, 1949). Later 

applications were concerned with detecting structural vibrations (control rod vibrations), in 

research reactors such as the one at the Oak Ridge National Laboratory (ORNL) and the High 

Flux Isotope Reactor (Fry, 1971). Gradually, neutron noise methods found applications in 

commercial power reactors as well (Fry & Kryter, 1973), (Fry, et al., 1984). At the same time, the 

information content in the fluctuating part of other signals (temperature, pressure, etc.) was 

utilized, either alone or in combination with neutron detectors or other signals (Coble, et al., 

2012). 
49 

TABLE 4-3 PWR DAMAGE MECHANISMS Embrittlement Fatigue 

Corrosion Wear Creep & Swelling Handling 

Upper 

Internals 

Control Rods, Guide tubes 

Guide X X 

Guide tube support split pins X 

Thermocouple columns X 

Core plate fuel alignment pins X X 

Lower 

Internals 

Baffles X X 

Thermal shield fasteners, welds X 

Locating systems (radials key) X 

Instrumentation guide columns X 

Bottom instrumentation thimble X 

In power reactors “noise sources” such as boiling of the coolant/moderator in a BWR, or 

vibrations of mechanical structures in a PWR, cause the medium in which the neutrons travel and 

multiply to fluctuate in time and space. These neutron fluctuations have a non-Poisson character 

because neutrons in the core are affected simultaneously in a random manner and hence their 

parameters become correlated. 

Usually the auto- and cross-spectral distributions (APSD and CPSD, respectively) or auto- and 

cross-correlation functions (ACF and CCF, respectively) of the alternating current (AC) 

component of the measured process signals are generated and used in the analysis of noise 

(Sweeney & Renier, 1984). Apart from a few cases, such as BWR instability, it is assumed that 

the fluctuating signals are small (typically less than 0.1% of the signal’s DC component), and 

linear systems theory can be used. The fluctuations in the measured signal (noise) are caused by 

the fluctuations of another signal (noise source or perturbation) whose effect on the noise is 

exerted through a physical process, described by the transfer function of the unperturbed system. 

This physical process is modeled by the dynamic transfer function. 
50 

TABLE 4-4 BWR DAMAGE MECHANISMS

Component Degradation Mechanism 

Core Plate IGSCC 

Core Spray Internal Piping IGSCC 

Core Spray Sparger IGSCC 

CRD Guide Tube No incidents of cracking reported 

CRD Housing No incidents of cracking reported 

In-Core Housing IGSCC 

Jet Pump 

 Diffuser IGSCC 

 Hold-down beam IGSCC 

 Inlet mixer Fatigue due to improper installation 

 Riser IGSCC 

LPCI Coupling No incidents of cracking reported 

Orificed Fuel Support No incidents of cracking reported 

Core Shroud IGSCC/IASCC 

Shroud Support IGSCC 

Top Guide IGSCC/IASCC 

If the dynamic transfer function of the unperturbed core is known, the measured neutron noise 

can be used to detect, identify and quantify the noise sources. If the properties of the noise source 

are known, these measurements can also be used to monitor changes in the state of the 

unperturbed core, such as reactivity coefficients and stability properties. In this way, both 

operational parameters in the normal state as well as changes of the reactor state to abnormal can 

be quantified. The occurrence of new anomalies can be detected and quantified. The methodology 

used to describe these stochastic processes is based on the linearized form of the Langevin 

technique which includes the following steps: Split up the neutron flux and the cross sections into 

mean values and fluctuations; neglect second order terms; subtract the static equations and 

eliminate the delayed neutron precursors with a temporal Fourier transform. 

The following examples and descriptions of the equipment and implementations show both 

promise, particularly in the diagnostics area, but also limitations. Examples of 

anomalies/disturbances that can be identified include: 

• Determining the location (radial position) of vibrating control rods in PWRs and 

deducing degradation of the tendons; 

• Detecting and quantifying core barrel vibration properties in PWRs and deducing 

degradation of the stringers; 
51 

• Measuring velocity of coolant flow in PWRs and deducing structural anomalies or 

degradation of the pumps controlling the flow; 

• Determining the position of a local thermal hydraulic instability in the core of a BWR; 

• Detecting and classifying detector tube vibrations and impacting in BWRs; 

• Determining two phase flow parameters in BWRs. 

Implementations include both in-core measurement and ex-core measurement: 

 

In core: Typically, several dozen neutron detector chains are suspended in the core. These are called 

Self-Powered Neutron Detector (SPND) chains or In-Core Flux Detectors (ICFDs). The signals 

from these detectors can be mapped to show an image of a cross section through the core. Figure 4-

5 (IAEA, 2012) is an example of such a mapping generated by the PAZAR system (LIPCSEI, et al., 

2009). This image shows an anomaly in the bottom right of the cross section, indicative of control 

rod vibration. The positions of the SPNDs are indicated with little crosses. 

FIGURE 4-5 NOISE MAP OF CONTROL ROD VIBRATION AT 1.1HZ (THIE, 1981) 

 

Ex-core: External neutron detectors can detect core vibrations: In PWRs, the core barrel is a structure 

hanging vertically inside the reactor pressure vessel from its top. Under certain degraded conditions the 

barrel might vibrate. Several modes of oscillations of the core barrel are usually encountered. The most 

prominent ones are the pendulum or beam-mode vibration, and the shell-mode vibration. The Eigenfrequency of the beam-mode is around 8 Hz, whereas for the shell-mode vibration the Eigen-frequency is 

about 20 Hz. In pre-operational test these vibrations can be measured by accelerometers, or pressure 

sensors, but in operational plants these sensors are usually not present and therefore such oscillations are 

commonly detected by ex-core neutron detectors. The vibrations cause neutron noise to be attenuated by 

the changes in distance to the detectors. If multiple detectors are used, both direction and amplitude of the 

motion can be analyzed. Pendular vibrations of the core are indicative of damage to a guide lag. 
52 

In general, reconstructing the perturbation (its location, strength and type) from the detector readings is 

very complicated. This process is called unfolding and requires very good models of neutron diffusion 

and scattering theory. The task is even more complex if the number of detectors is limited. One of the 

primary objectives is the detection of core-barrel vibrations and of fuel or control assembly vibrations.

 

A schematic illustration of an ex-core ion chamber is shown in Figure 4-6 for a typical PWR. In 

boiling water reactors (BWRs), average power range monitors (APRMs) and local power range 

monitors (LPRMs) are used to perform reactor diagnostics and to estimate the flow through the 

core. The APRM and LPRM signals are also used to measure the stability margin for the core in 

terms of a decay ratio. A detector tube with 4 surrounding fuel assemblies in a BWR core is shown 

in the schematic illustration Figure 4-7 (Kosaly, et al., 1981).

FIGURE 4-6 EX-CORE ION CHAMBERS IN A PWR 

 

FIGURE 4-7 DETECTOR TUBE IN A BWR CORE
53 

A classic example of the data acquisition for neutron noise measurement at ORNL is shown in 

Figure 4-8 (Fry, et al., 1984). Isolation of the data acquisition system from the plant equipment is 

required so that it will not impact reactor control system and protection systems. 

FIGURE 4-8 DATA ACQUISITION FOR NEUTRON NOISE MEASUREMENT (FRY, ET AL., 1984) 

The foregoing has emphasized the capabilities in neutron noise analysis for diagnostics because 

this is by far the area in which progress is currently being made. To exploit this methodology for 

prognostic purposes it is necessary to establish 

• End of life (or condition based maintenance) criteria in terms of equipment characteristics 

and in terms of their neutron noise signatures. 

• A model for the progression from a current state to the end of life point. 

• Until such a model is validated, an empirical history of the approach to end of life may 

permit practical prognostics to be developed. 

4.6 Summary 

This chapter presented four representative AD&P applications and introduced a classification 

based on the transparency of the relationships between the equipment state and measureable 

variables. This categorization is useful when investigating the possible challenges in a new or 

reused AD&P application and offers common assessment questions and issues that should be 

considered in each case. The examples chosen for this chapter are cases already in use that have 

been tested and proven to be useful. The fourth example is of an AD&P application in NPP 

internals which illustrates one of the anticipated paths to introduction of AD&P into this critical 

domain: based on existing technology already in use for other monitoring tasks in the nuclear 

environment. This example is a model-based example but not a typical one: the failure 

mechanisms of a rod or the vibration of a vessel are not necessarily well understood, but the 

neutron dynamics are understood well enough to provide a transparent link to a failed or degraded 

state. 
54 

5 Introducing Diagnostics and Prognostics to the Nuclear Power 

Environment 

In their basic forms, diagnostics and prognostics have existed for decades, and documentation of these 

engineering and analysis fields exist in many domains, from reliability through nondestructive testing and 

maintenance publications. Recently more focused vehicles for presenting diagnostic and prognostic tools 

and academic results have emerged, as well as communities that provide the environment for making 

R&D progress in this domain. Some of these are listed in Appendix B. 

Previous chapters provided an overview to diagnostics and prognostics and highlighted techniques and 

concerns relevant to AD&P that involve automated and continuous monitoring. This chapter discusses the 

process of introducing AD&P into an operating environment. There are two pathways by which AD&P 

may be introduced into the nuclear power environment: deliberative and opportunistic. The latter is 

usually derived from an AD&P application that is already implemented in other industries, whereas the 

deliberative path refers to a technique that is developed “from scratch” for the specific equipment under 

review. The deliberative pathway is encountered when a significant gain is anticipated from an 

implementation of an appropriate AD&P method, although the specialized diagnostic or prognostic 

application has not yet been developed. 

The opportunistic pathway is expected to be encountered much more frequently but the deliberative 

pathway will be described first because (as the name implies) it follows a defined format with individual 

steps that can be explained. Many of these steps will also be applicable to the opportunistic pathway but 

they may not be encountered in such an orderly sequence. Of course the methods and technologies used in 

an opportunistic pathway will have originated from a deliberative effort in another industry. 

5.1 Deliberative Pathway 

This pathway is entered by a decision to reduce maintenance expense or downtime for the plant as a 

whole or for a major section of it. In the following it is assumed that downtime can be converted into an 

equivalent expense and only the latter term will be used. An example of the deliberative pathway is found 

in the description of an effort to reduce the maintenance expense on aircraft electronic power converters 

(Celaya, et al., 2012). 

The pathway is initiated with an expense analysis as shown in Figure 5-1. An explanation of the 

contributions to such an analysis was provided in section 2.4. The first step of identifying top contributors 

is based on a review of appropriate expense data for a recent period. Among the high scorers some may 

be rejected from further consideration because of legal, regulatory or organizational concerns, while 

others may be preferred because successful applications have been mentioned in other areas (an 

opportunistic element). Part 2 of this report, Regulatory Considerations, considers the statutory issues that 

affect the introduction of AD&P to nuclear facilities. These include both the requirements that must be 

met in order to satisfy the existing regulations as well as questions about the relevance of the regulations 

in a future that includes significant advances in NPP design as well as in diagnostics and prognostics. 
55 

FIGURE 5-1 THE DELIBERATIVE PATHWAY

Once the highest cost items or most critical components have been selected their predominant failure 

modes are examined. Here again some may be declared ineligible but normally the further steps are all 

directed at one or several failure modes that cause the highest expense. Because of the focus on specific 

failure modes, the deliberative pathway tends to lead to model based or hybrid methodologies. However, 

there can be deliberative implementations that are purely data-driven. In such a process the focus is on the 

most offensive item without delving into the identification or isolation of individual failure modes. The 

process described in the following text and illuminated in Figure 5-1 applies to this case as well, while the 

details can differ substantially. 

 
56 

5.1.1 Example 1 

In this example of aircraft electronic power converters (Celaya, et al., 2012), the first step was implied in 

the statement of the problem: reducing maintenance expense on aircraft electronic power converters. The 

power converter was recognized as the largest contributor to maintenance cost. The repair records 

identified failures of the low voltage (10 VDC) electrolytic capacitors to account for more of the 

maintenance expense than any other item. 

For the selected failure modes functional models are then developed that incorporate all the a priori 

(usually qualitative) knowledge of how the failure modes affect the operation of the item. The equation 

shown to the left of the functional model block in Figure 5-1 is just an example of many possible forms 

that the model can take. Chapter 2 discusses in more detail what the model is based on and some 

examples of the use of models are discussed in Chapter 4. 

For the electrolytic capacitor it is known that during use the capacitance tends to decrease and that the 

parasitic (unintended) series resistance tends to increase. Thus an impedance model can be constructed in 

which Z is the total impedance, z is the nominal capacitance, a represents the scaling factor for the 

decrease in capacitance and b represents the parasitic resistance. The values of a and b need not be known 

at this time. 

Next a degradation model must be developed. Sources are the failure mode literature for the catalog item, 

diagnostic and prognostic literature for the general item type (in the cited example electrolytic capacitors), 

and in-house experience. In the equations shown to the left of the degradation model block in Figure 5-1, 

T represents a generalized stress-time variable that affects the associated parameters. Usually the 

relationship is non-linear. 

Because of the uncertainties in generating the degradation model it is good practice to run an accelerated 

life test to validate the model. The acceleration method may itself have to be independently verified. 

There are other steps in this pathway where methodology or parameters need to be verified in particular 

circumstances and these have been omitted from the figure. 

For electrolytic capacitors it was found that the most likely root cause of the degradation was loss by 

evaporation of electrolyte from the porous paper that separates the aluminum foil electrodes. The 

evaporation increases with the applied voltage, and thus the accelerated life test consists of repeated 

cycles of applying above rated voltage. Once this damage mechanism is verified the propagated damage 

model can be developed and end-of-life values of a and b can be generated. 

Activities beyond this point are described in Section 5.3 since they are common to the deliberative and 

opportunistic pathways. 
57 

5.1.2 Example 2

A hypothetical example applicable to nuclear power and fuel cycle facilities is that of an electric motor 

driving a pump. The functional model, Z, may represent the actual output power while z represents the 

ideal output power. Factors a and b modify the ideal: a may represent the electric efficiency and b the loss 

of output power due to bearing friction (it will take on a negative value). A predominant damage 

mechanism is loss of insulation resistance in the windings. This causes leakage currents that increase the 

internal temperature, leading to further degradation of the insulation. The increase in winding temperature 

reduces power input and increases bearing friction. The motor will slow down and eventually may stall, 

leading to total burnout. An accelerated life test for this damage mechanism can be constructed by 

running the motor at elevated temperature and reduced voltage (thus causing an increase in current for a 

given output load). 

As can be seen, the deliberative pathway can be labor intensive, may take a long time to complete and 

runs the risk of inconclusive test results and other uncertainties in obtaining required data. The advantage 

is that it can be directed at the primary area of expense. There are usually opportunistic elements that can 

be incorporated into the deliberative pathway to overcome some of the disadvantages. 

5.2 Opportunistic Pathway 

This heading describes a number of approaches by which AD&P methodologies can be introduced in the 

user’s domain, including 

• Embedded in an off-the-shelf (OTS) product. 

• Available as an add-on to an OTS product. 

• Available as a free-standing product. 

• Described in the literature but not yet productized. 

• Adaptable from a method described in the literature. 

The listing is from low cost and risk and narrow focus, toward higher cost and broader target areas. 

Except for the first item, in which there is no selection of an AD&P methodology, the adoption process 

will take the form shown in Figure 5-2. 

The customer becomes aware of one or more AD&P methodologies that may be suitable in the target 

environment. These are evaluated on the basis of anticipated return on investment or a similar criterion. If 

the evaluators are confident of a positive return without a prototype test they may proceed directly to 

procurement. Otherwise arrangements for a test will be made and the test results will determine further 

steps. 

An example of an embedded OTS product is the S.M.A.R.T diagnostic and prognostic for disk drives (D22 in the Appendix A). It monitors hard disk drives for many failure modes and degradation mechanisms, 

some with go/no go assessments and some with quantitative measurements for which the user can define 

end-of-life criteria. 
58 

FIGURE 5-2 OPPORTUNISTIC PATHWAY FOR ADOPTION OF AD&P 

An important example of a free-standing AD&P related product is the In Step PRiSM centralized 

performance monitoring software that was mentioned in Section 2.4. 

An organization that experiences a high failure rate of low voltage electrolytic capacitors may consider 

the capacitance and parasitic resistance monitoring methodology described in the preceding section. 

Much of the model generation and verification has already been done, but accessibility of capacitors for 

measurement and the required instrumentation may still present obstacles. 

Adapting the AD&P methodology described here for electrolytic capacitors to another electronic 

component this will require the development of new models and parameter estimations. But the process 

can still make use of the methodology described in the literature and thus avoid some of the development 

risk. This would constitute an example of the adaptation mentioned above. 

It is anticipated that the introduction of digital instrumentation & control systems to the nuclear power 

industry will provide opportunistic paths to the introduction of AD&P. This introduction has already 

begun and it is anticipated that it will become a major part of new and renewed licensing efforts. There 

are actually two pathways here. Digital instrumentation can be monitored to the degree that allows for the 
59 

implementation of AD&P in its own operation. Further, it offers the bandwidth and frequency capabilities 

required to apply advanced diagnostics, and in particular prognostics, to the equipment it controls. 

The decision to adopt AD&P in the opportunistic pathway is taken within the framework of regulations. 

The decision process includes similar questions and issues to those confronted in the deliberative 

pathway. 

 

5.3 Uses of AD&P Data 

 

In keeping with the definitions presented in the introduction to this report, the aim of AD&P is to 

rationalize inspection and maintenance practices. Diagnostics establishes that an item has reached its end 

of useful life, and prognostics generates an estimate of remaining useful life. But there are other avenues 

for utilizing AD&P data and some examples will be discussed here: 

• Reducing exposure to damage mechanisms. 

• Improving access for repair of damage. 

• Improving product design. 

 

In the hypothetical motor example of subsection 5.1.2 the damage exposure can be reduced by not 

allowing heat build-up within the motor, e.g., by supplying external cooling air. Damage to disk drives 

can be reduced by protecting them from shock and vibration. Thinking beyond RUL may bring with it 

operational as well as financial benefits. 

Frequently an obstacle to more productive inspections and more focused repair is the difficulty of 

accessing the most easily damaged components. For instance, the cited reference on the electrolytic 

capacitor prognostics does not mention how the capacitors mounted on a printed circuit board can be 

accessed to make the capacitance and resistance measurements. Usually the capacitors are part of a filter 

circuit in which the presence of other components may mask small changes in capacitor parameters. It is a 

usual maintenance practice to replace circuit boards in their entirety even though only one or a few parts 

may approach end of life. Ability to isolate frequently failing parts may yield large benefits. 

Finally, AD&P data can and should be used to lead to improvements in product design. This can be 

achieved in several ways, including letting the manufacturer know of the damage experience, testing the 

products of competing manufacturers to determine which one is most resistant to the damage mechanism, 

or switching to another component type that can serve the function without exposure to the failure 

mechanism. 
60 

5.4 Quality and Performance Metrics

The assessment or review of a diagnostic or prognostic application necessarily requires an understanding 

of how its performance is measured and qualified. When presented with performance measures it is 

important to understand not only their numerical value but also what they represent and in some cases 

what they can mask. These performance metrics may be related to the top level results but may also be 

indicative of efficiency and quality of embedded algorithms. Although it may not be necessary for a 

reviewer to grasp the entire scope of the application and its performance, a background on the metrics 

they are most likely to be presented with is helpful for a complete picture. This section discusses some of 

the most common performance metrics used in diagnostics and prognostics and in fields that use similar 

technologies. 

As the different diagnostic and prognostic methods cover very different physical measurements, as well 

as a variety of models and algorithms, there are also many different quality and performance metrics that 

can be applied to the methods (Saxena, et al., 2008), (Schwabacher, 2005), (Schwabacher & Goebel, 

2007). This section discusses several metrics that have a broad application and are frequently used to 

qualify applications of AD&P methods. 

Advanced diagnostics and prognostics rely on varying levels of statistical analysis. Hence traditionally, 

performance and quality evaluations borrowed standard metrics from statistics. These metrics are helpful 

in evaluating how well diagnostic and prognostic methods perform, but they are not always sufficient. In 

particular, a single measurement of a metric at a given time may be misleading. It is often useful to track 

over time the values of the metrics recorded at intervals. This is especially important when considering 

prognostics where predictions are updated continuously and should become more accurate as more data is 

collected. The trend over time is as important as any of the instantaneous measurements. Another issue 

that comes up with prognostics and is different from other statistical analyses is the time value of an RUL 

prediction. The quality of an early prediction is not as significant as the quality of a prediction made close 

to the EOL threshold. This is because the time to take action becomes shorter. The accuracy at small RUL 

must be significantly better than that of large RUL. 

Metrics derived from statistical analysis include accuracy and precision based metrics11 which are 

combinations and functions of standard statistical measurements of error and precision: 

• Error: size of the deviation of the result from the actual value 

• Sample Standard Deviation: measures the dispersion/spread of the error with respect to the 

sample mean of the error. 

Different users will apply different filters and function to these metrics in order to arrive at the metric that 

offers them the most focused information: average error, average bias, mean absolute percentage error, 

 

11 Many of these metrics are in wide use in domains such as weather and finance which are 

concerned with the quality of predictions, and where feature classification and trend analysis have 

been used for decades to generate them. 
61 

correlations, mean absolute deviation, median absolute deviation, and other derivatives of these statistical 

measurements. A widely used derivative is that of False Positives and False Negatives which will be 

discussed in more detail below (Duda, et al., 2000). 

All these metrics can also be assessed for their sensitivity: their response to input changes and external 

perturbations. The sensitivity is usually defined as the ratio of the change in the target metric to the 

change in the input or the size of the perturbation. A sensitivity value close to zero indicates robustness, 

while a value that shoots up for certain inputs indicates the method is unreliable. 

It is important to remember in this context that the metric actually driving the development and 

implementation of advanced diagnostics and prognostics in all domains, is the Return On Investment 

(ROI). The significance of AD&P to operating costs was discussed previously in section 2.4. This benefit 

is the engine pulling this industry, and performance in terms of ROI is a significant input to operator 

decisions on implementations. However, it is not relevant to licensing decisions. 

5.4.1 False Positives and False Negatives 

Advanced diagnostic and prognostic measures are frequently implemented in an effort to avoid 

the manual process of removing a component to perform a visual or other inspection in order to 

determine an exact diagnosis. They often rely on indirect features and measurements and benefit 

from analyses of large sets of data. Naturally there are elements of randomness and noise in the 

degradation processes, the measurements, and the analysis models employed. These are 

unavoidable and lead to uncertainties in estimates that are used to diagnose and prognosticate. 

This uncertainty cannot be removed in its entirety although an effort is made to reduce it. The 

uncertainty is expressed in the fact that rather than having point estimates and predictions, 

diagnostic and prognostic algorithms will work with distributions. Figure 5-3 illustrates this 

uncertainty with a simple example of a diagnostic measurement. The solid curve indicates the 

probability of the measurement result reflecting a negative outcome and the dashed curve 

indicates the probability of the measurement result reflecting a positive outcome. As in medicine 

a “positive” result is bad news and indicates the diagnosis of a degraded condition, a negative 

result is good news and indicates the system is functioning well. Operators will rely on these 

distributions to reach a point decision on the diagnosis (positive or negative). To do so a 

threshold is imposed: measurements to the right of the threshold are considered positive, to the 

left, negative. 

 

When the measurement is far to the right or far to the left of the threshold one can be confident 

with regard to its interpretation. When the measurement is in the region where the distributions 

overlap the interpretation may be wrong. Correct interpretations are designated as “true”: True 

Positive (TP) means a correctly interpreted positive diagnosis; True Negative (TN) means a 

correctly interpreted negative diagnosis. Incorrect interpretations are designated as “false”: False 

Positive (FP) means a positive diagnosis of a state with an actual negative value (i.e. diagnosing 

a problem which does not exist, a false alarm); False Negative (FN) means a negative diagnosis 
62 

of a state that is actually positive (i.e. missing a diagnosis of a failure or degraded condition). 

These definitions are illustrated in Figure 5-3. 

Negative Diagnostic 

TN Positive Diagnostic

FN

TP

FP

Negative Diagnostic 

TN FN Positive Diagnostic

TP

FP

FIGURE 5-3 DISTRIBUTIONS OF DIAGNOSTIC MEASUREMENT: (TOP) A LOW THRESHOLD WILL INCUR 

MORE FALSE POSITIVE (FP) RESULTS; (BOTTOM) A HIGH THRESHOLD WILL INCUR MORE FALSE 

NEGATIVE (FN) RESULTS. 

When the measurement is far to the right or far to the left of the threshold one can be confident 

with regard to its interpretation. When the measurement is in the region where the distributions 

overlap the interpretation may be wrong. Correct interpretations are designated as “true”: True 

Positive (TP) means a correctly interpreted positive diagnosis; True Negative (TN) means a 

correctly interpreted negative diagnosis. Incorrect interpretations are designated as “false”: False 

Positive (FP) means a positive diagnosis of a state with an actual negative value (i.e. diagnosing 

a problem which does not exist, a false alarm); False Negative (FN) means a negative diagnosis 

of a state that is actually positive (i.e. missing a diagnosis of a failure or degraded condition). 

These definitions are illustrated in Figure 5-4. 
63 

FIGURE 5-4 DEFINITIONS FOR FALSE POSITIVE AND FALSE NEGATIVE INTERPRETATIONS

False positive and false negative rates are commonly used as a metric for evaluating the 

performance of classifiers, and in particular diagnostic and prognostic classifiers. Although they 

provide a measurement of how well the classifier is performing they can be misleading. Consider 

a diagnostic measurement that proves to have very few FN events during test. Although this is a 

good indicator one must be careful to question whether there are few FN events because the 

classifier is exceptionally well designed, or is it because there are overall very few situations of 

actual positive conditions (in other words, the system is not misdiagnosing positive events, 

because there are hardly any such events to begin with). To overcome this type of bias, several 

ratios are defined that provide a more robust metric of the classifier performance: 

 

Sensitivity or True Positive Rate: TP FN

TP

P

TP TPR

+ = = (Eq. 5-1)

Specificity or True Negative Rate: 

TN FP

TN

N

TN TNR

+ = = (Eq.5-2) 

Specificity and Sensitivity are better measures of the actual success rate of the classifier – be it a 

diagnostic or prognostic tool. A combined graphical presentation of the TPR and the False 

Positive Rate (FPR=FP/(FP+TN)) is termed the Receiver Operating Characteristic (ROC) and is 

used to fine tune the algorithms to optimize both specificity and sensitivity. Figure 5-5 shows a 

typical ROC curve. The curve is a plot of the value of the TPR as one tunes the FPR from low to 

high. The dashed line indicates a “neutral” system with a ratio of 1. This line differentiates 
64 

between good and poor classifiers: Any algorithm or tool falling beneath the diagonal would be 

considered a poor classifier. The solid curve in Figure 5-5 exhibits a good classifier and indicates 

how to optimize or “tune” it in terms of false positives and negatives (where to put the 

threshold). 

FIGURE 5-5 ROC CURVE

The area under the ROC curve can also be used as a score for the diagnostic or prognostic method. In 

practice tuning the algorithm to improve the position on this curve can be difficult due to lack of data as 

well as lack of “tuning” parameters. 

 

5.4.2 Cost of False Positives and False Negatives 

Figure 5-3 illustrates how a diagnostic tool can be tuned towards more FP or more FN by 

moving the vertical threshold line. It is tempting to balance the system so as to minimize the FN 

events at the price of more FP events: False negatives are the events where a degraded condition 

(which should yield a positive diagnosis) is missed by the diagnostic system. Indeed a FN can be 

critical as operators will not receive warning of an imminent failure that can cause catastrophic 

failure or complete shutdown. Decision makers will want to avoid the cost of a catastrophic FN 

event. 
65 

What about FP events? Although a FP event will not cause a direct failure, only increased 

workload or brief operational disruptions, a system biased towards a high rate of FP events will 

eventually lose its credibility. When experiencing multiple false alarms the human tendency is to 

ignore the alarms all together. At the very least, the method is ineffective and does not contribute 

to the ROI as it is ignored. At the very worst, an ignored TP (True Positive) has the same cost as 

a FN. It can be catastrophic. 

If a quantitative objective can be identified, such as the plant’s operating cost, this can be minimized by 

selecting the threshold (the vertical line in Figure 5-3) such that the expected increase in operating cost 

due to false negatives is equal to that due to false positives. The expected cost increase due to false 

negatives is the product of the probability of a false negative (the area of the negative curve to the right of 

the threshold) and the cost of a false negative; the expected cost increase due to a false positive is the 

probability of a false positive (the area of the positive curve to the left of the threshold) and the cost of a 

false positive. Thus, if the cost of a false positive is equal to one-tenth of that of a false negative, the 

threshold should be set such that the probability of a false positive is ten times that of a false negative. 

5.4.3 Prognostic Metrics 

As discussed above, the evaluation of prognostic tools differ somewhat from that of diagnostics 

because of the time element involved. The metrics applied to classifiers can be applied to 

prognostic tools as well, but their relevance may be limited if the progression in time is not 

accounted for. A FP event would be defined as an unacceptably low RUL prediction while a FN 

event is defined as an unacceptably high RUL prediction. 

A very useful metric is that of Prognostic Horizon (PH) which quantifies how much in advance a 

prognostic tool or algorithm can predict RUL (within a specified accuracy) before a failure 

event. Consider an algorithm that provides an accurate prediction but only minutes before actual 

failure. As accurate as this prediction is, it is useless if it does not allow for the planning of 

undisruptive maintenance (or in safety-critical applications the corrective action that will prevent 

catastrophic failure). 

A more formal metric that touches on the prognostic horizon is the α-λ performance which requires the 

prediction accuracy at specific times to follow a narrowing cone: α is an accuracy modifier and λ is a 

window modifier: a parameter that quantifies the prognostic window (RUL). The required accuracy band 

will narrow as RUL shrinks. In order to comply with desired α-λ specifications at all times, an algorithm 

must improve with time to stay within the cone. 

A similar prognostic metric is that of “timeliness” which penalizes errors of later predictions with an 

exponential weight. Thus a statistical quality metric can be modified by a “timeliness” factor. 

Other such metrics that are sensitive to the time dimension include Relative Accuracy (RA) which 

quantifies the accuracy relative to the actual time remaining before failure, and Convergence which 

quantifies how fast the performance converges for an algorithm as the End of Life (EOL) approaches.
66 

6 The Future of AD&P in Nuclear Power Plants and Nuclear Fuel Cycle 

Facilities 

AD&P can make a substantial contribution to the economics of the nuclear industry by helping to reduce 

maintenance cost and downtime, but the current use is sparse. An understanding of the failure process is 

essential for successful application of AD&P and therefore the first two sections of Chapter 2 have been 

devoted to that subject. This is followed by description of the critical role of instrumentation in providing 

knowledge of the state of equipment. Some instrumentation failures can lead to dangerous plant 

conditions and these should be considered in any AD&P efforts. 

 

The broad range of AD&P techniques has been described in Chapter 3, and Chapter 4 covers several 

examples in detail in order to demonstrate research and data requirements for a successful application. 

Probable pathways for the introduction of AD&P have been described in Chapter 5. Appendix A contains 

an annotated listing of over 50 representative AD&P techniques. The evaluation of their maturity shows 

few that are commercially available, particularly in the prognostics area. Lest this be interpreted as a 

bleak outlook for AD&P a discussion of several trends is presented below that indicate much promise for 

the field. These trends are 

• The growing emphasis on passive design features for ensuring the safety of nuclear power 

generation and fuel handling may increase opportunities to use AD&P without requiring 

regulatory approval. 

• The steadily increasing capabilities of electronics and their reduced cost and physical dimensions 

• The maturation of the field of Prognostics and Health Management 

• The effect on future application of AD&P on the interaction of these primary trends 

6.1 Passive Safety Features 

The trend toward increasing use of passive safety features is exemplified in Vogtle Unit 3 in Burke 

County, Georgia. It is the next plant scheduled to go online in this country. The plant is a Westinghouse 

AP 1000 pressurized water design, for which it is claimed that it uses 

• 50% fewer safety-related valves 

• 35% fewer pumps 

• 80% less safety related piping 

• 85% less control cable 

• 45% less seismic building volume 

than previous pressurized water reactors (Schulz, 2006), (Bull, 2010). Similar reduced dependence on 

active safety features can be found in most new designs: “The greatest departure from most designs now 

in operation is that many incorporate passive or inherent safety features which require no active controls 

or operational intervention to avoid accidents in the event of malfunction, and may rely on gravity, natural 

convection or resistance to high temperatures.” (World Nuclear Association, 2013) 
67 

New reactor designs bring with them the opportunity to introduce new features that promise cost savings, 

some through use of AD&P. Economic motivation and advances in electronic capabilities (see next 

section) may lead to increased use of AD&P in the operational functions of NPPs and fuel cycle facilities. 

6.2 Increased Electronic/Digital Capabilities 

In 1965 Gordon E. Moore observed that the number of transistors on a given size chip had doubled every 

two years for the past ten years and that it was expected that this trend would continue (Moore, 1965). It 

is now almost 50 years since “Moore’s Law” has been published, and its validity seems to be as firmly 

established as ever. This has led to vastly greater functionality, speed and precision (or resolution) of 

anything that can be implemented as an integrated circuit, as well as very significant reduction in cost and 

bulk. Many improvements in NPPs have been made possible by this trend, such as much more capable 

control room displays and more precise and lower cost sensors and controllers. 

The self-monitoring instrumentation discussed in Subsection 2.3.1 is one obvious application of the more 

capable and economical electronics to AD&P. All varieties of self-test (see Section 3.3) are heavily 

dependent on readily available computer memory (both for selection and sequencing of test, and for the 

interpretation of test results). Practically all AD&P techniques in Chapter 3 depend on highly capable and 

affordable digital electronics. Artificial intelligence and machine learning mentioned in Chapter 4 as part 

of AD&P examples would not be possible without semiconductor manufacturing and packaging 

techniques that were introduced only in the past 20 years. 

Continued progress in the development of electronics is expected to be a major contributor to the 

availability and affordability of free standing diagnostic and prognostic equipment and even more so to 

the incorporation of AD&P techniques in equipment that will be used in NPPs and fuel cycle facilities. 

6.3 The Field of Prognostics & Health Management 

Diagnostics have become an accepted and valued part of everyday life, e. g., the diagnostic connector in 

our cars and the diagnostic indications on the control panel of many household appliances. Prognostics, 

on the other hand, are not as well known, except in the aircraft and defense sector. This may change as 

additional industries and the commercial sector look for ways to reduce maintenance expenses. 

One of the signposts that this movement is alive is the increasing number of prognostics and health 

management (PHM) conferences, publications and organizations (see Appendix B) including the 

development of a standard (IEEE Reliability Society, Draft P1856). The standard establishes defined 

equipment levels that are addressed by the prognostic applications, distinguishes between types of 

prognostics (model-driven, data-driven and others), emphasizes the importance of knowledge of failure 

modes and attempts to classify algorithms that are to be used. It also addresses economic motivation 

(return on investment) and implementation issues. The terminology used in this report is generally 

consistent with that proposed in this draft standard. 
68 

This first Part of the report serves primarily an educational function and is followed by a second Part that 

focuses on regulatory considerations. 

 
69 

Part 2: Regulatory Considerations 

Chapter 7 discusses NRC regulations and their accompanying regulatory guides (RG). Also covered are 

two IEEE standards pertinent to AD&P IEEE Std 603 and IEEE Std 7-4.3.2. IEEE Std 603-1991 is 

incorporated by reference in 10CFR50.55(a)(h) and IEEE Std 7-4.3.2 is referenced in IEEE Std 603 and 

endorsed in RG 1.152. The chapter describes how AD&P equipment is categorized and how this affects 

the extent of the regulations. Since the initiative to introduce AD&P will come from the operators who are 

motivated by potential reductions in maintenance and monitoring costs this chapter also discusses 

regulations for change in Surveillance Requirements (SR) and in surveillance frequencies. Other possible 

changes to Technical Specifications (TS) are also discussed. 

Chapter 8 focuses on industry standards. These consider the IEEE standards mentioned in Chapter 7 in 

more detail. Appendix B of this report provides a list of organizations dedicated to the progress in the 

field of AD&P and includes working groups and organizations that are developing standards and/or 

guidance for regulated use of AD&P. A typical process for government regulation of a new technology is 

through the adoption in whole or part of existing, industry standards and codes and this may be one of the 

avenues for coverage of AD&P. 

Chapter 9 considers the path for the introduction of AD&P into NRC regulated facilities and discusses 

how the goals of AD&P implementations correspond with the goals of government regulation (in 

particular the safety regulation). 

Chapter 10 presents conclusions of both parts of the report. 

 
70 

7 Nuclear Regulatory Commission Documents 

The governing document for all regulatory requirements for NPPs and NFCFs is 10CFR (U.S. 

Government 10CFR). This chapter examines regulations applicable to diagnostics and prognostics. It first 

looks at what equipment may be subject to regulations (section 7.1) and then at the requirements 

pertaining to AD&P established by the regulations (section 7.2). Technical Specifications are discussed 

in section 7.3, and section 7.4 focuses on Surveillance Frequencies. Regulatory requirements specific to 

NFCFs are discussed in Section 7.5. 

7.1 AD&P and Plant Equipment 

10CFR50.65 is titled “Requirements for Monitoring the Effectiveness of Maintenance at Nuclear Power 

Plants”, frequently called the Maintenance Rule. The opening sentences are abstracted below. 

[The licensee] shall monitor the performance or condition of structures, systems, or 

components, against licensee-established goals, in a manner sufficient to provide 

reasonable assurance that these structures, systems, and components, as defined in 

paragraph (b) of this section, are capable of fulfilling their intended functions. These 

goals shall be established commensurate with safety and, where practical, take into 

account industrywide operating experience. When the performance or condition of a 

structure, system, or component does not meet established goals, appropriate corrective 

action shall be taken. 

The Maintenance Rule can serve as an indication (not necessarily comprehensive) of equipment that may 

benefit from AD&P for predictive maintenance or replacement. Plant Q-Lists, as well as all Technical 

Specification requirements, provide the plant-specific list of safety-related equipment subject to 

regulation. 

An analysis of 10CFR50.65 has been issued by an industry group, the Nuclear Energy Institute, as 

NUMARC 93-01, Rev. 4 9 (Nuclear Energy Institute, 2010)12 and contains a listing of equipment subject 

to the Maintenance Rule. That table from Section 8.2.1 is reproduced below because it provides a 

convenient (even if not authoritative) view of what is covered by the Maintenance Rule. 

 

 

12 RG 1.160 rev. 3 “Monitoring the Effectiveness of Maintenance at Nuclear Power Plants” was 

issued in 2011 (NRC, 2012) and states that Revision 4A to this document “provides methods that 

are acceptable to the NRC staff for complying with the provisions of 10 CFR 50.65 with the 

following provisions and clarifications...” The text that follows addresses application details and 

allows in many cases more alternatives than the NUMARC document. 
71 

TABLE 7-1 EQUIPMENT SUBJECT TO THE MAINTENANCE RULE

Equipment Type Example

Safety related equipment Coolant systems, shut-down provisions, containment 

Accident/transient mitigation 

equipment 

Fire suppression system, boric acid transfer system 

Emergency operation equipment Emergency lighting and communication 

Equipment that prevents a 

safety-related operation 

Instrument air system that controls containment isolation 

valves, pipes that can leak into safety instrumentation 

Equipment that can cause reactor 

shut-down 

Failures in turbine-generators, multiple rod drops 

It is repeatedly emphasized in the NUMARC document that industry-wide experience is to be utilized in 

assigning an appropriate type designation to equipment; thus, if it has been observed anywhere that a leak 

affected the operation of a safety-related component, that piping installation is classified as falling within 

the Maintenance Rule. 

7.2 Requirements Impacting Diagnostics and Prognostics 

Provisions of 10CFR50.55(a)(h) may affect implementation of AD&P. Excerpts from this regulation are 

reproduced below. 

(h) Protection and safety systems. (1) IEEE Std. 603-199113 , including the correction 

sheet dated January 30, 1995, which is referenced in paragraphs (h)(2) and (h)(3) of this 

section, is approved for incorporation by reference by the Director of the Office of the 

Federal Register in accordance with 5 U.S.C. 552(a) and 1 CFR Part 51. 

(2) Protection systems. For nuclear power plants with construction permits issued after 

January 1, 1971, but before May 13, 1999, protection systems must meet the 

requirements stated in either IEEE Std. 27914, "Criteria for Protection Systems for 

Nuclear Power Generating Stations," or in IEEE Std. 603-1991, "Criteria for Safety 

Systems for Nuclear Power Generating Stations," and the correction sheet dated January 

30, 1995. For nuclear power plants with construction permits issued before January 1, 

1971, protection systems must be consistent with their licensing basis or may meet the 

requirements of IEEE Std. 603-1991 and the correction sheet dated January 30, 1995. 

 

13 This standard has been superseded by IEEE Std. 603-2009. A Rulemaking is in process to address 

differences between IEEE Std. 603-1991 and IEEE Std. 603-2009. 

14 This standard was withdrawn in 1984 (IEEE, 1971) 
72 

(3) Safety systems. Applications filed on or after May 13, 1999, for construction permits 

and operating licenses under this part, and for design approvals, design certifications, and 

combined licenses under part 52 of this chapter, must meet the requirements for safety 

systems in IEEE Std. 603–1991 and the correction sheet dated January 30, 1995. 

7.2.1 Single Failure and Independence Criteria 

Important concerns in connection with AD&P arise from Definitions and Explanations that precede the 

General Design Criteria listed in 10CFR50 Appendix A. These include the single failure criterion which 

in part states: 

A single failure means an occurrence which results in loss of capability of a component 

to perform its intended safety functions. Multiple failures resulting from a single 

occurrence are considered to be a single failure. 

An example of how this can be pertinent is when the source of data for the safety function is shared with 

an AD&P component. Any failure mode within the connection to the AD&P component that can affect 

the data flow to the safety function will violate the single failure criterion. Some commonly used methods 

for providing isolation are discussed in subsection 2.3 of this report. 

Independence requirements are detailed in IEEE 603-1991 which, as mentioned in the beginning of this 

chapter, is incorporated by reference in 10CFR50. There are several elements of the implementation and 

use of AD&P which can have ramifications on independence and these are dicsussed below: 

Independence between redundant portions of a safety system. An issue may arrise when AD&P is used to 

compare independent channels in order to monitor channel health. If the AD&P is basically a single 

channel function it can in effect be viewed as a single channel placed in series with the multiple channel 

redundant system. This reduces independence and also may violate the single failure criterion. It is 

recommended that AD&P should not be implemented within any safety system. Safety system 

monitoring data should be exported and analyzed outside of safety systems. 

Independence between safety systems and other systems. A possible application of an AD&P 

implementation serving multiple systems will also be required to maintain the independent status of any 

safety-system it may be serving. The derived requirement that an AD&P implementaion cannot serve 

both safety-related and non-safety systems may be unnecessarily restrictive as there could be instances 

when such an implentation may share information but maintain the independence required by safety 

standards. There could arise a need to include AD&P-specific regulation as it applies to this criterion in 

order not to unnecessarily restrict operators in the implementation of AD&P. 
73 

7.2.2 Regulatory Guide 1.153 

Guidance for the application of par. 50.55a(h) is provided by RG1.153, Criteria for Safety Systems, 

(NRC, 1996) the pertinent provisions of which fall into two parts 

• Restatement of nuclear reactor design criteria from Appendix A of 10CFR50 as a basis for 

requirements for safety systems 

• Restatement of the applicability of IEEE Std 603 and of other industry standards that will be 

discussed in the next chapter. 

Many of the design criteria require an assessment that the present or future strength of an SSC is at least 

equal to a load that may be imposed on it by a combination of environmental extremes (including 

earthquakes, tornadoes and tsunamis) and abnormal operating conditions. This implies that some form of 

diagnostic and prognostic capability may be beneficial but it does not state that it must be automated or be 

advanced in any form. 

A set of circumstances that may impact AD&P implementations is encountered in connection with 

Criterion 21 

Protection system reliability and testability: The protection system shall be designed for 

high functional reliability and in-service testability commensurate with the safety 

functions to be performed. Redundancy and independence designed into the protection 

system shall be sufficient to assure that (1) no single failure results in loss of the 

protection function and (2) removal from service of any component or channel does not 

result in loss of the required minimum redundancy unless the acceptable reliability of 

operation of the protection system can be otherwise demonstrated. The protection system 

shall be designed to permit periodic testing of its functioning when the reactor is in 

operation, including a capability to test channels independently to determine failures and 

losses of redundancy that may have occurred. 

 

In-service testability and the other features mentioned in this criterion represent a need for diagnostic 

capabilities. The implementation of the diagnostic capabilities is not specified, and compliance could take 

the form of manual record keeping. 

The requirement for independence and redundancy must be maintained regardless of AD&P use. 

Sensitivity to single failures is interpreted to apply also to the AD&P servicing any safety system as it can 

result in missed latent failures or progression to failure. The particular relevance of this provision to 

AD&P is discussed in Section 7.2.1 above. 

The one exception to the technologically neutral position relative to the means of failure detection is 

found in Criterion 20 that requires automatic operation of core protection functions: 

Protection system functions. The protection system shall be designed (1) to initiate 

automatically the operation of appropriate systems including the reactivity control 
74 

systems, to assure that specified acceptable fuel design limits are not exceeded as a result 

of anticipated operational occurrences and (2) to sense accident conditions and to initiate 

the operation of systems and components important to safety. 

The sensing and data handling required for this function are usually simple and may not need the 

capabilities usually associated with AD&P but do not exclude them. 

Several of the criteria that deal with testing, notably General Design Criteria 37, 40, 43 and 46, 

contain a requirement to permit periodic testing. Generally the plant technical specification 

identifies the means for and frequency of this surveillance in the Surveillance Requirements (SR) 

sections. AD&P may in some cases provide a continuous monitoring capability and in others 

offer prognostics that permit a considerable extension of the inspection interval. Surveillance 

frequency is discussed in section 7.4. 

In summary, neither the cited portions of 10CFR50 nor RG 1.153 were prepared with any awareness of 

advanced diagnostic and prognostic techniques. The provisions do not present a serious obstacle to the 

adoption of AD&P. Since updating of RG 1.153 is probably easier to accomplish than modification of 

10CFR50 this may be an avenue for facilitating the benefits to plant safety and economics that may be 

achieved with the introduction of AD&P. 

 

RG 1.160 rev. 3 “Monitoring the Effectiveness of Maintenance at Nuclear Power Plants” issued 

in 2011 (NRC, 2012) states the acceptance by NRC staff of compliance methods presented in 

NUMARC 93-01 (Nuclear Energy Institute, 2010). This statement however, does not change the 

conclusion that the Maintenance Rule (10CFR50.65) requires diagnostic capability but not 

necessarily through the use of AD&P. 

7.3 Technical Specifications 

Part of an NRC license authorizing the operation of a nuclear power station is the Technical Specification 

(TS). It establishes requirements for items such as safety limits, limiting safety system settings, limiting 

control settings, limiting conditions for operation, surveillance requirements, design features, and 

administrative controls. None of several current TS documents that were reviewed in the preparation of 

this report show awareness of AD&P (or its variants). This is consistent with the focus on results rather 

than on methodology. It is conceivable that widespread adoption of an AD&P technique may make it an 

acceptable method for satisfying certain criteria. The adoption will be facilitated if the technique has been 

defined by a professional standard such as generated by the ASME or IEEE. 

Regulatory concerns arise when the introduction of an AD&P method can affect the existing surveillance 

requirements in a TS. An example of the introduction of alternative means to satisfying a surveillance 

requirement is given below from the Babcock and Wilcox TS Bases (NRR, 2012): Under the Post 

Accident Monitoring (PAM) Instrumentation (Section B.3.3.17) alternative monitoring is authorized in 

the following language: 

“At this unit, the alternative monitoring provisions consist of the following:” , 
75 

and is followed by alternative Surveillance Requirements specific to these units. The particular case of 

alternatives to surveillance frequnecy requirements is discussed in detail in Section 7.4. 

An indirect inclusion of AD&P in TS is through the Surveillance Requirements, in particular the 

Surveillance Frequencies. The required surveillance type and frequency are derived from the equipment 

failure characteristics as well as the current surveillance procedures. The improved assessment of 

equipment condition by AD&P is an element that will enter into this equation. In particular, it can be 

anticipated that implementation of AD&P may cause the plant operator, to modify (reduce) manual, 

periodic surveillance. The regulatory considerations of surveillance frequencies are discussed in section 

7.4. 

Another manner in which TS may be modified as a result of AD&P usage is through both quantitative 

and qualitative parameters that define various “conditions” that require action. Improved diagnostics, and 

in particular prognostics, could lead to more specific and reliable measures of equipment limits (for 

example: a requirement that control rod alignment be within a specified range of values) that will be 

included in a TS. The bases for these changes can be anticipated to derive from the industrial standards 

established by professional organizations such as ASME and IEEE. 

The discussion of the benefits of AD&P to improve plant assessment earlier in this report may cause 

licensees to make requests to 

• Safely reduce the frequency of surveillance. 

• Make safe changes in other surveillance requirements that pertain to the operator’s ability to keep 

equipment operational while performing surveillance. 

If the existing TS and their regulatory bases are interpreted as “anything that is not prohibited is allowed” 

a gradual introduction of AD&P will not encounter significant obstacles. 

7.4 Modification of Surveillance Frequencies 

As discussed in the previous section, the issue of surveillance frequencies is not the only factor that can 

affect the introduction of AD&P but it is an important and recurring topic, and initially it may be the 

primary area where implementations of AD&P will interact with regulations. Surveillance frequencies are 

established in the Surveillance Requirements (SR) in Technical Specifications. 

7.4.1 Precedents for Modification of Surveillance Frequencies 

As discussed earlier in this chapter, one of the main areas in which introduction of AD&P may have 

regulatory ramifications is in licensee requests to change the frequency of periodic surveillance.

Generally the plant Technical Specification identifies the means for and frequency of the surveillance in 

the Surveillance Requirements (SR) sections. Typically the frequency established in the Surveillance 

Requirements comes from one of the following sources: 

1. 10CFR 50 (e.g. for the reactor Coolant System Pressure Isolation Valve, CFR 50.55a(g) (U. S. 

Nuclear Regulatory Commission, 1996)). 
76 

2. The American Society of Mechanical Engineers (ASME) code for Operation and Maintenance of 

NPP (ASME, 2011). 

3. Regulatory Guides (e.g. for standby diesel electrical power generator Regulatory Guide 1.9 

Revision 3, 1993 (NRC, 1993)). 

4. Generic Letters (e.g. for standby diesel electrical power generator Generic Letter 84-15 (NRC, 

1984)). 

5. A “Surveillance Frequency Control Program” (see discussion below). 

The last will probably initially be the preferred route because of established precedents. 

As AD&P becomes more ubiquitous in the industry one can anticipate that the 

advantages, as they relate to frequency of surveillance, will be formalized via one of the 

other means (1-4 above). The regulatory framework for the licensee-sponsored 

“Surveillance Frequency Control Program” (SFCP) is discussed below. 

TSTF-425 (TSTF, 2009), is the Technical Specification Task Force transmittal that provides the basis for 

the modification of Technical Specifications (TS) to include the SFCP. It forms the basis for the transfer 

of control over surveillance frequencies to the licensees. TSTF-425 proposed the following changes to 

TS: 

• Adding the new administrative control programs which are the Surveillance Frequency Control 

Program (for example Specification 5.5.20 in NUREG-1430, 1431, 1432) 

• Revision of the Bases of Surveillance Requirements (SR) to include insertions that accommodate 

a surveillance frequency in accordance with the SFCP. 

Of specific interest are the key safety principles that TSTF-425 lists as part of the Technical Analysis 

(Section 4.0). In the implementation of new AD&P there may be questions as to compliance with some of 

these principles. In particular: 

Criterion 2: “The proposed change is consistent with the defense-in-depth philosophy” (Key 

Safety Criterion #2 in Section 4.0: Technical Analysis of TSTF-425).

An AD&P implementation that modifies existing design patterns (in particular those related to 

redundancy and manual controls) will require justification and verification that defense-in-depth 

is maintained. 

Criterion 3: “The proposed change maintains sufficient safety margins.” (Key Safety Criterion #3 

in Section 4.0: Technical Analysis of TSTF-425). 

Criterion 4: “When proposed changes result in an increase in core damage frequency or risk, the 

increases should be small and consistent with the intent of the Commission’s Safety Goal Policy 

Statement” (Key Safety Criterion #4 in Section 4.0: Technical Analysis of TSTF-425).

 Criterion 5: “The impact of the proposed change should be monitored using performance 

measurement strategies.” (Key Safety Criterion #5 in Section 4.0: Technical Analysis of TSTF425).
77 

The principles stated above may require verification for a new AD&P implementation based on the 

guidance of NEI 04-10 “Risk-Informed Method for Control of Surveillance Frequencies” (Nuclear 

Energy Institute , 2007) (as discussed below). 

7.4.2 NEI 04-10 Risk Informed Technical Specification Task Force and Regulatory Guides 

(1.177, 1.175, 1.174 and 1.200) 

TSTF-425 emphasizes that the basis for changing a periodic surveillance frequency should be in 

accordance with the Nuclear Energy Institute (NEI) Risk Informed Technical Specification Task Force 

(RITSTF) to implement RITSTF Initiative 5b documented in NEI 04-10 (Nuclear Energy Institute, 2007). 

NEI 04-10 was approved by the NRC initially only as applied to BWRs, and did not include relocation of 

requirements to perform surveillance on a staggered test basis. However, Revision 1 of NEI 04-10 (the 

accession number for this revision is ML071360456) removes both these restrictions. TSTF-425 Revision 

3, which is dated March 2009 applies to both BWR and PWR and allows for licensee controlled changes 

of frequency of testing as well as methodology: staggered or sequential. NEI 04-10 provides the 

acceptable methodology for applying a Probabilistic Risk Assessment (PRA) as a basis for changing a 

surveillance/test frequency. When applied, the resulting frequency will be placed in the list included in 

the SFCP. 

The technical adequacy of the PRA for a risk-informed decision is addressed by NRC RG 1.200, “An 

Approach for Determining the Technical Adequacy of Probabilistic Risk Assessment Results for RiskInformed Activities” (NRC, 2009). This guide can be followed when proposing a change to the SFCP 

according to the process defined in NEI 04-10. The NEI 04-10 process offers a qualitative route, and 

when the qualitative route is insufficient a quantitative route, which requires recalculating Core Damage 

Frequency (CDF) and Large Early Release Frequency (LERF), provide the input for the PRA. A 

qualitative route may be sufficient if the requests for change are minimal. NEI 04-10 and TSTF-425 

require that the PRA be continuously revisited and updated. NEI 04-10 requires the review and approval 

by an Independent Decision-making Panel (IDP). NEI 04-10 draws on several regulatory guidance 

documents for safety and regulatory justification that are discussed below: RG 1.177 (NRC, 2011), RG 

1.174 (NRC, 2002) and RG 1.175 (NRC, 1998). 

RG 1.177 “An Approach for Plant-Specific, Risk-Informed Decision Making: Technical 

Specifications” 

This regulatory guide describes methods acceptable to the NRC staff for assessing the nature and impact 

of proposed TS changes by considering engineering issues and applying risk insights. These are 

applicable to surveillance and test frequency requirements and are directly implementable when 

considering changes due to the adoption of new AD&P. 

TS changes are expected to meet a set of five key principles. Of significance in the case of changes due to 

adoption of a new AD&P program and a change in surveillance frequency is the requirement to monitor 

the impact of the proposed change using performance measurement strategies. The following are directly 
78 

applicable in the case of AD&P: Regulatory Position 3.1 and Maintenance Rule 10CFR50.65 discussed 

in Regulatory Position 3.2. 

 

According to RG 1.177 the considerations and data requirements for a change in surveillance frequency 

or method (i.e. staggered or sequential) (listed in paragraph A-1.1) are based on risk measures. These are 

further detailed and discussed in NUREG/CR-6141 (Samanta, et al., 1994).Paragraph A-2.3.3 discusses 

Data Relating to Component Testing which is also determined by PRA studies. Implementation of AD&P 

will affect such measures as the efficiency of test or the (negative) effects of surveillance testing (e.g. 

introduction of plant transients). 

RG 1.175 An Approach for Plant-specific, Risk-Informed Decision making: In Service Testing 

The traditional requirement for all plants that In Service Testing (IST) programs are performed in 

compliance with the requirements of 10CFR50.55a(f) and with Section XI of the ASME Boiler and 

Pressure Vessel Code (ASME, 2011),is relaxed by RG 1.175 which describes an acceptable alternative 

approach applying risk insights from PRA to make changes to a nuclear power plant's IST program (Risk 

Informed In Service Testing, RI-IST). An accompanying Standard Review Plan (SRP) (NRC, 1998a) is 

provided to the NRC staff in reviewing RI-IST applications. 

This regulatory guide provides application-specific details of a method acceptable to the NRC staff for 

developing RI-IST programs and supplements the information given in Regulatory Guide 1.174. 

 

RG 1.174 An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on 

Plant-Specific Changes to the Licensing Basis 

This document provides guidance on acceptable methods for utilizing PRA information together with 

established traditional engineering information in the development of RI-IST programs that have 

improved effectiveness regarding the utilization of plant resources while still maintaining acceptable 

levels of quality and safety. It can be used directly to implement a PRA when establishing a new AD&P 

program. 

7.4.3 Summary of Surveillance Frequency Requirements 

Where the adoption of AD&P involves a change of frequency or type of surveillance/test requirements 

this will require at a minimum a qualitative PRA. The PRA will take into account both the impact of a 

new AD&P program on safety margins, as well as the impact of changing the surveillance frequency 

while using the new data/information provided by this program. The licensee may use RG1.200 (NRC, 

2009) to determine the adequacy of the PRA. In situations where a qualitative assessment is not deemed 

adequate by the IDP, a quantitative assessment is required. This includes evaluating quantitatively the 

impacts mentioned above on the CDF and LERF. 

For the introduction of AD&P and a resulting change (reduction) in surveillance frequency the qualitative 

PRA may take into account: 
79 

• The nature of items requiring correction in the existing surveillance program – if the effect of 

these on plant safety is minimal the risk associated with a change in surveillance frequency may 

be acceptable. 

• The frequency at which items requiring corrections have been found in the existing surveillance 

program – if such items have been found only rarely and if they do not have an immediate 

adverse effect on plant safety the risk associated with a change in surveillance frequency may be 

acceptable. 

• The increased information content of surveillance data obtained by AD&P may more than 

compensate for the reduction in frequency - AD&P may yield more high frequency data and may 

permit more accurate comparison with previous data to evaluate trends. 

7.5 Documents Specific to Nuclear Fuel Cycle Facilities 

The primary objective of regulatory guidance for NFCFs is to protect public health and safety from 

hazards including radiological, nuclear criticality, fire and chemical. The regulatory requirements are 

provided in 10 CFR Part 40, “Domestic licensing of source material”, Part 70, “Domestic licensing of 

special nuclear material,” and Part 76, “Certification of gaseous diffusion plants.” Fuel cycle facilities 

perform various functions including conversion, deconversion, enrichment and fuel fabrication. Various 

safeguards are used for the purpose of protection from hazards such as particle removal systems 

(scrubbers), and maintenance of differential pressure to prevent unintentional ventilation from the facility 

to the outside, etc. Existing diagnostics and maintenance practices are aimed at assessing the health of 

these safeguards. 

Material that may affect AD&P is found in NUREG 1520(Rev. 1) Standard Review Plan for the Review 

of a License Application for a Fuel Cycle Facility (NRC, 2010). The target of this discussion is the 

IROFS (Items relied on for safety) for applications regulated under 10 CFR Part 70 Subpart H, 

“Additional Requirements for Certain Licensees Authorized to Possess a Critical Mass of Special Nuclear 

Material.” Maintenance practices may remove an IROFS from service with a required shutdown of the 

associated process or plant. AD&P can reduce or eliminate the need for shutdown of the process or plant 

and may therefore be regarded as desirable. 

NUREG-1718: Standard Review Plan for the Review of an Application for a Mixed Oxide (MOX) Fuel 

Fabrication Facility contains similar material for the specialized case of MOX fuel processing facilities. 

RG 3.56 General guidance for Testing, Operating and Maintaining Emission Control Devices at Uranium 

Mills (NRC, 1986) adapts the Maintenance Rule to the specific environments of Uranium Mills. 

RG 3.74 Guidance for Fuel Cycle Facility Change Processes (NRC, 2012a) specifies which changes 

require prior approval of the NRC. A particularly pertinent one is 

“Use of new processes, technologies, or control systems for which the licensee has no prior 

experience (see 10 CFR 70.72(c)(1)(ii)).” 
80 

Since prior experience with AD&P may not be available, this could be interpreted as guidance for 

obtaining prior NRC approval. 

The regulatory documents pertaining to NFCFs do not add new questions or issues to the regulatory view 

of AD&P. AD&P may potentially benefit NFCFs with many active safety functions. 

 
81 

8 Industry Standards 

Among the benefits of using industry standards are the provision of a framework for inputs from users, 

producers and public interest organizations and the participation of experts from several disciplines in the 

writing and review of these standards. Professional organizations, and particularly the IEEE, have 

mandatory review cycles during which the standard may be affirmed without changes, may be changed to 

respond to advances in technology or new operating experience, or may be withdrawn because there is no 

longer interest in the topic. An important industry standard is IEEE Std. 603, “Standard Criteria for Safety 

Systems in Nucclear Power Generating Stations”. The 1991 version of this standard (IEEE, 1991)is 

invoked in 10CFR50 and is therefore a regulatory document. Rulemaking is in progress which will 

address the newer version of this standard: IEEE 603-2009 (IEEE, 2009). The difference between the two 

versions of IEEE 603 is at this time not significant for AD&P. 

Also, IEEE 603 which is targeted at safety systems is only in very limited situations applicable to AD&P 

that monitors the condition of the safety system. AD&P becomes part of a safety system if it furnishes an 

input to the safety system when it is in use in an operating power plant. Under those conditions it is 

subject to the provisions of IEEE 603. But if it receives data from a safety system or uses data from 

instrumentation that also services a safety system, it can avoid being part of the safety system by 

following established procedures for preventing disturbances in the AD&P from propagating to the safety 

system. The following details amplify these observations. 

 IEEE 603 is discussed below in two sections: 

• The body of the standard which is normative (must be adhered to via 10CFR50). 

• Annex A which is informative . 

Another important standard is IEEE Std. 7-4.3.2 “IEEE Standard Criteria for Digital Computers in Safety 

Systems of Nuclear Power Generating Stations” (IEEE, 2010) an early version of which is referenced in 

IEEE 60315; it is discussed in section 8.3 of this chapter. 

8.1 Body of IEEE Std. 603 

The standard addresses the power, instrumentation and control portions of safety systems; mechanical 

features are brought partially under the standard via interface requirements. The NRC Regulatory Guide 

1.153 “Criteria for Safety Systems” (NRC, 1996) references IEEE Standard 603-1991, as well as criteria 

from the Maintenance Rule that have been discussed in Section 7.2 above. 

Two types of provisions of IEEE 603 are particularly pertinent where AD&P is subject to the standard. 

The provisions also affect instrumentation that is common to AD&P and safety systems in future reactors 

and in the licensing extension of existing ones: 

• Classification 

 

15 IEEE Std. 7-4.3.2-2003 is referenced in the latest version of IEEE 603.
82 

• Design requirements. 

The design requirements are important but because they were discussed in Chapter 7 above they are not 

revisited in this section. 

The classification, that determines which criteria are applied to each equipment type, shown in Figure 8-1 

below, is a matrix of general elements and operational elements of safety systems. The matrix helps to 

establish which criteria apply to AD&P, depending on the equipment they are serving and the particulars 

of their installation. The operational elements are arranged in order of criticality to the safety function, 

with Other Auxiliary Features being the least critical. Examples shown later in the standard specifically 

list “Built-In Test Equipment and Circuitry” as belonging to Sense and Command Features in the Other 

Auxiliary Features group of Operational Elements. Thus it is reasonable to assume that this classification 

will apply to most AD&P components. 

FIGURE 8-1 CLASSIFICATION IN IEEE STD. 603 
83 

The significance of this classification becomes evident in par. 5.12 of the standard: 

5.12 Auxiliary features. 

Auxiliary supporting features shall meet all requirements of this standard. 

Other auxiliary features that perform a function that is not required for the safety systems 

to accomplish the safety functions, and are part of the safety systems by association (e. g., 

not isolated from the safety system) shall be designed to meet those criteria necessary to 

ensure that these components, equipment and systems do not degrade the safety systems 

below an acceptable level. 

8.2 Annex A of IEEE Std. 603 

Annex A of the standard (which is informative and therefore not a regulatory document) reaffirms that 

Built-In Test and other reliability related features are included in the “other auxiliary features”: 

A.5 Other Auxiliary Features 

Included in the design of most safety systems are components and equipment whose primary 

function is to increase the availability or reliability of the safety system without directly 

performing a safety function. These components include, but are not limited to, equipment 

protection devices, built-in test equipment, isolation devices, etc. 

The Annex further describes these features as required to 

meet only those requirements in this standard required to ensure that they do not degrade the 

safety system below an acceptable level. Examples of safety system criteria that such portions 

might not have to meet are operating bypass, maintenance bypass, and bypass indication.

AD&P can introduce common failure modes and become “part of the safety system by association” 

through use of a common sensor or other signal source. A short circuit or electrical noise spike in the 

AD&P equipment could then degrade the safety system. However, this potential threat to safety of the 

nuclear facility can usually be prevented through simple design features, such as the use of qualified 

isolation devices. This issue of connecting safety to non-safety related is not specific to AD&P and is 

known and well regulated. 

Another way in which safety systems may become degraded due to the operation of AD&P equipment is 

by frequent false alarms. The degradation can be caused by placing the plant into an exception state for 

the purpose of investigating the cause of the alarm as well as by causing operators to question the validity 

of alarms. Use of multiple independent signal sources and careful design of the alarm circuitry should 

assure that the adverse effects on the safety system can be kept “below an acceptable level”. 

 
84 

A further but unlikely path for introducing failure modes that are common to the target system and AD&P 

is by use of a common processor that can either physically malfunction or become overloaded and delay 

outputs that are essential to the safety of the plant. NRC guidance discourages the use of related 

processors for non-safety tasks and therefore this path is considered unlikely. Where such use is 

unavoidable all processes are to be treated as safety related. If the extension of the classification as Other 

auxiliary features from the listed example of built-in test equipment to AD&P in general is accepted, and 

if the simple safeguards enumerated above are adopted, IEEE Std. 603 as invoked by 10CFR50 imposes 

no restrictions that inhibit broad use of AD&P. 

IEEE Std. 603 references a number of other standards that are listed below together with the NRC 

guidance documents and their significance to AD&P: 

IEEE Std. 7-4.3.2 Standard Criteria for Digital Computers in Safety Systems of Nuclear Power 

Generating Stations. Covered by RG1.152. This is applicable to AD&P and is discussed below. 

IEEE Std. 338 Standard Criteria for the Periodic Surveillance Testing of Nuclear Power 

Generating Station Safety. Covered by RG1.118. Applies to safety systems – not to AD&P. 

IEEE Std. 497 Standard Criteria for Accident Monitoring Instrumentation for Nuclear Power 

Generating Plants. Covered by RG1.97. Does not apply to AD&P. 

8.3 Provisions of IEEE Std. 7-4.3.2 “Standard Criteria for Digital Computers in 

Safety Systems of Nuclear Power Generating Stations” 

The applicability of this standard is endorsed in RG 1.152. Section 5.5.3 of IEEE Std. 7-4.3.2 covers fault 

detection and self-diagnostics. It requires self-diagnostics if faults can result in undetected partial 

degradation of the system. In these cases the requirement states further that the following features are 

required: 

a) Self-diagnostics during computer system startup. 

b) Periodic self-diagnostics while the computer system is operating. 

c) Self-diagnostic test failure reporting. 

In addition the self-diagnostic functions shall be subject to the same V&V processes as the safety system 

functions (safety function V&V are discussed in 5.3.3 and 5.3.4 of the standard). 

As computers become embedded in safety systems (e.g., emergency core cooling systems) these 

provisions can be interpreted to require self-test, or more properly built-in test, of the entire system and 

provide a basis for a prognostics and health management function. 

Other provisions of this standard pertinent to AD&P are: 

Section 5.5.2 Design for test and calibration: the requirement that test and calibration functions not 

adversely affect the ability of the computer to perform its safety functions can be generalized to 
85 

diagnostic and prognostic functions as well. In particular the standard states that “appropriate bypass of 

one redundant channel is not considered an adverse effect in this context.” This can be interpreted as 

guidance to the implementation of diagnostic and prognostic functions on such a computer or system. 

Section 5.6 Independence: there is a requirement for separation of safety functions from non-safety 

functions so that the latter cannot prevent the former from performing their intended functions. When 

both safety and non-safety functions reside on the same computers this can be achieved in one of two 

ways: 

1. Barrier requirements placed on the safety functions. 

2. If barrier requirements are not implemented the non-safety functions shall be developed and 

implemented according to the standard for safety functions. 

8.4 Applications of Self-Test and Built-In Test 

The requirement for self-test of digital equipment is one of the key provisions of IEEE 7-4.3.2 and it is 

also a requirement that can be expected to carry over into other AD&P. The technical aspects of self-test 

were discussed in Section 3.3 of Part 1 of this report. 

The role of self-test in NPPs was addressed by Royce D. Beacom in a paper “Discerning the Need for 

Fault Detection and Self Diagnostics” in a 2009 meeting of the American Nuclear Society (Beacom, 

2009). It states that self-testing (a) should be applied only where there is a demonstrable benefit and (b) 

must act and furnish information in a manner that can be assimilated by the operators under high stress 

conditions. In support of the first condition it elaborates: 

In most transients or events, well trained operators can quickly detect the abnormal 

situations and execute control functions to shut down or recover the plant through 

automatic responses. However, in some low-probability complex events which fall 

outside the scope of routine operator skill-based training and rule-based operating 

procedures, the operators need to use knowledge-based problem solving techniques to 

evaluate and predict the next possible plant states, to define tasks for control, and to 

recover the plant to a safe state. 

In support of the second condition it cites a decision by the operator of French nuclear plants, EdF, to put 

a moratorium on installation of decision support features because of excessive demands on operator 

training for effective usage of the relevant information. 

The apparent conflict between the established practice of self-test in digital systems and the established 

practice of depending as much as possible on plant instrumentation could be resolved if data on actual 

problems were available. Possible sources of this information are the experience in: 

1. Development of the system that is to be licensed. 

2. Non-safety/protective applications in the plant and in the fleet of the same licensee. 

3. Safety/protective applications in other nuclear facilities. 

4. Monitored facilities outside the nuclear industry. 
86 

The information sought is the incidence rate and manifestations of problems, the means of detection, and 

how the observed problems can be prevented from occurring in the future. A start for the collection of the 

first data item is in Interim Staff Guidance 06, e. g., V&V problem reports (Section D.4.4.2.2), 

Configuration Control problem reports (Section D.4.4.2.3) and Test Incident Reports (Section D.4.4.2.4). 

The incentive for providing information for the second item is that it will facilitate the licensee’s 

application for safety/protective digital functions. The means by which the information is to be collected 

should be acceptable to the licensing organization. The source for the third item is LERs; it may be 

desirable to create a separate category for those pertaining to digital systems. The information for the last 

item will most likely come from the instrumentation provider and a procedure should be outlined for 

benefits for creditable collection and dissemination. 

Until such data become available both BTP 7-17 and the Beacom paper recognize that diagnosis of 

failures in digital components is less related to conditions monitored by the operator than in the analog 

case and needs self-test provisions. The following headings examine factors in failures of digital systems 

that support this statement. 

8.4.1 Lack of correspondence between a failure and its manifestation. 

One of the common causes of failure in a digital device is inversion of a memory bit, i.e., a stored 0 is 

converted into a 1 or vice versa. If the affected memory location is not used there will be no operational 

manifestation. If it is part of a data item, the subsequent read-out of this data item will yield a faulty 

value. But if it is part of an instruction (a component of a computer program) a wide range of 

manifestations is possible, some of which will result in warning messages (such as “array bound 

violation” or “accessing non-existing memory”), other unexpected computer operations or complete 

cessation of the function. One of the causes of bit inversions in semiconductors is background radiation 

and therefore bit inversions are for practical purposes considered inevitable. 

To counter the effects of bit inversions and other memory errors, error detecting codes are employed. A 

typical error correcting method is to append a check bit to every eight data bits, with the check bit 

selected so as to make the sum of the nine bits either odd or even. In even parity, if the sum of the data 

bits is even, the check bit is a 0, if it is odd the check bit is a 1. More sophisticated coding schemes, 

including error detecting and correcting (EDAC), are frequently used. Practically all built-in test 

methodologies include memory checks that are based on checksums. 

8.4.2 Latent Faults 

As AD&P is expected to depend on software, good development, quality control and V&V practices 

(NRC, 2007) are essential for minimizing latent faults that may cause failures in digital systems. 

Failures in digital systems can also be caused by random defects in processors, e.g. in a seldom used 

operations code or register, or in communications ports. The ultimate protection against latent faults and 
87 

defects is defense-in-depth, multiple independent means of performing a safety function or for preventing 

the performance of an unsafe function. 

8.4.3 Developers (particularly for software) come from a different culture than NPP engineers 

and operators. 

In every discipline there are ground-rules that practitioners are expected to adhere to without specific 

instructions. Designers of NPP subsystems can be expected to be familiar with the provisions of 

10CFR50 pertinent to their product but this does not necessarily hold at the level of digital 

instrumentation components or of AD&P. On the other hand, the specialists in digital instrumentation 

come with valuable craft practices that can enhance the safety and certainly reduce the cost of the 

installation. 

Therefore specifications for digital systems, including AD&P, must be drawn very carefully and without 

reliance on “generally understood” provisions. Even references to professional standards may need 

special guidance clauses because the language of the standards may not be clear to the digital specialists. 

8.4.4 Visibility of self-test performance. 

Another item that is unique to digital instrumentation is that the execution of many self-test functions is 

not displayed to the operator. BTP 7-17 requires that the completion of tests for which surveillance credit 

is taken be communicated to the operator (and probably logged). It is also required that self-test functions 

be periodically tested and that bypassing of the functions be announced in the same manner as bypassing 

or inoperable status of other critical functions is announced. 

8.4.5 Conclusions 

Self-test and BIT of digital functions are powerful techniques that can complement other diagnostic 

measures to assure safety of nuclear installations. They are particularly suited for monitoring the 

execution of digital functions that have failure modes that are not intuitively recognized by the operators. 

Operational experience in NPPs or NFCFs is limited and therefore caution should be exercised if self-test 

and BIT are to be employed for diagnosis of events that are routinely handled by operators. 
88 

8.5 Other Industry Standards and Guides 

The following are activities by professional organizations that may impact AD&P in the future. 

IEEE Standards Project 1856, “(Draft) Standard Framework for Prognostics and Health Management of 

Electronic Systems”. This is an ongoing project originated at the University of Maryland concerned with 

the definitions of equipment levels, prognostic methodologies and procedures for their application. It is 

expected that this will facilitate propagation of PHM techniques that have been successful in one 

application/industry to others. 

In a separate effort the Prognostics and Health Management Society provides a communal meeting place 

and publications forum for PHM scientists and engineers. Among its many activities the society is 

engaged in the development and adoption of international standards. 

 

IAEA sponsors safety related standards and although there is not one currently dedicated to AD&P, there 

are several existing guides/standards that are relevant to this subject and are listed in the Bibliography 

section of this document. Additional guidance is anticipated as there are several current international 

collaborations dealing with the technical issues of aging equipment. Once more these offer a field test of 

the newer technologies. 

ASME codes are frequently used as the bases for NRC regulation (both in regulatory requirements such 

as in TS, and in regulatory guides). The codes build on industry-wide acceptance of testing and 

surveillance techniques and as AD&P are adopted by industry they will be codified by ASME. 

 
89 

9 Potential of AD&P to Support Plant Safety 

Of central importance is also the consideration of potential benefits of AD&P in terms of plant safety, 

potential safety-negative impacts of AD&P or of particular methods of implementation of AD&P, and 

potentially unnecessary burden of existing regulations upon the full realization of AD&P. The preceding 

sections have primarily dealt with the prevention of safety-negative impacts and the effects of existing 

regulations. The focus is now shifted to the potential benefits. The technical literature recites benefits for 

industry in general that may be applicable to plant safety, and some of these are described in the first 

portion of this chapter. There is evidence of weaknesses in existing procedures (documented in LERs) 

that could be avoided by introduction of AD&P and these are discussed in section 9.2. The final section 

covers evaluation of the plant safety effects due to the operation of AD&P. 

 

9.1 AD&P Contributions to Plant Safety Assurance 

The general benefits of AD&P are discussed in Part 1 of this report. In this section benefits that pertain to 

regulatory considerations are discussed. Benefits of AD&P that have been reported in the literature 

generated by the organizations listed in Appendix B and that appear particularly applicable to safety of 

NPPs and nuclear fuel cycle facilities include: 

• Faster identification of and response to failures, thus preventing progression to higher levels and 

reducing the impairment of system operation. 

• Avoiding the intrusiveness of intermittent inspections. 

o Less interruption of plant operation. 

o Fewer opportunities for errors during inspection and on resumption of service. 

• Specific identification of the failure type, thus facilitating speedy repair. 

• Aggregation of failure or degradation reporting over a large number of units, thus permitting 

earlier recognition of wearout processes (facilitated by hierarchical coding of failure modes). 

• Potential for reduction of human error as a cause for unidentified and unmitigated damage. 

Information about achieved benefits is available through professional meetings and publications that are 

listed in Appendix B. These benefits may motivate licensees to consider adoption of similar practices in 

nuclear plants. 

Examples of how AD&P can aid plant safety can be gleaned from Licensee Event Reports (LERs). 

Examples from a cursory review of LERs issued during the latter half of 2013 are presented below. 

Several contained express statements that human errors or procedural shortcomings were among the 

causes and these could have been avoided by AD&P. In several other cases not cited here it could be 

surmised that procedures were involved but it was not specifically stated. 
90 

St. Lucie #2, 3 June 2013, Docket 05000389

“...the cause was a human error in evaluating the impact to plant operation caused by the failure of the 

hydrogen analyzer CIV. Contributing causes included: 1) an inadequate procedure, and 2) ineffective 

hydrogen analyzer labeling.” 

AD&P may have made the recovery procedure more effective. 

Susquehanna #1, 7 June 2013, Docket 05000387

“Two causal factors were identified: 1) Less than adequate life cycle management based on delay in the 

project to replace the EHC system with a digital replacement, 2) Less than adequate periodic maintenance 

to prevent oxidation and corrosion in the pressure set potentiometer.” 

AD&P may have detected potentiometer corrosion by measuring electrical noise. 

Wolf Creek Generating Station, LER 2013-008, Docket 05000482

On September 11, 2013 the Class 1E electrical equipment air conditioning unit SGK05A was declared 

nonfunctional causing a plant shutdown. “The cause of the SKG05A failure was an inadequate flush and 

restoration of the system following actions taken to restore SGK05A in May 2013.” 

Wolf Creek Generating Station, LER 2013-010, Docket 05000482

On October 18, 2013 the Class 1E electrical equipment air conditioning unit SGK05A was declared 

nonfunctional causing a plant shutdown. “The cause of the SGK05A failure was a loss of lube oil pressure 

sensing of the pressure switch of the SGK05A compressor. Contaminates in the system caused the loss of 

lube oil pressure sensing to the pressure switch. An inadequate flush and restoration of the system in May 

2013 allowed contaminates to remain in the system.” 

As mentioned in Part 1 of this report, AD&P can provide means for continuous sensing of lubricant 

contamination. 

9.2 Pathways for the Introduction of AD&P 

The effect of regulatory provisions on the introduction of AD&P is highly dependent on the pathway 

through which the new methodologies are being introduced. In the following four routes of introduction 

are being considered 

• As part of a new reactor design. 

• As a replacement of analog instrumentation by digital instrumentation. 

• New into TS application as a result of favorable experience in a non-TS application. 

• New without prior experience. 
91 

A licensee applying for a new plant will have solicited proposals from eligible suppliers and selected one 

for which favorable construction and operational data are available. The licensee and the selected supplier 

will be aware of applicable regulations and will discuss areas of potential problems, including those 

affecting AD&P, with the licensing body. The application processing and construction intervals provide 

an opportunity for accommodation in the regulations as well as for changes in the AD&P design or area 

of applicability. Considerations for the other pathways are discussed below. 

9.2.1 Replacement of Analog by Digital Instrumentation 

Replacement parts for existing analog instrumentation may be difficult to obtain and plant operators are 

then motivated to investigate digital instrumentation. As mentioned in the first part of this report, one can 

anticipate that AD&P will be part of the trend of increasing digital instrumentation both because there are 

current AD&P implementations for digital systems and because much of AD&P relies on digital 

capabilities. 

An example of this situation that took several years to resolve resulted in granting of Amendments to 

Renewed Facility Operating Lincenses to Oconee Units 1, 2 and 3 for digital upgrades (NRC, 2010a). The 

amendments credited the self-test features of the digital instrumentation with several reductions in 

surveillance requirements, such as only verifying setpoints against the previously required channel 

functional test (SR 3.3.5.2/3.3.5.3). In another instance the requirement for a channel functional test every 

92 days (without digital upgrade) is relaxed to actuation of interposing relays every 92 days and complete 

functional test every 18 months after the digtial upgrade is complete (SR 3.3.7.1/3.3.7.2). 

The Digital Instrumentation and Controls Task Working Group #6 developed an Interim Staff Guidance 

(ISG) which describes the licensing process that may be used in the review of license amendment requests 

associated with digital I&C system modifications in operating plants originally licensed under Part 50 

(NRC, 2011b). Section B of this ISG provides an overview of the scope of the review and points to all the 

regulation and guidance documents relevant to the licensing process of digital instrumentation including 

both hardware and software. 

The ISG refers to IEEE 603 and where the digital system includes a processor it will be subject to IEEE 

Std. 7-4.3.2, in particular the self-test provisions that have been mentioned in Section 8.4. Adherence to 

these provisions may permit relaxation of the requirements for fixed interval testing of the processor. 

A potentially adverse condition arises if processing of the digital instrument output is assigned to a central 

computer that also handles other safety critical data. The ISG refers to this problem and builds on existing 

NRC regulations and guidance (independence, defense in depth) to prevent the introduction of common 

mode failures via this pathway. 

 

9.2.2 Introduction into TS Application after Use in Non-TS Application 

AD&P is applicable to many plant features and procedures that are not safety related and are not covered 

by the plant TS. Keeping appropriate records on the operation, failure frequency and failure modes of this 
92 

equipment can be an important step in facilitating regulatory approval for using this AD&P in safety 

related applications. 

It is therefore recommended that standard procedures be developed for evaluation of AD&P equipment in 

non-TS applications that will form a basis for subsequent approval for use in safety-related applications. 

At the end of this chapter general guidelines for evaluation of AD&P equipment are presented that are 

partly derived from those generated for the introduction of remote monitoring. These guidelines can form 

the template for such a standard. 

9.2.3 Introduction into TS Application without Prior Experience 

Where a proposed new safety-related AD&P installation is intended to replace a conventional practice it 

may be desirable to operate the two in parallel for an initial observation period. Although this cannot be 

depended on to detect rare events it can detect conditions that can be components or precursors of rare 

events that occur more frequently. The parallel operation should be structured to 

• Minimize changes to the existing procedure; all changes that need to be made should be 

documented and approved. 

• Permit operation of the AD&P independent of the existing procedure; document where this is not 

possible. 

• Obtain a complete inventory of plant equipment and state at the start of parallel operation. 

• Obtain an operating and failure history of the target equipment for at least one year prior to the 

start of the parallel operation. 

The purpose of the last two bullets is to facilitate trouble shooting and to aid in the evaluation of the 

AD&P. From the regulatory point of view the parallel operation serves to determine if 

• AD&P detects at least all instances of non-conforming operation of the target equipment that are 

detected by the existing procedures. 

• AD&P alerts to no more events that turn out to be nuisance alarms than the existing procedures 

(alarms irrelevant to the safety of the plant and of the target equipment). 

• Provides safety and operational benefits that compensate for the additional complexity and 

maintenance obligations due to the installation of AD&P. 

If the findings in all instances are favorable a reduction in the frequency16 or intensity of the conventional 

procedures may be agreed to for a subsequent period of parallel operation with the aim of eventually 

relying exclusively on AD&P. If there are negative findings the operation of the AD&P may be changed 

to allow for an additional period of parallel operation or it may be abandoned. 

 

16 According to the regulatory procedures outlined in section 2.3 of this report 
93 

9.3 Evaluation of the Effects of AD&P Operation on Plant Safety 

The evaluation of AD&P operation is intended to determine whether changes in procedures (or 

discontinuance) are necessary and also to form the basis of forward looking changes in regulations for 

further AD&P installations. Although this is a post-introduction effort it needs to be planned for as part of 

the introduction activities. The evaluation should address the following key issues: 

• Did the adoption of AD&P achieve the intended benefits? 

o Reduction of maintenance effort. 

o Increase in plant availability. 

• Did the adoption of AD&P, in spite of the analysis conducted prior to introduction, produce side 

effects that may impair the safety of plant operation? 

o In routine reactor operation. 

o Under design basis events. 

o By reducing the availability or effectiveness of mitigation provisions. 

The plant operator is motivated and equipped to perform the evaluation of the first solid bullet but 

regulators will be concerned with the second solid bullet. Because the last two conditions are not likely to 

be observed the following describes some methodologies that can be employed for predicting the 

response of AD&P to rare conditions. The underlying assumption is that the response of AD&P to design 

basis events has been examined by simulating these conditions, and that the findings need to be confirmed 

by examining the response to more routinely observed status changes. 

A dedicated effort can be undertaken for each installation that tracks the response to each system status 

change, e. g., increase or decrease in power output, outage of one of several redundant channels, testing of 

a Diesel generator. Baseline data for this will have to be generated for a period prior to AD&P activation 

and some special instrumentation may be required. For new NPPs the baseline data can be generated by 

simulation. The internal response of the AD&P equipment to these plant status changes can be evaluated 

to determine 

• Whether the response to the status change is appropriate. 

• Whether the response can be extrapolated to a design basis condition, and if so, whether it will 

alarm at the appropriate level. 

• Whether the response to the status change came close to resulting in a false alarm. 

In addition to a general review of the performance of AD&P, all LERs (Licensee Event Reports) for a 

given plant in which AD&P had been installed for a defined period (e. g., one or two years) can be 

examined to determine whether there was a proper AD&P response, and whether the event was due to the 

AD&P installation or was aggravated by it. This approach may be limited by some of the following 

factors: 

• Detection of the connection to the AD&P installation is highly dependent on the expertise of the 

investigator. 

• Even with expertise available it may not be possible to conclude that regulatory action regarding 

the AD&P installation is warranted. 
94 

• This approach cannot identify all events that were prevented due to the installation of AD&P 

(because the incident was no longer a reportable event). 

An alternative is a statistical approach to evaluate the effects of AD&P on plant safety. This can 

inherently detect positive as well as negative trends but it requires (a) establishing an index for plant 

safety and (b) defining a procedure for measurement of that index. Neither one of these is a trivial effort. 

One candidate for the index is the number of LERs with reporting requirements code 50.73 which 

includes most unexpected operational events. During the first half of 2013 there were approximately 50 

events with that reporting code. This equates to about one report per year for each operating power 

reactor. Where an AD&P installation is made in only a single reactor this will require an unreasonably 

long time for detection of a trend. But if a given installation includes five or more plants, significant 

trends should be detectable within a year and more subtle ones over a two to three year period. 

A combination of the detailed analysis of individual reports and the statistical approach is possible, and 

this may be an effective initial step. 

 
95 

10 Conclusions 

Part 1 of this report has shown that diagnostic and prognostic procedures depend on instrumentation for 

knowledge of the state of the plant and that therefore the dependability of instrumentation is of concern in 

the application of these techniques. Measures for achieving the required dependability were presented. 

Among AD&P techniques an important distinction is between model-based and data-driven procedures. 

In the former replacement or repair decisions are based on a mathematical model. In the latter case 

replacement or repair decisions are based on statistical analysis of failure effects, usually as seen in 

operating data. Hybrid techniques that combine elements of the model-based and data-driven approaches 

are also frequently encountered. In all cases parameters are determined by examination of test or 

operational data and the failure criterion (the threshold below which an item is declared failed) is selected 

empirically. 

While many of the AD&P techniques described in the body of Part 1 are still in the research or early 

evaluation stages, a number are being successfully employed in industry and transportation, notably 

• Self-test of digital systems. 

• S.M.A.R.T surveillance of computer disc drives. 

• Analysis of the health of rotating components by vibration signatures and contamination of 

lubricants. 

• Detection of insulation degradation of electrical conductors. 

In NPPs and NFCF a distinction must be made between applications subject to NRC license and those 

made at the discretion of operators. In the latter category there are several of the above techniques or their 

equivalents. In the licensed category only self-test could be currently found. Among techniques still in 

the evaluation pipeline, one that is particularly suitable for NPPs is the analysis of the high frequency 

component of neutron noise that is described in Section 4.5. 

Part 2 of the report examines regulatory issues affecting the use of AD&P. There are no outright 

prescriptions or prohibitions in 10CFR or documents referenced there except the requirement for self-test 

in digital systems that is contained in IEEE Std. 7-4.3.2. This standard, although not incorporated by 

reference in 10CFR50, provides guidance in the installation of digital equipment. Restrictions affecting 

some types of AD&P may be encountered in guidance documents and in Technical Specifications. An 

example is the requirement that the connection of AD&P to the target equipment must not interfere with 

the operation of the latter under any conditions. Reasonable means for complying with these restrictions 

are usually available. 

Avenues for the introduction of AD&P include 

• Migration from non-licensed to licensed applications. 

• Extension of the scope of self-test into full-fledged AD&P. 

• Acquisition of instrumentation or equipment that includes AD&P. 

• Licensing of new plants that include AD&P. 
96 

Measures to protect against unsafe conditions caused by introduction of AD&P include parallel operation 

of established surveillance measures with AD&P for an introductory period and monitoring AD&P for 

early warnings of failures to alarm or excessive false alarms. With such safeguards AD&P can be 

expected to make a positive contribution to the safety and operation of NPPs and NFCFs. Examples of 

how existing capabilities could have been deployed to prevent shut-downs of NPPs have been presented. 
97 

Appendix A Taxonomies and Their Application 

An objective of this document is to provide information on the methods and technologies utilized in 

AD&P. This objective can benefit from a well-organized taxonomy of AD&P methods which is helpful 

for systematically illuminating both the challenges in this field and their possible solutions. Two 

taxonomies, presented in subsection A.1, build on the categorization presented in subsection 4.1 

according to model-based, data-driven and hybrid techniques. Section A.2 includes descriptions of 

specific methods categorized according to this division. Each method is described under a separate title 

identified by a letter (M, H or D corresponding to model-based, hybrid, data-driven accordingly) and a 

sequential number. Each description includes a listing of the kinds of equipment it may target; the 

technology involved; failure modes it can be applied to; a discussion of the implementation and a maturity 

level. 

In order to facilitate lookup from Chapter 3, all the entries in section A.2 are grouped according to the 

techniques identified in subgroups in that chapter. 

 

A.1 Taxonomy of Technologies and Methods Used in Advanced Diagnostics and 

Prognostics 

In the following, two taxonomy structures for AD&P techniques are represented: a research taxonomy, 

based on the classifications just described, and a review taxonomy, based on the types of equipment for 

which they are used. Both taxonomies are populated in accordance with the literature described in this 

appendix. The literature includes papers from recent Prognostics & Health Management (PHM) 

conferences or from PHM sessions of other conferences sponsored by the Institute of Electrical and 

Electronic Engineers (IEEE) or the American Nuclear Society (ANS). Other referenced publications are 

authored by experts consulted as part of this study (see Acknowledgment section of the Introduction) or 

recommended by these experts. No claim is made that these methodologies represent a comprehensive 

survey but they are believed to be representative for application to equipment/components used in current 

or future NPPs or NFCFs. 

References that specifically address use of AD&P in NPPs are mainly in the mechanical realm (e.g. 

passive structures and cables, and active components such as pumps and turbines) with some in the 

electromechanical domain (e.g. generators and transformers). However, the general AD&P literature 

includes many more methods that are focused on electronic equipment. This state reflects the needs of 

other domains (e.g. aerospace control systems) but diagnostics and prognostics for electronic systems 

may be proposed for application in advanced nuclear power plants. To prepare for this future, the 

taxonomies detailed below include many methods applied to electronic parts, assemblies and components. 

 

 
98 

A.1.1 Research Taxonomy 

The top headers of the research taxonomy shown in Table A-1 are based on the model vs. data 

categorization and the rows indicate the application according to system complexity beginning with 

Primitives (pipe, transistor), progressing through Assemblies (small ICs, control rods), and Components 

(Hard drive, motor, power supply), to Systems (cooling system, plant instrumentation). The goal of the 

research taxonomy is to align the three categories with the complexity of the equipment. In light of the 

discussions in section 4.1-.4.4 the reader will note that model-based applications will naturally dominate 

in simple systems while the data-driven applications will dominate in the more complex, higher-up in the 

system hierarchy, equipment. The research taxonomy validates this notion. 

TABLE A-1 RESEARCH TAXONOMY. 

Target Element No. of Examples and References 

Data-driven Hybrid Model-Based 

Primitive (e.g. 

pipe, transistor) 

13 

See Entries: 

D-1 - D-13 

3 

See Entries: 

H-1 – H-3 

1 

See Entries: 

M-1 

Assembly (e.g. 

Small IC, control 

rod) 

7 

See Entries: 

D-1-D-5; D-14-D-15 

D-18 

4 

See Entries: 

H-4 – H-7 

6 

See Entries: 

M-2 – M-7 

Component (e.g. 

Hard drive, 

motor, power 

supply) 

8 

See Entries: 

D-1; D-14; D-16; D-17; 

 D-19 – D-23 

5 

See Entries: 

H-8 – H-12

3 

See Entries: 

M-8 – M-10 

System (e.g. 

Cooling System, 

plant 

instrumentation) 

6 

See Entries: 

D-14; D-20; 

D-24 – D-27 

1 

See Entries: 

H-13 

0 

The bold faced numbers in each cell of the table indicate the number of references found, and the lower 

entries in each cell are the key to these references in the following subsection (e.g. H-4 is the 4th hybrid 

method). It is clearly seen that there are many more data-driven entries than model-based ones which is 

not too surprising since extensive physics of failure investigations are required to establish a model-based 

application. System level entries are predominantly in the data-driven column with none at all under 

model-based column. Failures at the system level can be due to many failure modes and it would be 

prohibitively expensive to develop a physics of failure model for each of them. Even the model-based 

entries at the component level are restricted to one or a few predominant failure modes. Discussions with 

active members of the scientific community17 researching and developing the field of advanced 

 

17 In the development of this document, the authors held extensive interviews with leaders of this 

community from national laboratories (Idaho National Lab, Pacific North National Lab) NASA 
99 

prognostics indicate that the middle column of applications relying on the hybrid approach will most 

likely be the one most rapidly populated in the near future. The scarceness of sufficient data for 

effectively applying data-driven methods on the one-hand, and the intractability of physical models of 

complex equipment for applying model-based methods on the other, leads to this hybrid approach. 

 

A.1.2 Review Taxonomy 

The review taxonomy is aimed at reviewers of AD&P applications for specific equipment types. It is 

proposed as an alternative classification in which the row headings are the type of covered equipment and 

the columns are the Data/Hybrid/Model methods previously discussed. The goal of the review taxonomy 

is to align this classification with system type (mechanical, electromechanical and electronic). The 

discussion of the three categories did not highlight types of systems that are more likely to be monitored 

by data-driven or model-based applications. However, there is a correlation between these categories and 

the type of equipment and this correlation may be helpful for NRC staff when reviewing license 

applications: The review taxonomy will enable the reviewer to directly look up any other techniques for 

the same type of system, in any of the three categories. It will offer a knowledge base of what is in use 

and what is in development for this equipment under application 

The review taxonomy presented in Table A-2 shows that to–date the preferred approach for mechanical 

elements is model-based and for electronic elements it is data-driven. This can be explained by the 

difference in the number of failure modes: one (or few) for mechanical elements and many for electronic 

elements. When there are more than a few failure modes the effort required to develop and verify detailed 

damage models becomes large. A few references appear in more than one column of Table A-2 because 

they are applicable to several element types. 

Each of the cells in Table A-2 can be further expanded into the complexity categories used in Table A-1. 

Thus for the electronic hybrid cell the expansion into complexity categories is shown in Table A-3. This 

expansion capability is primarily mentioned as a possibility for future updates when a larger number of 

entries may have to be covered. 

 

 

(Ames Prognostics Center of Excellence) and academia (University of Tennessee, Idaho State 

University). Discussions were also conducted with leaders of the industrial R&D and operations 

community through such organizations as IAEA, EPRI and INPO 
100 

TABLE A-2 REVIEW TAXONOMY

Element Type Number of Examples and References 

 Data-driven Hybrid Model-based 

Mechanical: Passive 

Piping, reactor vessels, coolant loops, core 

internals, fuel channels, rod housing 

0 0 2 

See Entries 

M-1; M-6 

Mechanical: Active 

Hydraulics, valves, steam/vacuum, control rod 

driving systems, rotating machines, diesel engines, 

bearings, cooling fans, turbines 

0 1 

See Entries 

H-4 

5 

See Entries 

M-2-M-4; 

M-6&7 

Electro-mechanical 

Motors, sensors, actuators, generators, engines, 

machines, electric power systems 

2 

See Entries 

D-21; D-23 

3 

See Entries 

H-9 -H-12 

4 

See Entries 

M-5; M-8-

10 

Electronic 

Primitives (resistors, capacitors, transistors, 

diodes), CMOS single dies, assemblies, memory, 

hard drives, cards (e.g. avionics, communication 

etc.), transformers, solder joints, cables, 

connectors, sensors 

26 

See Entries 

D-1 - D-20; 

D-22 -D-27 

8 

See Entries 

H-1-H-3; 

H-5 - H-8; 

H - 13 

0 

TABLE A-3 EXPANSION OF THE HYBRID ANALYSIS FOR ELECTRONICS

Complexity References 

Primitive 3 

H-1 – H-3 

Assembly 3 

H-5 – H-7 

Component 1 

H-8 

System 1 

H-13 

Perusal of this Appendix will show that several component types are entered multiple times. Prominent 

among these are bearings which have six explicit entries and several more in which they are covered as 

part of a higher element (generator, etc.). This is not too surprising since bearings are well known to be a 

source of failure. The entries differ in the type of sensor (temperature, vibration, acceleration, oil quality, 

and combinations of these) and in the algorithm used to compute RUL. Other multiple entry subjects 

include solder joints (ubiquitous in electronics and amenable to failure model construction) and hot carrier 
101 

electrons (as a cause of degradation in electronics). All of these are potentially important for NPPs and 

NFCFs. 

The taxonomy, in its two variants, highlights how the methodology categorization aligns with other 

system characteristics such as complexity and type. The categorization of data and model-based 

methodologies is accepted throughout the field of AD&P, although the exact terminology may vary. This 

categorization is indicative of the information one has on the system and dictates the tools one can use to 

derive diagnostic and prognostic evidence. However, students of this field and possible reviewers will 

most probably be exposed to AD&P techniques from the equipment side. The research taxonomy is 

useful for understanding how the methodologies can be used in real applications. The review taxonomy is 

useful for understanding how a particular application fits into the context of the entire field. 

A.2 Applications: Annotated bibliography of taxonomy entries 

The following sections provide descriptions of specific AD&P methods and technologies. The entries are 

labeled according to the taxonomy structure: D denoting data-driven (D-1 through D-27); H denoting 

hybrid (H-1 through H-13); and M denoting model-based (M-1 through M-10). The descriptions are 

intended to provide limited background on the application and technology; the equipment it can be used 

for; and the maturity of the technology and its usage. The entries are grouped under the titles introduced 

in Chapter 3. 

A.2.1 Canaries 

D-1: Fuses (Vichare & Pecht, 2006) 

Target

Electronic assemblies and subassemblies 

Technology

Fuse opening precedes/preempts assembly/component failure and indicates failure inducing conditions. 

Implementation

Diagnostics: non-specific indication of an abnormal condition 

Prognostics: none 

Maturity: mature technology 

D-2: Prognostic Canary CMOS Cells (Mishra, et al., 2002), (Ridgetop Group, 2012) 

Target

CMOS single dies, small assemblies 
102 

Technology

Canary cells are sensitive to the same stressors as the prime equipment (voltage, current, temperature, 

humidity, radiation, vibration) and experience the same stress level but are programmed to fail earlier due 

to scaling which is achieved via increased voltage, current or both. Prognostic distance can be 

programmed to allow time to replace. 

Failure modes and mechanisms

Electrostatics discharge (EDC), hot carriers, metal migration, dielectric breakdown, radiation effects. 

Implementation

Diagnostics: none. 

Prognostics: preempts failure and can provide estimate of RUL 

Maturity: R&D and testing 

D-3: Prognostic Canary solder joint Cells (Gu, 2009), (Gu, et al., 2007) 

Target

Electronic primitives and assemblies, single dies 

Technology

Canary cells are sensitive to the same stressors as the prime equipment (voltage, current, temperature, 

humidity, radiation, vibration) and experience the same stressor level. 

Failure modes and mechanisms

Low cycle fatigue of solder joints 

Implementation

Diagnostics: none. 

Prognostics: preempts failure and can provide estimate of RUL 

Maturity: prototype and commercial R&D 

D-4: Prognostic Canary Corrosion Cells (Mishra, et al., 2002) 

Target

Electronic primitives and assemblies 

Technology

Canary cells are sensitive to the same corrosion stressors and experience the same stress level. 

Impedance spectroscopy (phase and magnitude / frequency) are used to assess damage level. 

Failure modes and mechanisms

Corrosion 
103 

Implementation

Diagnostics: none. 

Prognostics: preempts failure and can provide estimate of RUL 

Maturity: R&D 

A.2.2 Performance Monitoring Techniques

D-5: Built In Current Sensors (BICS) (Smith & Campbell, 2000), (Pecuh, et al., 1999), (Xue & 

Walker, 2004) 

Target

CMOS primitives and single die ICs, small assemblies 

Technology

Supply current is monitored via BICS. Excess current used to identify faults 

Failure modes and mechanisms

Bridging defects, open defects, parasitic defects 

Implementation

Diagnostics: threshold and signatures 

Prognostics: Trend and signatures 

Maturity: R&D and test 

D-6: MOSFET Performance Signatures (Kalgren, et al., 2007) 

Target

MOSFET devices 

Technology

Feature extraction from multiple performance measurements provides degradation signatures 

Failure modes and mechanisms

Fractures and void spaces; gate-oxide breakdown; contact migration; semiconductors degradation (the 

failure mechanisms are thermal cycling, electromigration, hot carrier effects and time dependent dielectric 

breakdown) 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 
104 

D-7: Marginal Checking: Cables and Connectors (Born & Boenning, 1989) 

Target

Cables and connectors 

Technology

Tracking of small changes to critical parameters: 

• Impedance changes 

• Physical damage 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-8: Marginal Checking: Oscillators (Born & Boenning, 1989) 

Target

Voltage controlled oscillators 

Technology

Tracking of small changes to critical parameters: 

• Output frequency 

• Power loss 

• Efficiency 

• Phase distortion 

• Noise 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-9: Marginal Checking: FET (Vichare & Pecht, 2006) 

Target

Field Effect Transistors 

Technology

Tracking of small changes to critical parameters: 

• Gate leakage current 

• Drain source leakage current 

• Drain source resistance 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 
105 

Maturity: Test and R&D stage 

D-10: Marginal Checking: Ceramic Chip Capacitors (Vichare & Pecht, 2006) 

Target

Ceramic chip capacitors 

Technology

Tracking of small changes to critical parameters: 

• Leakage current 

• Resistance 

• Dissipation factor 

• RF noise 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-11: Marginal Checking: Diodes (Vichare & Pecht, 2006) 

Target

General purpose diodes 

Technology

Tracking of small changes to critical parameters: 

• Reverse leakage current 

• Forward voltage drop 

• Thermal resistance 

• Power dissipation 

• RF noise 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-12: Marginal Checking: Electrolytic capacitors (Vichare & Pecht, 2006) 

Target

Electrolytic capacitors 

Technology

Tracking of small changes to critical parameters: 

• Leakage current/resistance 

• Dissipation factor 
106 

• RF noise 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-13: Marginal Checking: Amplifiers (Vichare & Pecht, 2006) 

Target

RF power amplifiers 

Technology

Tracking of small changes to critical parameters: 

• Voltage Standing Wave Ratio (VSWR) 

• Power dissipation 

• Leakage current 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-15: Load Cycle Counting (Vichare, et al., 2006), (Vichare, et al., 2007) 

Target

Electronic assemblies 

Technology

Load cycle counting is used to generate histograms for the distributions of load cycle features: cyclic 

range, cyclic mean load, rate of change of load, and dwell time. Diagnostic and prognostic models are 

based on these features 

Implementation

Implemented with a solder joint damage model and compared with actual resistance. 

Diagnostics: Thresholds are used for system level diagnostics 

Prognostics: Based on damage model 

Maturity: R&D 
107 

D-16: Marginal Checking: CMOS IC (Vichare & Pecht, 2006), (Born & Boenning, 1989) 

Target

CMOS ICs 

Technology

Tracking of small changes to critical parameters: 

• Supply leakage current 

• Supply current variation 

• Operating signature 

• Current noise 

• Logic level variations 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: Test and R&D stage 

D-18: Pulse Width Modulation (Khanniche & Mamat-Ibrahim, 2004) 

Target

Voltage Source Inverters (VSI) 

Technology

Monitoring of current pulse width modulation, and applying discrete wavelet transforms (DWT) and 

fuzzy logic used to identify faults. 

Failure modes and mechanisms

Transistor open circuit faults and intermittent misfiring faults 

Implementation

Diagnostics: applied to identify faulty devices in large integrated systems. 

Maturity: R&D stage 

D-19: Resistance Spectroscopy (Pradeep, et al., 2010), (Ridgetop Group, 2012) 

Target

Ball grid Arrays (BGA); all connectors with multiple solder joints 

Technology

Track resistance of edge connectors experiencing highest stress. Pre-failure signatures analyzed to 

estimate future state. Applicable to functional systems where corner interconnects in area-array packages 

are often redundant 
108 

Failure modes and mechanisms

Failures due to vibrations/mechanical stress. 

Implementation

Suitable for prognostics. 

Maturity: R&D as well as commercial tool. 

D-20: Error Codes and Signals (Hecht, 2006) 

Target

Digital memories and systems 

Technology

The frequency of triggering error detecting codes for memories and communication increases as the 

devices age. Similarly the magnitude of the error signal in closed loop control systems increases in 

magnitude and changes in frequency spectrum as a function of damage processes to which the system is 

exposed. The rate of error triggers and the RMS magnitude of error signals furnish clues on component or 

system deterioration. 

Failure modes and mechanisms 

Threshold changes in semiconductors, gain reduction and mechanical degradation in closed loop systems 

Implementation

Diagnostics: detection of error rates or error signal values exceeding baseline 

Prognostics: time trending of excess error rates or error signal 

Maturity: Test and R&D stage; see D-22 for use in disk memory errors 

D-21: Pulse Width Modulation (Power Devices) (Zidani, et al., 2008) 

Target

Motors: Voltage-Fed PWM Inverter Induction Motors 

Technology

The proposed fuzzy technique requires the measurement of the output inverter currents to detect 

intermittent loss of firing pulses in the inverter power switches. 

Failure modes and mechanisms

Misfiring (intermittent loss of firing pulses) in the switches of a PWMVSI induction motor drive 

Implementation

Diagnostics: applied to identify faults in the inverter. 

Maturity: R&D stage 
109 

D-22: Self-Monitoring Analysis and Reporting Technology (SMART) (ATA, 2008), 

(PCTechGuide, Undated), (PCGuide, 2001) 

Target

Hard drives 

Technology

Precursor monitoring via an interface between the computer startup program (BIOS) and hard disk drive. 

Measurements include variations on head flight height, error counts, variations in spinning time, 

temperature and data transfer rates among others. 

Failure modes and mechanisms

Head damage: cracks, contamination, bad connections to electronics; motor/bearing: motor/bearing 

failure or wear; excessive run-out; no spin; electronic module failures: bad connections, chip failures; 

Media: scratches, defects, bad servo, ECC corrections 

Implementation

Diagnostic: error values 

Prognostic: trending of error values 

Maturity: Mature technology for diagnostics 

H-1: Performance Measurement (Circuit As a Sensor) (Nanduri, et al., 2007) 

Target

CMOS RF devices 

Technology

C-V and I-V characteristics of transistor change prior to actual breakdown and show up as signatures in 

device performance parameters such as gain, trans-conductance, series resistance and threshold voltage. 

These translate into analog circuit performance showing up in phase and frequency response, linearity, 

gain and impedance. Gate-source capacitance used for damage accumulation model. 

Failure modes and mechanisms

Time Dependent Dielectric breakdown (TDDB) due to hot carriers 

Implementation

Prognostics: Impact Technologies study in lab 

Maturity: R&D and test 
110 

H-9: Model-based AD&P for Electro-Mechanical Actuators (Byington, et al., 2004), 

(Byington, et al., 2004) 

Target

Electromechanical actuators (EMA), applicable also for other actuators: mechanical, hydraulic and 

electro-hydraulic, 

Technology

Model simulates the actuator response to various command signals (e.g. flight control command signals); 

optimization algorithms estimate model parameters which are compared to the base (healthy) model. The 

residuals provide both diagnostic and prognostic information. (Parameters are physical such as frictiondamping coefficient, local gear stiffness, torque constant, motor temperature) 

As the technology is based on a simulated model of the performance of the actuator and its parts it can 

differentiate between different failure modes and provide information on the most advanced failure mode 

as well as the fastest progressing failure mode. 

Failure modes and mechanisms:

Model-based prognostics can theoretically cover all failure modes (controller: electronic filter failure, loss 

of power, switch/connector failure, sensor failure; motor: bearing seizure, shaft misalign/fracture, 

windings open/short; gearbox: fatigue cracking, gear stripping; Acme configuration: cracked nut, cracked 

screw, nut seizes to screw; leadscrew/ballscrew: bearing seizure, jammed leadscrew). Failure modes 

implemented: 

• Gear slipping 

• Bearing seizure 

• Motor failure 

Implementation

Diagnostics & Prognostics: Implemented by Impact Technologies. 

Maturity: commercial R&D & prototyping 

H-10: Motor Current Signature Analysis (MCSA) / Motor Power Signature Analysis (MPSA) 

(IAEA, 2007) (IAEA, 2008), (Kryter & Haynes, 1989), (Strangas, et al., 2008) 

Target

Electromechanical equipment: rotating machines driven by motors 

Technology

Motor Current Signature Analysis (MCSA) / Motor Power Signature Analysis (MPSA) measure changes 

in mechanical loads which lead to changes in the current through the motor. Power analysis is sensitive to 

operational changes. 

Failure modes and mechanisms

• Wear on: bearing, packing, coupling and gears 

• Valve stem taper 

• Stem nut wear 
111 

• Degraded voltage 

• Degraded valve stem lubrication 

• Worm gear tooth wear 

• Obstruction in the valve seat area 

• Motor pinion disengagement 

• Degraded worm and work gear lubrication 

• Changes in stem packing adjustment 

• Improper torque switch settings 

Implementation

Trending on monthly/quarterly basis offers qualitative prediction about machine deterioration. 

Long term trending can diagnose improper operating conditions and improper maintenance (improper 

installation of seals packing gears bearings; inaccurate shaft alignment or rotor balancing). Provides an 

additional input to vibration analysis. It is simple to implement for online monitoring. 

Maturity: In use 

H-11: Electrical Signal Analysis of Electromechanical Generators (Kumar, et al., 2012) 

Target

Electromechanical generators (wind power systems) 

Technology

Analyze and trend electric output signals: frequency and phase. 

Failure modes and mechanisms

• Wear on: bearing, packing, coupling and gears 

Implementation

Trending can provide prognostic of the failing gear. 

Maturity: R&D 

H-13: Quality of Service. (Brown, et al., 2005), (Nanduri, et al., 2007) 

Target

Communication equipment, possibly others 

Technology

Estimating RUL based on combination of failure models and features based on quality of performance: 

GPS based on NMEA 0183 Protocol; Quadrature Amplitude Modulation (QAM) communication system 

CMOS failure models are the basis for accelerated fault-to-failure testing. Prognostics makes use of 

universal system level features: for GPS these are reported using the NMEA 0183 protocol, for the 

QAMCS these are based on Cyclic Redundancy Check (CRC). These require a simple RS232 

connection between the communication device (GPS) and computer which analyzes the data. 

 
112 

Failure modes and mechanisms

Hot carriers lead to time dependent breakdown of dielectric and degradation of CMOS devices. This leads 

to system level functional failure (rather than operational) 

Implementation

Implemented as experimental prognostic by Impact Technologies 

Maturity: R&D and test 

M-6: Physics Based Prognostics for Pneumatic Valves (Daigle & Goeble, 2011), (Daigle & 

Goebel, 2009) 

Target

Pneumatic valves 

Technology

Physical model applied to pneumatic valve. The physical parameters measured include opening degree, 

pressures, friction coefficient, response time. The model calculates an End of Life (EOL) probability 

distribution that can be translated to RUL 

Implementation

Diagnostics: The measurements identify the damage and its location 

Prognostics: Based on historical data a RUL is generated 

Maturity: Currently implemented via simulation on historical (space shuttle) data. R&D stage. 

M-8: Electrical Surge Analysis (IAEA, 2007) 

Target

Motors: stator windings, (on both induction and synchronous machines), DC armatures, synchronous field 

poles 

Technology Electrical surge comparison: Applying a transient surge at high frequency to two separate but 

equal parts of winding and comparison of the two, will indicate short circuit or open coil. 

Failure modes and mechanisms

Turn-to-turn and phase-to-phase insulation deterioration; open circuit or reversal in connections in coils 

Implementation

Trending can offer tracking of insulation deterioration and prediction on time to failure. Attempts to 

correlate conduction voltage to RUL are complicated by the differences in stresses on insulation during 

motor operation – the ability to operate with deteriorated insulation depends on load, start cycle 

frequency. 

Trending can identify improper motor repair practices, as well as improper operating conditions (beyond 

design specifications). 

Expensive – both instrumentation and requirement for highly trained/experienced operators. 
113 

Maturity: Electrical surge analysis is a mature quality control test method. The development of portable 

test instruments now allows this testing to be conducted on a troubleshooting and condition based 

maintenance basis. 

A.2.3 Self-Test 

D-14: Built-In Test: BIT (Pecht, et al., 2001) (Gao & Suryavanshi, 2002), (Drees & Young, 

2004), (Motorola, 2002), (Johnson, 1996) 

Target: 

Large circuits, modules (avionics), systems (large scale systems, avionics, multi-chip, power supplies, 

remotely controlled systems). Logical devices: Cache, Bus, ASIC, ECC RAM, Serial EPROM, FLASH, 

NVRAM, Real time clocks; 

Technology:

Built-in test (HW+SW): internal tests meant to detect hardware and software faults. These include: Error 

detection and correction circuits; totally self-checking circuits; self-verification circuits. Internal tests can 

include: register stuck-at conditions, register manipulations, device set-up instructions. 

Roughly BIT are categorized as: 

• Interruptive BIT (I-BIT) ; 

• Continuous BIT (C-BIT) ; 

• Periodic BIT (P-BIT). 

Have relatively high false alarm rates that may be indicative of intermittent faults (Williams, et al., 1998), 

(Rosenthal & Wadell, 1990), (Allen, 2003) 

Implementation

Diagnostics: Primary area of implementation 

Prognostics: Not traditionally. Requires integration with stress models in order to evaluate precursors to 

failure 

Maturity: Many mature applications for diagnostic purposes 

D-22: Self-Monitoring Analysis and Reporting Technology (SMART) (ATA, 2008), (PCGuide, 

2001) (PCTechGuide, Undated) 

Target

Hard drives 

Technology

Precursor monitoring via an interface between the computer startup program (BIOS) and hard disk drive: 

head flight height, error counts, variations in spinning time, temperature and data transfer rates. 
114 

Failure modes and mechanisms

Head damage: cracks, contamination, bad connections to electronics; motor/bearing: motor/bearing 

failure or wear; excessive run-out; no spin; electronic module failures: bad connections, chip failures; 

Media: scratches, defects, bad servo, ECC corrections 

Implementation

Diagnostic: error values 

Prognostic: trending of error values 

Maturity: Mature technology for diagnostics 

A.2.4 Multivariate Analysis 

D-24: Support Vector Machines (SVM) Support Vector Classification (SVC) Support Vector 

Regression (SVR) (Sotiris & Pecht, 2007), (Cheng, et al., 2010) 

Target

Electronic products, systems. 

Technology

Applicable to multivariate analysis of large systems. Multiple sensors track surface insulation resistance 

(SIR), leakage current, cross talk, EMI, currents and voltages, vibration frequency, temperature, electrical 

resistance, inductance. SVMs used to classify and identify features. Data features are mainly statistical: 

mean, variance, distance measure, Eigen values. The analysis is sensitive both to improper functioning of 

single component or accumulation of small deviations of multiple components. 

Implementation

Diagnostics: Thresholds are used to identify faults and determine severity 

Prognostics: Time series provides progression of fault or damage and estimate prognostic distance 

Maturity: R&D 

D-25: Distance Metrics (Kumar, et al., 2008), (Kumar & Pecht, 2007) 

Target

Electronic products, systems. 

Technology

Applicable to multivariate analysis of large systems. Multiple sensors track surface insulation resistance 

(SIR), leakage current, cross talk, EMI, currents and voltages, vibration frequency, temperature, electrical 

resistance, inductance. 

Distance metrics such as Mahalanobis Distance (MD) used to reduce multivariate data to univariate data. 

Implementation

Diagnostics: Thresholds are used for system level diagnostics 

Prognostics: Symbolic time series analysis uses MD for prognostics 
115 

Maturity: R&D 

D-26: Projection Pursuit Analysis (Kumar, et al., 2008) 

Target

Electronic products, systems. 

Technology Applicable to multivariate analysis of large systems. Multiple sensors track surface 

insulation resistance (SIR), leakage current, cross talk, EMI, currents and voltages, vibration frequency, 

temperature, electrical resistance, inductance. The Projection Pursuit Analysis uses a Principal 

Components Analysis (PCA), least squares optimization (LS) and a Singular Value Decomposition 

(SVD) treatment of the data. 

Implementation

Diagnostics: Thresholds are used for system level diagnostics. The Projection Pursuit analysis method 

was also used to identify 8 key parameters for root cause analysis of anomalies 

Prognostics: very early research may lead to prognostic information as well 

Maturity: R&D and test. 

D-27: Continuous System Telemetry Harness (CSTH) (Gross, 2006), (Cassidy, et al., 2002) 

Target

Computer systems (hardware + software) 

Technology

Inputs include: system performance (“soft” variables: loads, throughput, queue length, bit errors), 

Physical variables (internal temperature, voltage, current, distributed), Canary variables (distributed 

synthetic transaction generators testing for user wait time 24/7). MSET (Multivariate State Estimation 

Technique) used to predict behavior of single variables. Sequential Probability Ratio Test (SPRT) used to 

detect deviations at particular time. Hundreds of sensors input to pattern recognition algorithms to 

identify faults. Redundancy managed through “inferential sensing” when sensors fail. Method also useful 

for diagnosing and healing/rejuvenating software (e.g. memory leaks, locks etc.) 

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Developed by Sun Microsystems. 

Maturity: Commercial R&D and prototype 

M-10: Model Based Diagnostics of Electrical Power Systems (Mengshoel, et al., 2008) 

Target

Electrical power systems 
116 

Technology

Sensors for each component (prioritized according to failure rates) provide input to a real time program. 

The models incorporate sensor failures via input from system commands. 

Failure modes and mechanisms

Component and assembly failures: relays, batteries, inverters, sensors etc. 

Implementation

Implementation requires agile algorithms that can be utilized in real time. Many implementations use 

Bayesian networks to diagnose failures based on sensor and command inputs. The application is mostly to 

diagnostic, rather than prognostic goals. 

Maturity: R&D 

A.2.5 Life Consumption Monitoring

H-2: Life Consumption Monitoring (Solder Joints) (Ramakrishnan & Pecht, 2003) 

Target

Solder joints 

Technology

Monitor stressors: temperature and vibrations. Estimate RUL based on damage/life consumption models. 

Failure modes and mechanisms

Solder joint fatigue 

Implementation

Prognostics: Temperature and vibrations monitored and damage model used to calculate “consumed life” 

and estimate RUL. 

Maturity: R&D 

H-8: Life Consumption Monitoring of modules/ TSMD (Harchani, et al., 2000), (Rouet & 

Foucher, 2004), (Shetty, et al., 2002) 

Target

Modules (avionics), cards, 

Technology

Monitor stressors: temperature and vibrations. Estimate RUL based on damage/life consumption models. 

TSMD: Time Stress Measurement Device 

Failure modes and mechanisms

Multiple failure modes: temperature and vibration induced failure modes. 
117 

Implementation

Prognostics: Temperature and vibrations monitored and damage model used to calculate “consumed life” 

and estimate RUL. 

Maturity: R&D 

M-1: Flow Accelerated Corrosion (Kim, et al., 2005), (Lee, et al., 2003) 

Target

Piping 

Technology

Online simulation of FAC condition (at elbows) using electrochemical and vibration sensors provides 

condition monitoring. Electrochemical sensors provide data on the amount of corrosive elements released 

into the flow. Vibration analysis provides direct measurement of local weight of the pipe wall 

Failure modes and mechanisms

Wall thinning due to erosion and corrosion 

Implementation

Diagnostic: provides information on location of thinning walls 

Prognostic: provides information on remaining thickness 

Maturity: implementation of integrated technologies is in R&D stages. Sensors exist and vibration based 

measurement of thickness exists. 

M-4: Smart Oil Analysis (Byington & Schalcosky, Undated), (Byington, et al., Undated) 

Target

Rotating machines, moving machinery, diesel engines 

Technology

This technology uses smart oil sensors for measuring Electrochemical Impedance Spectroscopy (EIS) that 

provides over a wide range of frequencies information on both type and size of contaminants. 

Failure Modes

Wear of metal components; breakdown of lubricants; external contaminants; failures of components that 

allow entry of moisture/water. 

Implementation

Diagnostics and prognostics: allows diagnosing quality of oil. This can be used to assist in diagnosing 

structural damage or diagnose and provide prognosis on the breakdown of lubricants. The smart sensors 

allow for online implementation. 

Maturity: Commercial R&D 
118 

M-6: Physics Based Prognostics for Pneumatic Valves (Daigle & Goeble, 2011), (Daigle & 

Goebel, 2009) 

Target

Pneumatic valves 

Technology

Physical model applied to pneumatic valve. The physical parameters measured include opening degree, 

pressures, friction coefficient, and response time. The model calculates an End of Life (EOL) probability 

distribution that can be translated to RUL 

Implementation

Diagnostics: The measurement identifies the damage and its location 

Prognostics: Based on historical data a RUL is generated 

Maturity: Currently implemented via simulation on historical (space shuttle) data. R&D stage. 

A.2.6 Vibration Analysis 

H-4: Vibration Analysis of Rotating Elements (Harris, 2000), (Lacey, 2008), (Porotsky & 

Bluvband, 2012) 

Target

Rotating elements in machines (bearings, cooling fans, turbans) 

Technology

Use of diverse sensors: displacement, velocity and accelerometers, each provide coverage of different 

frequencies and amplitudes. Many different algorithms for feature selection and analysis. Time trending. 

Failure modes and mechanisms

• Cracks, pits, and roughness in rolling element bearing components 

• Unbalance of rotating machine parts. 

• Shaft misalignment. 

• Coupling problems. 

• Bends, bows, and cracks in shafts. 

• Excess sleeve bearing wear. 

• Loose parts. 

• Misaligned or damaged gear teeth. 

• Deterioration caused by broken or missing parts. 

• Deterioration caused by erosion and corrosion. 

• Resonance of components. 

• Electrical effects. 
119 

Implementation

Mature prognostics and diagnostics. Recent improvements provide more accurate RUL estimates. 

Implementation often in tandem with oil/fluid analysis. 

Maturity: Mature technology 

H-5: Vibration Analysis of Transformers (Wang, et al., 2010), (Rivera, et al., 2000), (Leibfield, 

1988), (Hu, et al., 2012) 

Target

Transformers, power transformers 

Technology

Use of multiple accelerometers to analyze vibration of magnetic cores and windings 

Failure modes and mechanisms

Structural failures of windings, supports and joints.

Implementation

As advanced diagnostic and prognostic tool still in R&D stage. Complements, temperature and oil 

analysis. Vibration analysis can be applied to prognostics 

Maturity: technology in use 

A.2.7 Acoustic Techniques 

M-2: Acoustic Monitoring (IAEA, 2008), (IAEA, 2007), 

Target

Hydraulic equipment; compressed air systems; steam systems; vacuum equipment; valves, fittings of 

valves, boiler tubes; feedwater heater tubes; reactor vessels and piping (heads, steam generator inlet/outlet 

pipes and fittings of closing holes); control rod housing; main stream lines; transformers; fossil high 

energy piping, primary coolant loop 

Technology 18

Monitoring of acoustic emissions and interpreting acoustic signatures characteristic of wear and 

deterioration: 

Monitoring of acoustic emissions where none are expected signal changes in condition. For example: 

• Motor operated valves that are nearly open or nearly closed will emit sound above background 

noise and indicate deterioration; 

• Cavitation in pumps or pipes with high flow rates indicate a change that can result in rupture; 

• Leaks are characterized by acoustic emission 

 

18 Example of commercial tools: “Pulse Analyzer Platform” 

http://www.bksv.com/Products/PULSEAnalyzerPlatform/WhatisPULSE.aspx
120 

Monitoring of changes in acoustic signatures of components with characteristic acoustic signals (e.g. 

engines) indicates condition changes. 

Monitoring for transient emissions: Detect the transient sound of energy release with abrupt deterioration 

processes such as: crack development/propagation and material loosening and separating from wall. 

Pulses are analyzed and compared with test results or finite element models for diagnostic and prognostic 

information. Requires highly sensitive Piezo electric or strain gauge sensors 

 

Acoustic monitoring is split into two ranges: 

Sonic range – (0Hz – 20kHz) – The sonic range includes all frequencies in the hearing range of humans. 

Sonic range includes all frequencies used in mechanical vibration analysis and low frequency leak 

detection (2Hz – 20kHz) 

Ultrasonic range – (20 kHz – 1MHz) - Ultrasonic frequencies are used in cavitation detection, acoustic 

emission, high frequency leak detection, corona and partial discharge detection. 

Failure modes and mechanisms

Leaks, cracks, cavitation corona and partial discharge, interfacial bond failure; delamination of layers; 

transformer failures: degradation of insulation, inception of bobbles, localized heating in oil or paper, 

paper tracking or carbonization, hydrogen gas evolution, cavitation leading to nitrogen emission, oil 

pyrolysis, core winding, loose parts impinging on walls, irregularities. 

Implementation

Diagnostics: Automatic recognition is mostly based on power spectra (using fast flux techniques) but also 

autoregressive analysis, wavelet decomposition , moment calculations, shifts of Eigen frequencies and 

higher harmonics. Shape and magnitude change are classified by expert systems. Improved algorithms 

distinguish events (e.g. loose parts impinging on walls) from background noise and allow for improved 

localization of events. 

Prognostics: Trending of acoustic emission allows for tracking the progression of grain structure 

breakdown. Is being used for online continuous monitoring. 

Sensors cannot withstand radiation and heat near reactor vessel – waveguides are used to attach the 

sensors, hence affecting the quality/sensitivity of measurement. 

Maturity: Mature and very active technology. Commercial tools to interpret measurements and provide 

both diagnostics and prognostic information exist. New uses and more accurate implementation in R&D 

stages as well. 

M-3 Ultra-Sound Test (Birks, et al., 1991), (Krautkrämer & Krautkrämer, 1990) 

Target

Mainly applied to passive metal and alloy structures and components such as vessels, pipes. Can be used 

on concrete, wood and composites with differing resolution. 
121 

Technology

Ultrasound testing is performed with very short ultrasonic pulse-waves with frequencies ranging from 

0.1-15 MHz and occasionally up to 50 MHz. These are directed into materials to characterize materials or 

detect internal flaws. A common example is ultrasonic thickness measurement, which tests the thickness 

of the test object, for example, to monitor pipe corrosion. 

An ultrasound transducer connected to a diagnostic machine is passed over the object being inspected. 

The transducer is typically separated from the test object by a couplant (such as oil) or by water, as in 

immersion testing. (When ultrasonic testing is conducted with an Electromagnetic Acoustic Transducer 

(EMAT) the use of couplant is not required.) Literature acknowledges two modes of operation: 

• Pulse-echo mode: in this mode the transducer both emits and receives the pulsed ultrasound wave 

as it is reflected by the boundaries in the material. These reflections indicate any defects or 

structures within a uniform substrate. In this mode only one surface must be accessible. 

• Through-transmission mode: also called attenuation mode. A separate receiver measures the 

pulsed waves passing through the medium. The amount of attenuation indicates any changes in 

the uniformity of the medium. 

Failure modes and mechanisms

Cracks, changes in thickness (due to corrosion or stress), shape changes 

Implementation

Diagnostics: Not an automated tool but valuable due to the High sensitivity and high penetrating power, 

which allows for the detection of small flaws deep in the structure. 

Maturity: Mature technology. Commercial tools in many industries requiring nondestructive methods, 

including aerospace, automotive and other transportation sectors, construction and medicine. 

A.2.8 Targeted Micro/Macro-scopic Measurements 

D-17: Molecular Test Equipment (MTE) (Wright & Kirkland, 2003), (Wright, et al., 2003), 

(Wright, et al., 2001) 

Target

CMOS ICs 

Technology

Molecular test equipment (MTE) within IC’s: Carbon Nanotube technology is used to measure voltage, 

currents and chemical changes indicative of progression to failure

Implementation

Diagnostics: thresholds and signatures 

Prognostics: signatures and time trending 

Maturity: R&D stage 
122 

D-23: Signal Validation through Multi Level Flow Modeling (MLM) (Öhman, 2002), 

(Larsson, 2002) 

Target

Sensors 

Technology

Sensor signals validated by checking consistency between redundant sensors to detect flow leaks and 

sensor failures

Failure modes and mechanisms

Any sensor failures 

Implementation

Diagnostic to detect failed sensors and false alarms 

Maturity: R&D 

H-3: Line Resistance Analysis (LIRA) (Fantoni & Toman, 2006), (Fantoni, 2009) 

Target

Cables 

Technology

Frequency domain analysis of high frequency resonance effects of unmatched transmission lines. 

Sensitive to small changes of wire electric parameters, mainly the insulation permittivity, that are a 

significant condition indicator of the cable state. Possibility to detect and localize small insulation cracks, 

in spite of different structures (insulation type, geometry) and non-aging related effects. 

Failure modes and mechanisms

Thermal and radiation aging, humidity, insulation defects, mechanical damage. 

Implementation

Diagnostics and prognostics. 

Maturity: R&D and test 

H-6: Dissolved Gas Analysis (IEEE, 2008), (IEC, 1999), (Golkhah, et al., 2011), (Mohd 

Radzian & Itoh, 2010), (Gavrilovs & Borscevskis, 2011) 

Target

Transformers, power transformers 

Technology

Analysis of gases (Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Acetylene (C2H2), Hydrogen 

(H2), Carbon monoxide (CO), Carbon dioxide (CO2), and non-fault gases are Nitrogen (N2), and Oxygen 
123 

(O2).) dissolved in the insulating mineral oil. Pattern recognition (Neural Networks) applied to diagnose 

type of fault/degradation. 

Failure modes and mechanisms

• Corona in oil ; 

• Pyrolysis in oil; 

• Arcing in oil; 

• Pyrolysis in cellulose;

These faults lead to the degradation of the oil which will cause further damage 

Implementation

Requires sample. 

Diagnostic method only 

Maturity: Technology in use 

H-7: Circuit Voltage Analysis (Guo, et al., 2012) 

Target

Small circuits including chips 

Technology

Application of State Vector Machines to voltage measurements at several measuring points to identify 

characteristic patterns of several failure modes. Choice of measuring points is based on understanding 

failure modes, while the pattern recognition and diagnostics driven by data only. 

Failure modes and mechanisms

• Circuit board damage 

• Burned out chip 

• Broken pins 

Implementation

Diagnostics: identifies failures 

Prognostics: None 

Maturity: R&D 

H-12: Tribology (Khonsari & Booser, 2008), (Bhushan, 2000), (Holmberg & Helle, 2008), 

(Glos & Sejkorova, 2012) 

Target

Rotating machines, moving machinery, diesel engines 

 
124 

Technology

Tribology: size and composition of internal and external contaminants 

• IR spectroscopy: nonmetal contaminants (from fuel, acids, degraded grease, additive 

deterioration); 

• Particle counting; 

• Total Acid Number (pH): oil oxidation/degradation; 

• Viscosity: presence of fluid contaminants (water, fuel solvents) ; 

• Water content; 

• Spectrometric analysis: wear particles, contaminants, additive depletion; 

• Ferrography: metal particles; 

• Micropatch: large contaminants; 

Failure modes and mechanisms

Wear of metal components; breakdown of lubricants, external contaminants, failures of components that 

allow entry of moisture/water. 

Implementation

Mature diagnostic technology. Provides information on wear before vibrations can be detected. No other 

method to identify water in the lubricant, incorrect viscosity, oxidation and chemical breakdown. New 

“real-time sensors” are making online, automated, analysis possible for advanced diagnostics and 

prognostics. Progress made with mini-labs for onsite measurements: Mini-labs include: Wear debris 

analysis (WDA) (both quantitative: ferrous density, and qualitative: analytical); particle counting; water 

measurement: crackle or time resolved dielectric; oil chemistry: dielectric or voltametric or TAN/TBN; 

40C viscosity. 

Maturity: Mature technology 

 

M-5: Coil Current Monitoring System (CCMS) (IAEA, 2007), (IAEA, 2008), (Westinghouse, 

Undated) 

Target

Control Rod driving system (CRDS) control element driving mechanism control system (CEDMC) 

Technology

Coil currents of Control Rod Driving System (CRDS) are monitored online at high sampling rate for 

timing sequence of CEDM motion, current amplitude and pattern mismatch of wave form. 

Failure modes and mechanisms

Failures in coils, failures in Hall effect current sensors, failures in timing control cards, voltage 

adjustment cards and silicon controlled rectifiers (SCR) 

Implementation

Diagnostics: Capable of fast diagnosis of condition of multiple elements in the CEDM and CEDMC 

(Control Element Driving Mechanism Control System). 

Prognostics: Historic data can be trended to provide degradation trends and prognostics 
125 

Maturity: Newly commercial: Westinghouse has recently developed a commercial implementation. 

M-7: Neutron Noise Surveillance and Monitoring (Including External) (IAEA, 2008), (Thie, 

1981), (Park, et al., 2003), (CZIBÓK, et al., 2003), (IAEA, 2005), (IAEA, 2007), (Coble, et al., 

2012) (Damiano & Kryter, 1990), (Fry, et al., 1984) 

Target

Core internals, control rods, fuel channels, PHWR detector tubes 

Technology

Neutron noise direct diagnostics: Process signal measured directly – to determine normal/abnormal 

function 

Neutron noise spectral analysis (auto and cross spectra APSD, CPSD) or auto and cross correlation 

functions (ACF, CCF) 

Failure modes and mechanisms

• Vibration: core, control rods, fuel channels (indicate structural wear but also generate wear) 

• coolant boiling; 

• moderator circulation; 

• water level oscillations; 

• Thermo-hydraulic instabilities; 

• Propagating temperature fluctuations; 

• BWR instabilities: void/pressure oscillations, Global, regional and local (due to unseating of a 

fuel assembly) instabilities. 

Implementation

Noise due to vibrations, boiling, coolant flow, and temperature gradients are translated in the reactor core 

to neutron noise. Anomalies are identified as they are measured and compared to anomaly signatures 

(broadband, sink structure, peaks etc.). 

To detect: one is required to know “signatures” of anomalies; to diagnose and localize origin of noise 

need to know the “core transfer function” either from measurements or model. 

Implementation requires much planning as electronics and procedures are not part of original NPP design 

and there are many complex requirements: 

• Multi-channel data acquisition systems for digitizing and storing detector signals; 

• Multichannel optical isolation amplifiers separating data acquisition system s from station 

hardware; 

• Maintenance procedures to connect and disconnect the systems without disrupting NPP 

operation; 

• Digital data processing to operate offline on the recorded signals to produce multichannel 

statistical functions in the frequency and time domains for noise analysis; 

• Physical models; 

• Isolation requirements; 
126 

• Analog signal conditioning: signal is either analog current as direct output of sensor (selfpowered flux detector, or flow transmitter), or analog voltage already transformed to standard 

voltage range; 

• Measurement must be done at steady state high-power operation; 

• Measurements for certain types of signatures (temperature signals) can require long times up to 

12 hours. 

Transient noise measurements can also be used (e.g. pump trip or startup, power step-back, pump 

changeover). 

Core vibrations are diagnosed via spectral analysis of ex-core neutron detectors. Signals are processed via 

a fast Fourier transform to calculate the auto power spectral density (APSD), cross power spectral density 

(CPSD), coherence function and phase. Increasing amplitudes and decreasing frequencies indicate 

material degradation: loosening of the core barrel secondary support or wear of the hold-down springs. 

Both beam mode (pendulum) and shell mode vibrations are monitored. A mechanical model of the 

pressure vessel, core barrel and core barrel support (based on finite element model) greatly enhance the 

ability of the analysis to identify the mechanical problem through the structure Eigen modes. 

Control rod vibrations detected via in-core neutron detectors (at least 3 required to identify the 

excessively vibrating rod) at different radial positions. Algorithms for identifying the rod are based on 

either parametric methods (localization curves) or empirical techniques (e.g. neural networks). In-core 

flux detectors detect the number of fuel channels vibrating based on coherence and phase measurements. 

Finite element vibration analysis is used to compare and identify abnormal vibration modes which 

indicate abnormal conditions of the end fitting support and the garter springs of the fuel channels. 

Detector tube vibrations are detected by ICFDs via changes in flux due to non-homogeneous static flux 

field: Increase in vibration amplitude as well as impact with surrounding structures is detected via the 

ICFD noise analysis. 

Neutron flux oscillations analyzed via local power range monitors (LPRM) and average power range 

monitors (APRM) indicate instabilities in BWR cores. 

Measuring radial weighted average of in-core temperature noise accurately calculates the Moderator 

Temperature Coefficient (MTC) for diagnostics. 

Maturity: Mature technology and application. Used extensively for testing in existing NPP. 

Automated/online implementation for diagnostics is more recent. 

M-9: Resolver Channel Mismatch Detection (Brown, et al., 2008), (Kobayashi & Miya, 2004), 

(Clark, et al., 2001) 

Target

Resolver position sensors 

Technology

Analysis of voltage mismatch of the two output voltages is used to calculate the mismatch 
127 

Failure modes and mechanisms

Can only detect insulation faults occurring on the output windings (and not on the reference winding): 

• Short circuits between windings 

• Open circuits 

• Short circuits to ground 

Implementation

Diagnostic: Can be implemented in several ways: using additional hardware, or using only an algorithm 

applied to outputs based completely on model of mismatch. Provides diagnosis of size of mismatch. 

Prognostic: Not implemented as prognostic. However, trending of mismatch size over time may be useful 

to generate RUL models. This may require a hybrid implementation. 

Maturity: R&D. 

 
128 

Appendix B 

B.1 Organizations, Centers and Communities 

PHM Society: organizes an annual conference (the International Conference on Prognostics and Health 

Management) with proceedings, and publishes an online journal: The International Journal of Prognostics 

and Health Management (http://www.phmsociety.org/journal ). The PHM society offers all publications 

free of cost and is a community conducive to the spreading of knowledge and results in the area of 

prognostic health management. 

IEEE Reliability Society is one of the organizers of the Annual Reliability and Maintainability 

Symposium (see below) and is also the sponsor of IEEE Standards Project 1856, “(Draft) Standard for 

Prognostics and Health Management of Electronic Systems”. The do tics: cument contains definitions of 

equipment levels, prognostic methodologies and procedures for their application. It is expected that this 

will facilitate propagation of PHM techniques that have been successful in one application to others. 

 

The IEEE Systems Society holds an annual Conference on Prognostics and Health Management (PHM) 

and publishes the annual proceedings. 

DOE Laboratories. The DOE National Laboratories, particularly PNNL and ORNL have dedicated staff 

working on diagnostics and prognostics and are the source of several publications referenced in this 

document. 

The Prognostics and Health Management Consortium is a group established in the Center for 

Advanced Life Cycle Engineering (CALCE) at the University of Maryland. The group manages a 

collaborative research program leading to much progress in multiple domains of the prognostic 

engineering field. 

Annual Reliability and Maintainability Symposium (RAMS) includes a prognostics track which offers 

an environment to exchange ideas and present academic results and commercial tools 

IAEA: The IAEA has been active in sponsoring standards for prognostics 

NASA: Of special interest are teams at NASA that are dedicated to research in the fields of 

nondestructive evaluation and testing as well as diagnostics and prognostics. The unique maintenance 

requirements of a space program (underlined by the difficulty or inability to perform maintenance in 

space) fuel a strong prognostics research program: 

• Prognostics Center Excellence is located at Ames Research Lab and focuses on such systems as 

power supplies, batteries, sensors, wiring and electronics. The center has compiled a “Prognostics 

Data Repository” to ease the problem of availability of data that will allow the comparison and 

benchmarking of algorithm performance. 
129 

• NDEAA – The Non Destructive Evaluation and Advanced Actuators Technologies lab is located 

at JPL and advances research in this field. 

 

B.2 Conferences 

There are several other societies and publications whose proximity to the area of diagnostics and 

prognostics results in frequent presentations and publications in these areas. These include: 

• The American Society for Nondestructive Testing: annual conferences and several publications; 

• The Acoustical Society of America : holds annually the International Congress on Acoustics 

(ICA) and several publications; 

• The Acoustical Society of Australia holds an annual meeting; 

• IEEE: conferences in the area of diagnostics and prognostics are distributed among the IEEE 

societies: IEEE International Workshop on Defect Based Testing (DBT) (2000; 2004); IEEE 

Aerospace Conference; IEEE Transactions on Reliability; IEEE 

Instrumentation and Measurement Technology Conference Proceedings, 2008; IEEE 

Instrumentation Measurement Magazine; 

• National conference on Innovative applications of artificial intelligence; 

• International Conference on Artificial Neural Networks (ICANN); 

• International Conference on Neural Information Processing (ICONIP); 

• Association for the Advancement of Artificial Intelligence: Artificial Intelligence for Prognostics 

Fall Symposium (2007); 

• The International Journal of Advanced Manufacturing Technology; 

• International Journal of Nuclear Safety and Simulation. 

 
130 

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