Kevin J. Melcher

Glenn Research Center, Cleveland, Ohio

T. Shane Sowers and William A. Maul

Analex Corporation, Brook Park, Ohio

Meeting the Challenges of Exploration Systems:

Health Management Technologies for Aerospace

Systems With Emphasis on Propulsion

NASA/TM—2005-214026

October 2005
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Kevin J. Melcher

Glenn Research Center, Cleveland, Ohio

T. Shane Sowers and William A. Maul

Analex Corporation, Brook Park, Ohio

Meeting the Challenges of Exploration Systems:

Health Management Technologies for Aerospace

Systems With Emphasis on Propulsion

NASA/TM—2005-214026

October 2005

National Aeronautics and

Space Administration

Glenn Research Center

Prepared for the

First International Forum on Integrated System Health Engineering

and Management in Aerospace

sponsored by NASA Ames Research Center and NASA Marshall Space Flight Center

Napa, California, November 7–10, 2005
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Abstract

The constraints of future Exploration Missions will require unique Integrated System Health 

Management (ISHM) capabilities throughout the mission. An ambitious launch schedule, humanrating requirements, long quiescent periods, limited human access for repair or replacement, and 

long communication delays, all require an ISHM system that can span distinct, yet interdependent 

vehicle subsystems, anticipate failure states, provide autonomous remediation and support the 

Exploration Mission from beginning to end. NASA Glenn Research Center has developed and 

applied health management system technologies to aerospace propulsion systems for almost two 

decades. Lessons learned from past activities help define the approach to proper ISHM development:

• Sensor Selection – identifies sensor sets required for accurate health assessment;

• Data Qualification & Validation – ensures the integrity of measurement data from sensor to 

data system;

• Fault Detection and Isolation – uses measurements in a component/subsystem context to 

detect faults and identify their point of origin;

• Information Fusion and Diagnostic Decision Criteria – aligns data from similar and disparate 

sources in time and use that data to perform higher-level system diagnosis;

• Verification &Validation – uses data, real or simulated, to provide variable exposure to the 

diagnostic system for faults that may only manifest themselves in actual implementation, as 

well as, faults that are detectable via hardware testing.

This presentation describes a framework for developing health management systems and highlights 

the health management research activities performed by the Controls and Dynamics Branch at the 

NASA Glenn Research Center. It illustrates how those activities contribute to the development of 

solutions for Integrated System Health Management.

Meeting the Challenges of Exploration Systems: 

Health Management Technologies for Aerospace 

Systems with Emphasis on Propulsion 

Kevin J. Melcher

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

T. Shane Sowers and William A. Maul 

Analex Corporation 

Brook Park, Ohio 44142

NASA/TM—2005-214026 1
Glenn Research Center at Lewis Field

HMS Development Process

Data 

Validation

Fault Condition

Identification

Fault Location

Identification Fault Isolation

Information

Fusion

HM 

Diagnostics/Prognostics

Recommended

1. Control Response

2. Maintenance Plan

Recommended

1. Control Response

2. Maintenance Plan

Design

Requirements

Concept of 

Operations

System Design

FMEA/Life 

Studies

Optimal 

Sensor Set

Qualified

Sensors

Health Estimated

Life Usage

Sensor 

Selection

HMS V&V

Fault 

Detection

Models

Component

Definition

Life Limits

HMS

Requirements

High Risk

Failure Modes

Data 

Validation

Fault Condition

Identification

Fault Location

Identification Fault Isolation

Information

Fusion

HM 

Diagnostics/Prognostics

Recommended

1. Control Response

2. Maintenance Plan

Recommended

1. Control Response

2. Maintenance Plan

Design

Requirements

Concept of 

Operations

System Design

FMEA/Life 

Studies

Optimal 

Sensor Set

Qualified

Sensors

Health Estimated

Life Usage

Sensor 

Selection

HMS V&V

Fault 

Detection

Models

Component

Definition

Life Limits

HMS

Requirements

High Risk

Failure Modes

Health Management System Development Process

Optimum performance and effectiveness of a health management system (HMS) is achieved when its 

development coincides with the development of the system whose health it is being tasked to 

manage. It is much less effective and efficient when it is implemented after the fact. 

The chart shown above was developed by the NASA Glenn Controls and Dynamics Branch to 

describe its process for developing HMSs. In general, the HMS development process requires a 

thorough understanding of the system being managed. Knowledge acquisition from domain experts 

is essential in establishing the scope of a HMS. A clear set of system requirements and concept of 

operations are some of the first essential elements. A Failure Modes and Effects Analysis must be 

performed to identify the critical faults and document how those faults manifest themselves in the 

system. Also, when development of a health management is incorporated early in the system design, 

little, if any, test data is available and a models of the system are essential. These system models 

must be developed with sufficient detail and complexity so that they provide a sound basis for 

developing and testing the required health management system.

Focusing on the design requirements for the monitored system, as well as, for the HMS itself, 

elements of the development process may be identified as follows :

• Sensor Selection

• Data Validation

• Fault Detection

• Fault Isolation

• Information Fusion

Note that clear boundaries between these elements do not necessarily exist. There is some overlap 

and distinctions between the elements are not always clear. However, it is useful to address 

technology capabilities and gaps in each of these elements separately. Therefore, in an attempt to 

discuss the advances required for future exploration missions, the charts that follow describe in more 

detail each of the HMS elements identified above.

NASA/TM—2005-214026 2
Glenn Research Center at Lewis Field 3

Systematic Sensor Selection Strategy

Developed in collaboration with 

Christian Brothers University

• Selects sensors (type/location) to optimize the fidelity and 

response of engine health diagnostics

• Targets high risk engine anomaly types/classes

• Assigns quantitative sensor suite value based on overall risk 

reduction, diagnostic speed, and probability of correct fault 

type/class isolation

• Accommodates various types of models/physical inputs

• Uses critical FMEA identified modes and risk assessments

• Considers sensor response and system/signal noise effects

• Accommodates fault scenarios from correlated test data 

and/or model simulations

Sensor Selection

RS-84 Boost Stage

Rocket Engine

Systematic Sensor Selection Strategy

S4 is a model-based procedure for systematically selecting an optimal sensor suite for overall health 

assessment of a given host system. S4 was developed in collaborative effort by the Controls and 

Dynamics Branch at NASA GRC and Christian Brothers University. Initial efforts to apply the 

technology focused on the Rocketdyne RS-83 and RS-84 boost stage liquid rocket engines. This 

systematic sensor selection strategy identified a minimum suite of 22 sensors (from a candidate set of 

59 sensors) that maximize risk reduction potential for the RS-84 engine. 

Sensor data are the basis for performance and health assessment of most complex systems. 

Therefore, careful selection and implementation of sensors is critical to enable high fidelity system 

health assessment. S4 is designed with these considerations in mind.

S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select 

iteration, and the final selection analysis. The knowledge base consists of system design information 

and heritage experience together with a focus on components with health implications. The sensor 

suite down-selection identifies a group of sensors that provide good fault detection and isolation for 

targeted fault scenarios. This process is composed of three basic components: a system health 

diagnostic model, a merit algorithm, and a selection algorithm. In the final selection analysis, a 

statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite.

Though this systematic sensor selection process was developed to enhance design phase planning 

and preparation for in-space propulsion health management, the S4 process can also be applied to a 

broad range of non-propulsion health management systems (e.g., power, communications) that are 

part of the Exploration Systems architecture.

Ref: Santi, L.M., Sowers, T.S., Aguilar, R.B., “Optimal Sensor Selection for Health Monitoring 

Systems”, 41st Joint Propulsion Conference and Exhibit, AIAA 2005-4485, July 2005.

NASA/TM—2005-214026 3
Glenn Research Center at Lewis Field 4

Sensor Data Validation

Data Qualification Validation Studio (DQVS TM)

Developed in collaboration with 

Expert Microsystems, Inc.

• Real-time detection of sensor failures and other data anomalies

• Rapid model prototyping enabled by development studio

• Uses analytical redundancy models, statistical limit filters and

Bayesian fault decisions

• Provides operating mode partitioning and generalized data 

interfaces

Sensor Data Qualification and Validation

Sensor data qualification and validation is the process of analyzing sensor data to insure that it 

accurately represents the system state being measured. NASA Glenn has developed and implemented 

a number of sensor qualification and validation technologies, e.g. Neural Networks, Model-Based 

Analytical Redundancy, Kalman Filters and Wavelets. These methods are used not only to identify 

hard sensor failures, but also soft sensor failures, such as drift and noise.

Data validation is an important element in the health assessment process. The goals of a sensor 

validation system are to prevent safety system false alarms, unnecessary shutdowns, or improper 

system responses by ensuring that automated health management systems “reason” with valid data. 

Proper fault detection and isolation can only be performed when information provided by the sensors 

is valid. Therefore, an initial analysis of sensor data to filter beyond simple off-scale data is crucial to 

any HM system performance. 

For one such project, NASA Glenn partnered with Aerojet and Expert Microsystems to develop an 

advanced sensor validation technology that utilized a Bayesian Belief network to provide real-time 

solutions for RS-83 and RS-84 propulsion systems. This technology incorporated the analytical 

redundancy relationships between all the sensors in the engine to establish a belief network that 

would identify faulty sensors and a level of confidence of this identification. The result of this effort 

is contained within a commercially available software suite, The Data Quality Validation StudioTM

produced by Expert Microsystems, Inc. NASA Glenn’s Controls and Dynamics Branch supported 

the development and gained expertise in the use of the software by providing space shuttle main 

engine domain expertise, models for sensor relationships, and by conducting validation testing of the 

new sensor failure detection algorithms.

Data validation is vital for any system that relies solely on sensor information to evaluate system 

performance and to assess system health. For Exploration Systems Missions where automation and 

remediation are based exclusively on sensor data and limited human input, advancement of these 

technologies to ensure optimum implementation, development and certification is required.

Ref: Bickford, R. L., et al., “Real-Time Flight Data Validation for Rocket Engines”, 32rd Joint 

Propulsion Conference, AIAA/ASME/SAE/ASEE, 96-2827, July 1996..

NASA/TM—2005-214026 4
Glenn Research Center at Lewis Field 5

Fault Detection and Isolation

Real-Time and Post-Test Diagnostic System

Inverse Model for Fault Detection and Isolation

Developed in collaboration with Christian Brothers University

• Model-based approach suitable for general system application

• Supports real-time and post-test diagnostic requirements

• Provides fault class (component/LRU/response group) isolation capability with 

compatible sensor suites

• Utilizes engine image model at complexity level consistent with real-time 

diagnostic constraint

• Accommodates control effects and low order fault dynamics

Model Inversion for Fault Detection and Isolation

Model inversion is the process whereby system states are identified from measurement values rather 

than the converse which is typical of the modeling process. An inverse model for fault detection and 

isolation in rocket engines was developed collaboratively by NASA Glenn’s Controls and Dynamics 

Branch and Christian Brothers University as part of a health management system for NASA’s Next 

Generation Launch Technology program. The inverse model framework is a component of the 

Systematic Sensor Selection Strategy (S4) mentioned previously.

The algorithms that effect inversion are referred to as inverse models (IM). The core of any inverse 

model is a parameter optimization algorithm whose function is to determine component performance 

that best reconcile system model prediction and observation. Diagnosis of system condition and 

conclusions related to health status are inferred from the magnitude and/or variance of health 

parameter excursions from the accepted norm. Most common model-based techniques that assign 

health status to parameter state estimates can be classified within the inverse model framework. This 

would include the broad class of influence methods as well as state space techniques and various 

hybrid model-based strategies.

An inverse model may be constructed to accommodate system nonlinearity, system dynamic 

response, and external control inputs at nearly any level of detail required. For each specific 

application, the appropriate inverse model form is suggested by the trading of diagnostic response 

time and state discrimination level. It is important to note that the design, maintenance, control, and 

health monitoring functions can all be supported by inverse models. Therefore the development of a 

robust inverse model for a given Exploration Systems application will likely support all diagnostic 

functions at some level.

The reader should note that references on this effort are currently unavailable as reporting of this 

work is in progress.

NASA/TM—2005-214026 5
Glenn Research Center at Lewis Field 6

Fault Detection and Isolation

Real-Time Diagnostic System

Propulsion IVHM Technology Experiment 

(PITEX)

GRC-led Developed in collaboration with 

Ames Research Center and Kennedy 

Space Center

• Real-Time Diagnosis of Faults

• Inclusion of Real-World Effects

• Demonstrated on Flight-Like Hardware

• Applicable to Relevant Exploration Subsystems

X-34 Reusable Launch Vehicle

PITEX

Block Diagram

Propulsion IVHM Technology Experiment

The Propulsion IVHM Technology Experiment (PITEX) is a real-time model-based diagnostic 

system for the main propulsion system of the X-34 reusable launch vehicle, a space-launch 

technology demonstrator. PITEX was developed by a multi-center team led by NASA Glenn. 

During development, the Controls and Dynamics Branch was responsible for acquiring knowledge of 

the system, for developing signal processing algorithms and feed system simulations, for providing 

failure scenario data, and for conducting extensive testing and evaluation.

PITEX was demonstrated in a simulation-based environment that used detailed models of the 

propulsion subsystem to generate nominal and failure scenarios during captive carry – the most 

safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion 

System (MPS) was performed, these simulated data were used to verify and validate the software 

system. Advanced diagnostic and signal processing algorithms were developed and tested in realtime on flight-like hardware. In an attempt to expose potential performance problems, these PITEX 

algorithms were subject to numerous real-world effects in the simulated data including noise, sensor 

resolution, command/valve talkback information, and nominal build variations. The current research 

has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics 

required to evaluate the diagnostic system, and studied the impact of real-world challenges 

encountered when monitoring propulsion subsystems.

PITEX has applicability to a wide variety of long duration systems, especially propulsion systems, 

that are likely to be part of NASA’s Exploration Systems Program.

Ref: Maul, W. A., et al, “Addressing the Real-World Challenges in the Development of Propulsion 

IVHM Technology Experiment (PITEX)”, First Intelligent Systems Technical Conference, AIAA, 

September 2004.

NASA/TM—2005-214026 6
Glenn Research Center at Lewis Field 7

Information Fusion for Enhanced Aircraft Engine 

Prognostics & Health Management

Engine Health 

Observations

Matlab/Simulink Architecture Demonstration Using Available C-17 T1 Aircraft Flight Data

Information Fusion for Extended Gas Path Analysis Capability

Information, or data, fusion is the ability to align data from similar and disparate sources in time and 

use those data to perform higher-level system diagnosis. In this area, expertise within NASA 

Glenn’s Controls and Dynamics Branch is historically found on the aeronautics, rather than the 

space, side of the house. Under NASA’s Aviation Safety Program, NASA and Pratt & Whitney 

(P&W) are collaborating to develop Information Fusion technologies.

A wealth of aircraft turbine engine data is available from a variety of sources including on-board 

sensor measurements, operating histories, and component models. Furthermore, additional data will 

become available, as advanced prognostic sensors are incorporated into next generation gas turbine 

engine systems. The challenge is how to maximize the meaningful information extracted from these 

disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health 

and condition of the engine.

To address this challenge, NASA and Pratt & Whitney (P&W) have developed a modular 

hierarchical information fusion architecture. To demonstrate the efficacy of this architecture, a fusion 

demonstration of two gas path analysis algorithms, the Enhanced Self-Tuning Onboard Real-time 

Model (eSTORM) and a neural network-based Gas Path Anomaly Detector (GPAD), was performed. 

The architecture used to fuse these two algorithms is shown above. This fusion allows the system to 

detect and isolate both sensor and component faults. Furthermore, once a sensor fault is detected, it is 

accommodated by replacing the faulty physical measurement with a estimated value. This allows the 

system to continue to accurately estimate component performance even in the presence of a sensor 

fault. 

Data Fusion is an enabling technology for long duration missions where self diagnosis of very 

complex systems may be the difference between mission success and failure.

Ref: Volponi, Allan J. et al, “Development of an Information Fusion System for Engine Diagnostics 

and Health Management,” Prepared for the 39th Combustion/27th Airbreathing Propulsion/21st 

Propulsion Systems Hazards/3rd Modeling and Simulation Joint JANNAF Subcommittee Meeting 

Colorado Springs, Colorado, December 1-5, 2003

NASA/TM—2005-214026 7
Glenn Research Center at Lewis Field 8

Health Management System

Verification and Validation

Data Qualification and Validation Test-Bed

Developed by GRC

• Discriminate between sensor faults and anomalous plant states

• Evaluate Performance of DQV Procedures

• Fidelity/timeliness of sensor fault detection capability

• Allows rapid, yet extensive, HMS prototype evaluation

• Flexible framework for evolving needs

Data Qualification and Validation Test-Bed

On-line data validation, one aspect of system verification and validation, is a performance-enhancing 

component of modern control and health management systems. It is essential that performance of the 

data validation system be verified prior to its use in a flight-rated control and health management 

system. A new Data Qualification and Validation (DQV) Test-Bed application was developed by 

NASA Glenn’s Controls and Dynamics Branch to provide a systematic test environment for this 

performance verification. The DQV Test-Bed was used to evaluate a model-based data validation 

package being employed as the data validation component of a rocket engine health management 

system. 

Four major modules compose the test-bed framework. The Test Manager Module defines the test 

conditions and controls the overall execution of the test sequence. Test data is provided by the Data 

Source Module. The Data Validation Module manages the system under test (SUT). The Analysis 

and Reporting Module evaluates the output from the SUT using the known test conditions and 

generates a series of reports summarizing the results.

The DQV Test-Bed was shown to be an effective tool for reducing development time and providing 

comprehensive testing of a health monitoring system for the RS-84 propulsion system. It provides an 

efficient avenue for assessing the strengths and weaknesses of prototype data validation and fault 

detection systems by improving the understanding of those systems’ capabilities and trade-offs. The 

DQV Test-Bed provides a potentially useful and important element of the necessary infrastructure to 

iteratively develop and flight-qualify health management systems for NASA Exploration Systems 

Program.

Ref: Sowers, T.S., Santi, L.M., Bickford, R.L., “Performance Evaluation of a Data Validation 

System”, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA 2005-4486, 

July 2005.

NASA/TM—2005-214026 8
Conclusion

The Controls and Dynamics Branch at the NASA Glenn Research Center has been involved in the 

development and application of critical health management technologies for aerospace propulsion 

systems for almost two decades. These technologies have been applied in real-time and non-realtime analyses and have included conventional and non-conventional techniques. The Branch has 

recently broadened its focus to include propellant and reactant feed systems, power distribution 

systems, and environmental control systems. While each subsystem has its own unique constraints 

and issues, there also exists an underlying commonality in the development and implementation of 

the their health management systems. Each subsystem requires that ...

• the health management system developers acquire extensive knowledge and that they 

develop an intimate understanding of the subsystem’s operation;

• health management system development take place in parallel with development of the 

monitored system to achieve optimal effectiveness;

• techniques in addressing optimum sensor placement, fault detection and isolation and 

information fusion, be developed and implemented based upon the unique constraint 

implied by the monitored system itself and imposed by the application or mission;

• each health management system implemented be verified and validated to the satisfaction 

of the systems’ designers and developers.

NASA Glenn’s has a rich legacy of health management research in both aeronautics and space to 

draw upon when developing Integrated Health Management Systems for future exploration 

missions. 

Ref: Garg, S., “Controls and Health Management Technologies for Intelligent Aerospace Propulsion 

Systems”, 42nd AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2004-0949, January 2004.

Glenn Research Center at Lewis Field 1

Propulsion HM Historical Perspective

• Optimal HM System Design Experience 

– Systematic Sensor Selection for MC-1, RS-83 and RS-84

– Testability Analysis for X-34 Main Propulsion System & Non-Toxic Reaction Control System

• Sensor Validation

– Data Qualification using Data Qualification Validation Studio (DQVS) for SSME, RS-83 and 

RS-84

– Sensor Validation for SSME, X-33 Aerospike 

engine & Integrated Propulsion Technology 

Demonstrator (IPTD)

• Automated Real-Time Diagnostic System

– Inverse Engine Model for MC-1, RS-83 and RS-84 

– Propulsion IVHM Technology Experiment (PITEX) for X-34 Main Propulsion System

– Propulsion Check Out and Control System (PCCS) for 

Integrated Propulsion Technology Demonstrator (IPTD)

– Atlas/Centaur Automated Diagnostic System (ACADS) monitoring and analysis of Centaur pneumatic system

• Automated Post Flight Diagnostic System

– Rocketdyne X-33 Aerospike Engine

– Post Test Diagnostic System (PTDS) for SSME ground tests

PTDS (SSME)

Automated Data 

Reduction/Feature 

Extraction (SSME)

Atlas/Centaur 

Automated Diagnostic 

System (ACADS)

PITEX (X-34)

PTDS (X-33)

PCCS (IPTD)

Sensor Selection, 

Validation, 

Inverse Model 

(MC-1, RS-83/84)

DQVS (SSME)

DQVS (RS-83/84)

Nearly Two Decades Experience Building and Deploying Health

Management Solutions for Power and Propulsion Systems

Nearly Two Decades Experience Building and Deploying Health

Management Solutions for Power and Propulsion Systems

ISHM Power 

& Propulsion 

ICP & ECP

1988 1992 2002 2004 1990 1994 1996 1998 2000 2006

NASA/TM—2005-214026 9
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15

Meeting the Challenges of Exploration Systems: Health Management

Technologies for Aerospace Systems With Emphasis on Propulsion

Kevin J. Melcher, T. Shane Sowers, and William A. Maul

Health management; Sensor selection; Data validation; Fault detection; Fault isolation;

Information fusion; Verification and validation

Unclassified -Unlimited

Subject Categories: 20 and 12

Prepared for the First International Forum on Integrated System Health Engineering and Management in Aerospace

sponsored by NASA Ames Research Center and NASA Marshall Space Flight Center, Napa, California, November

7–10, 2005. Kevin J. Melcher, NASA Glenn Research Center, e-mail: kevin.melcher@nasa.gov; and T. Shane Sowers

and William A. Maul, Analex Corporation, 1100 Apollo Drive, Brook Park, Ohio 44142. Responsible person, Kevin J.

Melcher, organization code RIC, 216–433–3743.

The constraints of future Exploration Missions will require unique Integrated System Health Management (ISHM) capabilities

throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for

repair or replacement, and long communication delays all require an ISHM system that can span distinct yet interdependent vehicle

subsystems, anticipate failure states, provide autonomous remediation, and support the Exploration Mission from beginning to end.

NASA Glenn Research Center has developed and applied health management system technologies to aerospace propulsion systems for

almost two decades. Lessons learned from past activities help define the approach to proper ISHM development: sensor selection—

identifies sensor sets required for accurate health assessment; data qualification and validation—ensures the integrity of measurement

data from sensor to data system; fault detection and isolation—uses measurements in a component/subsystem context to detect faults

and identify their point of origin; information fusion and diagnostic decision criteria—aligns data from similar and disparate sources in

time and use that data to perform higher-level system diagnosis; and verification and validation—uses data, real or simulated, to

provide variable exposure to the diagnostic system for faults that may only manifest themselves in actual implementation, as well as

faults that are detectable via hardware testing. This presentation describes a framework for developing health management systems and

highlights the health management research activities performed by the Controls and Dynamics Branch at the NASA Glenn Research

Center. It illustrates how those activities contribute to the development of solutions for Integrated System Health Management.