NIST ADVANCED MANUFACTURING SERIES 100-2

Measurement Science Roadmap for 

Prognostics and Health Management 

for Smart Manufacturing Systems

Prepared for

Brian A. Weiss

National Institute of Standards and Technology

Gaithersburg MD 20899

Prepared by

Joan Pellegrino

Mauricio Justiniano

Anand Raghunathan

Energetics Incorporated

Columbia MD 21046

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.AMS.100-2

NIST ADVANCED MANUFACTURING SERIES 100-2

Measurement Science Roadmap for 

Prognostics and Health Management 

for Smart Manufacturing Systems

Prepared for

Brian A. Weiss

National Institute of Standards and Technology

Gaithersburg MD 20899

Prepared by

Joan Pellegrino

Mauricio Justiniano

Anand Raghunathan

Energetics Incorporated

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.AMS.100-2

September 2016 

U.S. Department of Commerce

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 

Photo Credits

Report body:

Dollar Photo Club (DPC), 64341250 (pg.6), 68012349 (pg. 8), 65898405 (pg.16), 65797175 (pg.26), 74486360 (pg. 38)

DISCLAIMER

This report was prepared as an account of work cosponsored by NIST. The views and opinions expressed herein 

do not necessarily state or reflect those of NIST. Certain commercial entities, equipment, or materials may be 

identified in this document in order to illustrate a point or concept. Such identification is not intended to imply 

recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are 

necessarily the best available for the purpose.
Acknowledgements 

This report is based on the results of the Roadmapping Workshop: Measurement Science for Prognostics 

and Health Management of Smart Manufacturing Systems, held November 19-20, 2014, in Gaithersburg, 

Maryland. The workshop was sponsored by the National Institute of Standards and Technology (NIST), 

an agency of the U.S. Department of Commerce. 

Thanks are extended to the NIST organizers as well as the speakers and panelists who provided their 

perspectives on new technologies as well as the challenges impacting prognostics and health management 

(PHM) for smart manufacturing systems. We also appreciate the extensive contributions of the 

participants (listed in Appendix A); this report would not be possible without their valuable insights. 

Finally, thanks are extended to the Energetics Incorporated team for their assistance in facilitating the 

workshop and preparing this report.

Plenary Speakers (in order of appearance)

Howard Harary, NIST

Brian A. Weiss, NIST

Al Salour, The Boeing Company

Albert Wavering, NIST

Jay Lee, University of Cincinnati/Center for Intelligent Maintenance Systems

Panel 1: PHM Capabilities, Best Practices, Challenges, and Needs

Moderator: Gregory Vogl, NIST

Andrew Inman, Toyota

Carl Byington, Impact Technologies/Sikorsky

William Marscher, Mechanical Solutions, Inc. 

David Siegel, Predictronics Corporation 

Panel 2: PHM Performance Assessment – Monitoring and Measurement

Moderator, Moneer Helu, NIST

Harry Kekedjian, Ford Motor Company

Kai Goebel, National Aeronautics and Space Administration (NASA)

William Sobel, System Insights

John Oskin, Sage Clarity

Panel 3: PHM and the Human Element

Moderator: Patrick Brown, University of Cincinnati

Thomas Mooney, SOAR Engineering LLC

Andrew Hess, The Hess PHM Group

NIST Organizers

Brian A. Weiss, NIST

Gregory Vogl, NIST

Moneer Helu, NIST
i

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table of Contents

Acknowledgements ...................................................................................................................... vi

1. Introduction............................................................................................................................... 3

1.1. Overview of PHM..........................................................................................................................3

1.2. Background and Importance of Prognostics and Health Management ..........................................3

1.3. Roadmap Scope and Objectives.....................................................................................................6

1.3.1. Workshop Process ......................................................................................................................7

1.3.2. Prioritization Process.................................................................................................................7

1.4. Plenary and Panel Summaries........................................................................................................8

Plenary Talks ...........................................................................................................................................8

Panel 1: PHM Capabilities, Best Practices, Challenges, and Needs.....................................................10

Panel 2: Performance Assessment – Monitoring and Measurement......................................................12

Panel 3: PHM and the Human Element.................................................................................................13

1.5. References....................................................................................................................................14

2. PHM Manufacturing Process Techniques and Metrics ...................................................... 16

2.1. Overview......................................................................................................................................16

2.2. Goals ............................................................................................................................................16

2.3. Desired Capabilities .....................................................................................................................18

2.4. Challenges and Barriers ...............................................................................................................20

2.5. Priority Roadmap Topics .............................................................................................................22

3. PHM Performance Assessment ............................................................................................. 26

3.1. Overview......................................................................................................................................26

3.2. Goals ............................................................................................................................................27

3.3. Desired Capabilities .....................................................................................................................27

3.4. Challenges and Barriers ...............................................................................................................29

3.5. Priority Roadmap Topics .............................................................................................................31

4. PHM Infrastructure – Hardware, Software, and System Integration............................... 38

4.1. Overview......................................................................................................................................38

4.2. Goals ............................................................................................................................................38

4.3. Desired Capabilities .....................................................................................................................39

4.4. Challenges and Barriers ...............................................................................................................41

4.5. Priority Roadmap Topics .............................................................................................................44

5. Crosscutting Challenges......................................................................................................... 49

6. Next Steps ................................................................................................................................ 51

6.1. Machine Tool Linear Axes Diagnostics.......................................................................................52

6.2. Systems-Level Monitoring, Diagnostics, and Prognostics...........................................................53

6.3. PHM for Robotics........................................................................................................................54

6.4. Summary ......................................................................................................................................57

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
ii

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

7. Appendices............................................................................................................................... 58

Appendix A. Contributors....................................................................................................................58

Appendix B. Acronyms........................................................................................................................60

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
3

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

1. Introduction

1.1. Overview of PHM

Prognostics and Health Management (PHM) technologies are vital in today’s manufacturing environment. 

PHM can reduce the time and costs for maintenance of equipment or manufacturing processes, and 

minimize equipment/process fault and failure potentials, through the application of efficient, cost–

effective condition monitoring, diagnostic, prognostic, and maintenance tools. Another key benefit of 

PHM is that it can aid the optimization of overall equipment effectiveness (OEE); i.e., the ideal (yet 

virtually impossible) scenario of operating 100 % of the time at 100 % capacity, with an output of 100 %

good quality. Improved OEE means fewer equipment failures and reductions in stoppages, waste, rework, 

and start-up losses. Overall, the objective of PHM is to provide timely actionable information to enable 

intelligent decision – making for improved performance, safety, reliability, and maintainability.

1.2. Background and Importance of Prognostics and Health 

Management

Information and communication technologies are converging with many advanced and emerging physical 

technologies (including sensing, automation, machining, robotics, and additive manufacturing) to drive 

profound transformations to manufacturing, creating the next generation of industry. These Smart 

Manufacturing Systems (SMS) are paving the way for more efficient, automated, programmable, and 

flexible forms of manufacturing to meet changing consumer demands (Huizinga, 2014). The competitive 

advantage gained through the implementation of SMS has cross-cutting benefits to multiple industries. 

New studies show that for every one job created in a Smart Manufacturing plant, two, three, four or more 

“indirect” jobs are created outside the plant in companies to service, support and supply it (Bernaden,

2012). The importance of continued innovation in manufacturing, and especially advancing smart 

manufacturing systems, has been noted in numerous significant reports over the last several years, 

spurring continued investment and research (McKinsey, 2012; PCAST, 2011; PCAST, 2012; PCAST,

2014). As these reports note, advanced robotics, new information technologies, and sophisticated 

telecommunications capabilities are critical technological innovation areas for advancing manufacturing. 

The availability of large amounts of data (‘big data’) has also been cited as key to driving advances in

smart manufacturing, provided data can be turned into actionable insights or new operating 

improvements. 

The potential of smart manufacturing to revitalize U.S. manufacturing is driving new research

investments and initiatives. For example, a Digital Manufacturing and Design Innovation Institute 

(DMDII) has been launched as one of the innovation hubs in the new National Network for 

Manufacturing Innovation.1 The DMDII sponsors research projects in digital manufacturing and design; 

disseminates the lessons learned; and helps educate tomorrow’s manufacturing workforce.

2

 

1 www.manufacturing.gov

2 http://dmdii.uilabs.org/

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
4

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Investments in SMS across the globe are also taking advantage of these growth opportunities. For 

example, the Factories of the Future (FoF) Public-Private Partnership aims at helping EU (European 

Union) manufacturing enterprises and amounts to €1.15 billion over a seven-year period from 2014-2020 

(European Commission, 2013). Maintaining a domestic technical edge over the global competition will 

require focused actions and broad support.

The development of SMS is dramatically increasing 

the complexity of system, sub-system, and component 

interactions within manufacturing processes. It is 

challenging to determine if/when specific failures will 

occur, how the impact can be minimized, and how 

failures can be mitigated in the future. Prognostics and 

health management (PHM) combines sensing and 

interpretation of environmental, operational, and 

performance-related parameters to assess equipment 

health, predict remaining useful life (RUL), and 

diagnose failures (should they occur). In some cases, 

sensor readings are fed into one or more control 

systems that monitor potential fault and failure 

indicators such as vibration levels, flow rates, 

temperatures, chemical concentrations, and strains to 

provide real-time reporting to the equipment operators 

(Pecht, 2008). A potentially more effective approach is 

using PHM modules or data collectors to collect and 

analyze sensor data and other health information 

decoupled from the process or equipment’s control system. A separate PHM system offers more 

flexibility to add sensors, software, without the need to alter the control system, which can be difficult and 

expensive to change and re-qualify (Mooney, 2015). The basic components of a PHM system are depicted 

in Figure 1-1 (adapted from Pecht, 2008).

PHM for SMS can enable more efficient maintenance strategies such as Total Predictive Maintenance 

(TPM)3. These strategies engage all levels and functions in an organization to maximize the effectiveness 

of production equipment. PHM for smart manufacturing will facilitate smarter, safer, and more 

environmentally sustainable manufacturing processes. Estimates from the U.S. Environmental Protection 

Agency (EPA), which look at manufacturing efficiency as a means of improving environmental 

performance, show companies can realize a 15% to 25% increase in equipment efficiency rates within 

three years of adopting TPM strategies (EPA, 2011). 

As shown in Figure 1-2

4

, the total maintenance cost is impacted by the cost of repairs, the cost of 

proactive maintenance (e.g., PHM system installation and operation to proactively address equipment 

failures before they occur), and the cost of production losses and downtime. Good proactive maintenance 

programs can improve uptime, increase longevity, and improve maintenance cost control and operational 

safety. With the growing use of smart systems and networks in manufacturing, more equipment and 

process data is available for proactive asset management. Structured data obtained from equipment and 

3

Jay Lee and Edzel Lapira, “Smart: PHM algorithms help Total Predictive Maintenance step up its game.” Intelligent 

Maintenance Systems Center, University of Cincinnati, June 2011. http://www.imscenter.net/IMS_news/2011-tpm-gets-smartims.pdf

4 Life Cycle Engineering. Accessed 12/27/14. http://www.lce.com/pdfs/The-PMPdM-Program-124.pdf, 

Figure 1-1. General Prognostics and Health 

Management Design Components to Detect and 

Mitigate Equipment Failure

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
5

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

assets can be combined with more sophisticated analytics to create opportunities for better reliability, 

uptime, and longevity.

By detecting changes in performance, PHM can alert operators to interventions needed to maintain 

process optimization, subsequently improving OEE. The improved understanding of OEE made possible 

through PHM enables the plant to get the most out of process systems and can have a large impact on the 

cost-benefit model for PHM. As a result of OEE improvements, for example, PHM systems could be 

justified without considering cost reductions and avoidances (e.g., secondary damage).

For proactive maintenance to be 

effective, the potential failure modes of 

the equipment must be identified and 

precursors to failure monitored. 

However, as equipment, processes and 

operating environments evolve, failure 

modes and physics can change. PHM 

systems need to be open and adaptive to 

changes in all these areas. Complex, 

software intensive equipment and 

processes also do not lend well to typical 

physics of failure models. Developing

effective proactive systems and models 

requires a solid understanding of the 

physics of failure and how these may 

change as the environment and 

equipment changes. Applying that level 

of understanding into predictive 

algorithms and analytics, and 

incorporating it into training, can help 

operating personnel interpret pre-failure conditions and take corrective action. Analytics are also a key 

component of PHM. In some cases, analytics can root out complex behavior patterns better than physical 

models (Mooney, 2015).

As smart manufacturing expands in the United States, further design, verification, validation, 

implementation, and analysis of PHM systems will be needed. Measurement science plays an important 

role and can be used to enhance condition monitoring, diagnostics, and prognostics at many levels within 

a factory. For example, improvements in data management and analysis will play a key role in identifying 

signals among noise, reducing the occurrence of false positives. Additionally, better software will enable 

real-time, online equipment monitoring to provide more timely actionable intelligence. Measurement 

science will facilitate diagnostic- and prognostic-related information sharing among levels within a 

factory to minimize downtime, operating and maintenance (O&M) costs, and energy requirements.

Standards will provide guidelines for conducting development, testing, and implementation of PHM 

technology. Standards cover such aspects of the field as: terminology, definitions, methodologies, 

metrics, specifications, testing, software (SW), hardware (HW), monitoring, analysis, modeling, products, 

systems, reliability, safety, ratings, application, best practices, compliance, management, and training. 

The standards developed and approved through consensus will be the guidelines by which organizations 

Figure 1-2. The Maintenance Cost Paradigm: Total maintenance 

related cost = Cost of Proactive Maintenance + Cost of Repairs + 

Cost of Production Losses caused by Failures & PM Time (LCE 

2014).

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
6

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

can proceed with PHM implementation. Recent studies have been published that review a number of the 

key standards relevant to PHM (Vogl et al., 2014a; Vogl et al., 2014b).

1.3. Roadmap Scope and 

Objectives 

The National Institute of Standards and 

Technology (NIST) hosted the Roadmapping

Workshop: Measurement Science for Prognostics 

and Health Management of Smart Manufacturing 

Systems (PHM4SMS) at its Gaithersburg, MD 

campus on November 19-20, 2014. The event

brought together over 60 PHM experts from 

industry, government, national laboratories, and 

academia to identify measurement science 

challenges and associated research and 

development (R&D) needs for PHM in the 

context of smart manufacturing. The objectives 

of the workshop were to:

 Serve as a key building block for the creation 

of a measurement science roadmap for 

PHM4SMS research, by developing 

information on:

- Measurement science barriers, 

challenges, and gaps preventing

broad use of PHM4SMS

- Research and development (R&D)

needed to address the priority 

measurement and standards 

challenges

- Future measurement- and standardsrelated targets and goals for PHM

- Actions to overcome the high priority 

barriers 

 Inform future NIST technical programs and 

strategic planning

 Offer valuable information to other 

government agencies and stakeholders 

focused on PHM within manufacturing 

environments

The Roadmap for Measurement Science for Prognostics and Health Management of Smart 

Manufacturing Systems is based largely on workshop discussions as well as insights provided by speakers 

and panelists. The report is organized around three significant topic areas:

NIST Smart Manufacturing Operations Planning 

and Control Program

At the National Institute of Standards and 

Technology, the Smart Manufacturing Operations 

Planning and Control Program (SMOPAC) is 

conducting R&D on PHM systems to facilitate

the adoption of smart manufacturing (fullyintegrated, collaborative manufacturing systems 

that respond in real-time to meet changing 

demands and conditions in the factory, in the 

supply network, and in customer needs). The

overall program will aid in development of 

efficient networked sensing and control, 

prognostics and health management (including 

diagnostics and maintenance), integrated wireless 

platforms, industrial control security, efficient 

information exchange and interoperability of 

system components. Testbeds will be used to 

evaluate architectures, standards, and scale effects 

for reference implementations. The resulting 

manufacturing tools and resources will enable: 

increased efficiency of operations; manufacturing 

process development cycles at reduced risk, time 

and cost; and orders of magnitude expansion of 

manufacturing operations across increasingly 

diverse market segments. Additional information 

about the SMOPAC Program can be found at 

http://www.nist.gov/el/msid/syseng/smopc.cfm

Photo Credit: DPC 64341250 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
7

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

 PHM Manufacturing Process Techniques and Metrics – current and projected PHM techniques

and methods for application to smart manufacturing systems; metrics used to monitor system health

and enable PHM techniques; and the relationships between techniques and metrics.

 PHM Performance Assessment – current and future approaches to assess the performance of PHM

systems including (but not limited to) validation and verification of methods and uncertainty

quantification.

 PHM Infrastructure: Hardware, Software, and System Integration – current and future hardware

and software technologies (including sensors, controllers, models, and simulations) to enable or

integrate with PHM techniques.

The ideas presented here are a reflection of the attendees and not necessarily the entire industry. As such, 

they should be viewed as a representative sampling of the important perspectives, but not all-inclusive. 

The participants were selected based on their high level of technical knowledge related to PHM 

technologies, systems, and practices and are considered experts in the field.

This report details the findings from the first-ever event to focus on identifying the most pressing 

measurement science challenges and associated research and development (R&D) needs for PHM4SMS. 

The information is useful to both public and private decision-makers interested in furthering the 

capabilities of PHM and expanding its use in the smart manufacturing sector. It is envisioned that the 

national research agenda for PHM will incorporate some of the needs and challenges detailed in this 

report. Manufacturing and engineering personnel (plant managers, machine operators, reliability 

engineers, technology integrators, etc.) involved in making process improvements will also find this 

report useful. 

1.3.1. Workshop Process

Several key questions and focus areas were posed to gain insights on the important challenges and 

pathways to address them. 

Desired Capabilities

 Envisioned future: What capabilities are wanted and needed the most?

Challenges and Barriers for Achieving the Capabilities

 What technologies or other barriers limit development, implementation, and/or integration of

PHM?

 What are the measurement and standards barriers, challenges, and gaps?

Priorities

 What are the Research and Development (R&D), and standardization priorities?

Pathways for Measurement Science Roadmap

 Identification of the R&D activities, standards, and approaches for addressing priority challenges

 Next steps and actionable plan

1.3.2. Prioritization Process

Each group used a real-time voting scheme (5 votes per person, same value for each vote) to indicate 

which challenges were of the greatest priority to address. When voting, participants were asked to 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
8

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

consider a set of criteria, including 1) the challenges most urgent (timing) to address that would accelerate 

the development and use of PHM for smart manufacturing; and 2) challenges that would have the most 

impact on parameters important to manufacturing (i.e., productivity, equipment/process performance, cost 

effectiveness, and quality). After prioritizing the challenges as high, medium, or low, several of the higher 

priority challenges were examined more closely to create a roadmap for R&D, standards development, 

and other future efforts for PHM4SMS.

1.4. Plenary and Panel Summaries

Presentations from leading industry experts set the stage for workshop discussions. As permitted by the 

authors, presentations can be downloaded at http://www.nist.gov/el/isd/phm4sms-workshop.cfm. The 

presentations of speakers and panelists reflect unique opinions from their respective fields. Many of the 

themes identified were echoed during the breakout sessions and further expanded upon.

Plenary Talks

 Day 1 – Opening Remarks:

Howard Harary, Director, Engineering 

Laboratory (EL), NIST

Welcome and purpose: NIST welcomes 

you to provide your expert inputs on the 

challenges and needs for prognostics and 

health management for smart 

manufacturing systems. The NIST 

Engineering Laboratory (EL) is currently 

pursuing work in PHM in a number of 

areas, and your insights will help to guide 

our future efforts.

 Workshop Scope and Objectives: 

Brian A. Weiss, Project Leader –

PHM4SMS, NIST

Synopsis of workshop scope, process, 

and desired outcomes: NIST has a unique 

mission; measurement science plays an 

important role in the context of creating 

critical-solution enabling tools – including 

metrics, models, test methods, and 

knowledge – for U.S. manufacturers. 

Priorities and next steps need to be 

identified to address key measurement 

science needs, challenges, and gaps that are 

hindering the development and deployment 

of health monitoring, diagnostics, and 

prognostics technologies at multiple levels 

within a factory.

Successful Development and Implementation 

of PHM Systems: The Center for Intelligent 

Maintenance Systems (or IMS)

The Industry University Cooperative Research 

Center Program of the National Science 

Foundation (IUCRC/ NSF) has supported the 

development of the IMS since 2001. 

The Center collaborates with over 30 research 

institutions and over 70 industry partners 

worldwide – including Boeing, Ford, Toyota, 

and United Technologies. These companies 

validate, deploy, and benefit from PHM 

technologies such as embedded and remote 

monitoring, prognostics technologies, and 

intelligent decision support tools3. 

Every dollar invested in the IMS exhibits a 

benefit to cost ratio of 238:1 in return. 

Investments between 2001 and 2010 resulted 

in a returned value of more than $855M 

created by IMS knowledge and technology 

(Gray 2012).

Photo Credit: DPC #68012349

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
9

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

 Health Management of Smart Manufacturing Systems:

Al Salour, Senior Technical Fellow, The Boeing Company

Boeing experience with implementing prognostics and health management systems into

their manufacturing processes: Common standards and interoperability between systems will

benefit software-assisted diagnosis and prognosis. This is a challenge in a large organization

because business units have their own preferences and have even created unique software. One of

the main issues currently faced is the amount of time it takes to direct data towards a decision. A

lot of data is collected, yet current technology doesn’t provide enough actionable information.

Emerging tools and advancements are improving software-assisted diagnosis and prognosis to

provide more actionable information and facilitate quicker decision-making.

 Day 2 – Opening Remarks

Albert Wavering, Chief, Intelligent Systems Division (ISD), EL, NIST

Advances in intelligent manufacturing: The Intelligent Systems Division develops and

advances measurement science and standards to speed development, adoption, and integration of

leading-edge intelligent technologies that will improve U.S. manufacturing performance. A key

aspect of ISD’s work is enabling real-time monitoring, control, and performance optimization of

smart manufacturing. The Internet of Things combined with cyber-physical systems (highly

networked information technology/IT and physical systems) has the potential to change the face

of manufacturing. As the use of intelligent systems grows in this sector, new technologies and

strategies for prognostics and health management will be needed to keep pace and to take

advantage of new opportunities. This workshop is a good forum for gaining insights on how

PHM can adapt and advance in new smart manufacturing environments.

 Recent Advances and Transformation Direction of PHM:

Jay Lee, Professor at the University of Cincinnati, Founding Director of National Science

Foundation (NSF) Industry/ University Cooperative Research Center (I/UCRC) on Intelligent

Maintenance Systems

Evolution of advanced manufacturing facilities, product lifecycle management, and the

emergence of PHM: Despite improvements to PHM systems, the goal of achieving near-zero

breakdown has still not been achieved. In the next 10 years, PHM systems need enhanced control

and self-maintenance systems that are robust in the manufacturing environments of the future. In

advancing the deployment of PHM systems, it is crucial to learn from past mistakes and plan

what can be done in the future to make systems more valuable. Data collection, interpretation,

and storage will play an important role in the improvement of these systems.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
10

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Panel 1: PHM Capabilities, Best Practices, Challenges, and Needs

This panel session reviewed the current state of PHM

related to application in manufacturing environments. 

Panelists provided insights on PHM technology and 

systems, in particular current capabilities, best practices, 

and challenges, as well as technology gaps and 

limitations. The moderator and panelists are listed in 

Figure 1-3; key points are summarized below. 

 Andrew Inman, Toyota

Reliability and Maintenance

Toyota initiatives around reliability and maintenance and how to accelerate deployment of 

PHM within Toyota manufacturing plants: Machine maintenance is essential to maintaining 

efficient production of quality vehicle products that create consumer confidence. PHM efforts 

include the design of self-diagnosing machines and the use of effective process analysis to 

prevent failures and their reoccurrence. All of the systems have to work together to be effective; 

this is a challenge due to the differences in the systems and the vast amounts of data collected. 

Because manufacturing groups often work in silos, it is difficult to connect data and provide

feedback to various groups. Implementing a computerized maintenance management system 

(CMMS) may improve connectivity between the different groups and increase reliability.

 Carl Byington, Impact Technologies/ Sikorsky/ United Technologies

Best Practices in PHM and Application to Manufacturing

Hardware and software products being developed by Impact Technologies and how they 

are being integrated into Sikorsky helicopters and manufacturing processes: Safety and 

overall equipment effectiveness (OEE) are essential components of the Impact approach to Total 

Productive Maintenance (TPM). OEE takes into account all losses (down time, speed, and 

quality), resulting in a measure of truly productive manufacturing time. The value of prognostics 

is information and how it is used to improve production and reliability. One of the key concepts is 

moving away from reliability-centered maintenance towards condition-based maintenance + 

prognostics (CBM +). This approach involves utilizing the assessed health or diagnostic fault 

classifier output to predict a failure evolution. Typical manufacturing environments have rich data 

potential to develop greater prognostics. 

 William Marsher, Mechanical Solutions Inc.

Manufacturing Machinery PHM

Greatest challenges, needs, and opportunities for manufacturing PHM: Data is at the core of 

successful PHM. However, data collection and analysis is difficult to automate and can lead to 

false positives. Industry systems still require a human in the loop to analyze the data and verify 

problems. Developing better software to provide on-line monitoring with actionable information 

available in real-time would be difficult, but well worth it. Statistical data provides a good 

starting point, but machine/physics-based algorithms are necessary to put the information into a 

form that makes sense. With modern hardware, this software and information can be managed 

with a great degree of success to maintain control and reduce downtime. 

 David Siegel, Predictronics Corporation

Manufacturing PHM Successes and Challenges

Figure 1-3. Panelists for PHM Capabilities, 

Best Practices, Challenges, and Needs

Moderator, Greg Vogl, NIST

 Andrew Inman, Toyota

 Carl Byington, Impact Technologies/Sikorsky

 William Marscher, Mechanical Solutions, Inc.

 David Siegel, Predictronics Corp.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
11

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Overview of PHM manufacturing applications, past PHM successes in manufacturing, and 

the key challenges to PHM implementation: Obtaining the right data is essential to effective 

diagnostics and prognostics. PHM at the machine level is complicated by the multitude of 

operational settings. Normalizing this data to readily distinguish between routine operation and a 

problem is difficult. Additionally, data obtained from multiple places is often not uniform and 

maintenance records may be incomplete or unstructured. These inaccuracies and inconsistencies 

further complicate the use of historical data to develop and validate diagnostic and prognostic 

systems. 

 Highlights of Q&A

Human error and scarcity/cost of experts: Self-diagnosing machines are needed to prevent

failure through process analysis and prevent reoccurrence. Implementing more physics into PHM

software will reduce the need for experts to predict how a machine will behave, result in more

consistent prognostics, and expedite the process. Expert tribal knowledge can be better leveraged

in the design of PHM technologies which would significantly reduce the need and cost of human

expertise during equipment monitoring and diagnostics.

Systems communication and interoperability: Multiple systems, legacy equipment, and nonuniform software complicate PHM systems. Improving connections between systems; and

managing and interpreting the vast amounts of data collected are essential to PHM development.

Multiple data sources for understanding failure: It is difficult to obtain a sufficient training set

of data to determine when equipment failure will occur. In many cases, letting enough systems

fail to be useful for statistics is not an option. There are opportunistic failure events where we can

learn from failure but testing and modeling are important to deciphering and validating failure

data.

Real-time aspects of PHM systems: The need for real-time PHM systems represents a

significant development challenge. When there is a high value asset important to production, it is

useful to have real-time PHM to minimize fault and failure occurrences; and provide peace of

mind on equipment functionality. Some organizations aren’t ready for (or need) that level of

speed and can instead focus on other systems.

Key performance indicators (KPIs) at the component and system level: Most componentlevel KPIs are based on technical performance. The next level is effectiveness metrics and how

that manifests at a system level (e.g., maintenance hours/ operation hours), algorithm level, and

OEE at production level. There is a need to determine the appropriate KPIs to support PHM.

Security of PHM systems: This is a big concern, especially for the military. Information

assurance is an evolving concept, and a hurdle for those developing systems. Refineries and

power plants are reluctant to implement wireless technologies and are afraid of hacking. Wireless

technologies can make PHM capabilities more convenient. Convenience needs to be balanced

with developing a secure distributed architecture to mitigate cybersecurity concerns.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
12

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Panel 2: Performance Assessment – Monitoring and Measurement

This panel focused on techniques for measuring 

and/or monitoring how well PHM systems are 

performing their functions in smart manufacturing 

environments, as well as metrics that can be used to 

measure overall manufacturing performance (to 

isolate the impact of PHM technologies on 

manufacturing performance). The moderator and 

panelists are listed in Figure 1-4. The key points that 

emerged are summarized below.

 Harry Kekedjian, Ford Motor Company

Predictive Maintenance Strategy

Use of Predictive Maintenance (PdM) to assess the condition of in-service equipment and 

determine when maintenance should be performed: In theory, PdM provides cost savings over 

routine preventative maintenance, because maintenance tasks are performed only when 

warranted. In practice, limited plant resources and budgets make it challenging to perform 

maintenance tasks in a timely manner; and data collection and processing has been very 

monotonous and labor intensive. False positives are a common occurrence that need to be 

reduced. Information needs to be clean and clear, even when complex algorithms are involved, to 

enable rapid decision-making. PdM plans must make efficient use of limited resources and 

provide accurate, fast results to be effective. 

 Kai Goebel, NASA

Performance Assessment – Monitoring and Measurement

Performance assessments for prognostics systems: An effective prognostic algorithm for 

performance assessment requires multiple components. Important factors, such as minimizing 

downtime, must be embedded at an algorithmic level. A cost-benefit analysis and failure mode, 

effects, and criticality analysis (FMECA) will justify the investment of developing a prognostic 

system, including the identification of necessary sensors. Once in place, the prognostic system 

can be calibrated to characterize normal operations and fault modes. Prognostics metrics can then 

be used to evaluate, refine, and improve the ability of the PHM system to measure performance

(e.g., accurate and timely algorithm predictions for equipment end of life parameters).

 William Sobel, System Insights

Vimana, Predictive Analytics Platform for Manufacturing Intelligence

Development of predictive analytics for manufacturing: Most manufacturing processes are run 

sub-optimally from a tool and performance perspective. Now that improved sensors and 

technologies are becoming available, real-time data analytics, downtime and process 

improvement, and prognostics that go beyond machine health are possible. Presently, there are no 

common standards for the level(s) in which diagnostic and prognostic activities need to occur on

onboard equipment vs. in the cloud. Flex computing at System Insights (commonly referred to as 

fog computing), takes an integrated approach by performing big data analytics in the cloud, in 

addition to shop floor processing, to find optimal process parameters for use in production.

Figure 1-4. Performance Assessment –

Monitoring and Measurement

Moderator, Moneer Helu, NIST

 Harry Kekedjian, Ford Motor Company

 Kai Goebel, NASA

 William Sobel, System Insights

 John Oskin, Sage Clarity

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
13

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

 John Oskin, Sage Clarity

One View Methodology, Move from Static to Real-time KPIs

Moving from static to real-time prognostics metrics: Making a move from static to real-time 

KPIs can be costly in terms of both time and money. “Real-time” is a relative term, where 

timeliness could be monthly or hourly metrics, for example. KPIs for manufacturing 

performance, quality, safety, cost, and work order compliance need to be sorted according to 

factors such as their complexity, accessibility, and business priority. The transition often must 

occur as a multi-year strategy with an established roadmap to address the prioritized KPIs.

 Highlights of Q&A

Encourage suppliers to monitor equipment and capture data: It is difficult to push down 

PHM to the equipment of suppliers. The suppliers that implement PHM techniques will have a 

competitive advantage. Clarity is crucial when communicating PHM requirements to suppliers. If 

a requirement is vague, vendors will often interpret it as a minimum.

Justify the cost benefit of PHM systems: When looking at equipment that does not fail often, 

the cost benefit needs to be tied back to quality and safety– a healthy machine builds healthy 

parts. PHM system developers can also participate in warranty guarantees of their services to 

increase manufacturer confidence in PHM (e.g., any unpredicted maintenance is free).

Panel 3: PHM and the Human Element

This panel focused on the impact and interpretation of human 

decisions on PHM in smart manufacturing environments, and 

challenges to be faced when humans interact with PHM

technologies. The moderator and panelists are listed in Figure 

1-5. The key points that emerged are summarized below. 

 Thomas Mooney, SOAR Engineering LLC

Integration of PHM into business operation systems: Traditional PHM systems are too often 

isolated, stand-alone systems based on simple cause-and-effect models. PHM is not just a 

technical system where the human interaction and user experience pieces can be trivialized. The 

PHM environment needs to be flexible to accommodate the inclusion of various people, 

organizations, training, and leadership. There are plenty of sensors and data capable of providing

actionable information. Systems can be integrated to create value for all participants in the value 

chain and enable evidence-based decision making. The best PHM systems are not fully human or 

fully autonomous, but a hybrid where the machine intelligence augments the human as an ally, 

and not an adversary. It is important that PHM systems learn, adapt, influence (e.g., decisionmakers), and communicate across the organization. 

 Andy Hess, The Hess PHM Group

Better asset management through improved information on actual equipment condition:

Predictive prognostics, along with condition management, provide critical information, leading to 

a significantly more effective and beneficial integrated asset management system. Factors such as 

warranty guarantee tracking, prognostics, logistics, and CBM+ management play an important 

role in and are key enablers for new business practices and decisions. All industry sectors warrant 

some PHM and stand to benefit at various levels. For example, a 1 % improvement in corrosion 

monitoring through PHM could save billions of dollars worldwide. Operator buy-in is needed to 

effectively develop, apply, and manage these systems to yield the greatest impact.

Figure 1-5. PHM and the Human Element

Moderator, Patrick Brown, University of Cincinnati

 Thomas Mooney, SOAR Engineering LLC

 Andrew Hess, The Hess PHM Group

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
14

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

 Highlights of Q&A

Improve understanding of refurbished equipment: It is difficult to estimate the remaining life 

of refurbished equipment. Replacing parts within a larger system complicates the ability to 

predict when the system may fail. PHM with inventory tracking could note the differences in 

remaining useful life between individual parts – but this requires extensive record keeping.

There are broad applications for PHM systems that decision-makers will support: Target 

systems for PHM are often the most expensive and critical components of the manufacturing 

process, for example where costly process disruptions or creation of unsafe working conditions

and liabilities are vital to avoid. However, some products or processes contain non-expensive 

components for which their failure could lead to devastating consequences (i.e., lithium-ion 

batteries on an airplane) – PHM has important applications in this domain, as well.

PHM is best implemented and sold during the installation of the manufacturing process: It’s 

always cheaper to implement a PHM system in the beginning (e.g., when starting a new 

design/process) rather than after the fact. Determining the level of performance required for the 

PHM system in the beginning can help to determine how varying levels of maintenance and 

downtime will impact production and profitability. Tying reduced maintenance costs and 

productivity increases to PHM can help demonstrate the value of implementing a PHM system 

from the start. 

The human element of buy-in and support for PHM should not be overlooked: The 

motivation to utilize a PHM system must come from upper management, whom are more likely to 

endure the efforts to implement a long-term savings strategy than middle management. Once 

implemented, experience indicates that local operators and others within the organization will 

realize that the data gives them great feedback and they quickly learn how to operate the system. 

It is becoming more difficult for humans to validate software: As software code becomes 

more complex, it is becoming harder to validate and humans are becoming less involved in this 

validation process. It is also difficult to verify and validate systems that include people because 

the systems are dynamic and subject to change. Additionally, the longer a complex code is used,

the more likely it is to generate/produce errors. PHM is needed for software to maintain a 

system’s flexibility to accommodate changing conditions. 

1.5. References

Bernaden, John., 2012. Indirect Jobs: A direct way to talk about why we need smart manufacturing. 

Rockwell Automation. http://smartmanufacturing.com/wp-content/uploads/2012/01/SmartManufacturing-increases-indirect-jobs.final_.pdf

EPA, 2011. Lean Thinking and Methods, Total Productive Maintenance. Environmental Protection 

Agency. Accessed January 3, 2015. http://www.epa.gov/lean/environment/methods/tpm.htm

European Commission, 2013. Factories of the Future PPP: Towards competitive EU manufacturing.

http://ec.europa.eu/research/press/2013/pdf/ppp/fof_factsheet.pdf

Gray, D.O. et al., 2012. Measuring the Economic Impacts of the NSF Industry/ University Cooperative 

Research Centers Program: A Feasibility Study. Prepared for National Science Foundation (NSF) 

by North Carolina State University. Final Report.

Huizinga, G. Walison, P. van der Beek, H. Tops, P. Wijnstok, J. Heemskerk, P. van Os, M. van der Horst, 

T. Helmer, S. Huveneers, S. Butter, M. van der Zee, F. van Oort, S. Ypma, J. Mulder, G.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
15

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Kotternink, B. de Heide, M. Grosfeld, T., 2014. Smart Industry, Dutch Industry Fit for the Future. 

http://www.smartindustry.nl/wp-content/uploads/2014/07/Opmaak-Smart-Industry.pdf

McKinsey 2012. Manufacturing the future: The next era of global growth and innovation. McKinsey 

Global Institute, Global Operations Practice. November 2012.

http://www.mckinsey.com/insights/manufacturing/the_future_of_manufacturing 

Mooney, T., 2015. Personal Communication. Soar Engineering, LLC.

Pecht, M., 2008. Prognostics and Health Management of Electronics; Wiley-Interscience: New York, NY, 

USA.

PCAST 2011. Report to The President on Ensuring American Leadership in Advanced Manufacturing. 

Executive Office of the President, President’s Council of Advisors on Science and Technology 

(PCAST). June 2011.

PCAST 2012. Report to the President on Capturing Domestic Competitive Advantage in Advanced 

Manufacturing. Executive Office of the President, PCAST. July 2012.

PCAST 2014. Report to The President: Accelerating U.S. Advanced Manufacturing. Executive Office of 

the President, PCAST. October 2014.

Vogl, Gregory W., Weiss, Brian A., Donmez, M A., 2014a. “Standards for Prognostics and Health 

Management Techniques within Manufacturing Operations.” Annual Conference of the PHM 

Society, September 29-October 2, 2014.

http://www.nist.gov/customcf/get_pdf.cfm?pub_id=916376

Vogl, Gregory W., Weiss, Brian A., Donmez, M A., 2014b. “Standards Related to Prognostics and Health 

Management (PHM) for Manufacturing.” NIST Interagency/Internal Report (NISTIR), July 7, 

2014. http://www.nist.gov/manuscript-publication-search.cfm?pub_id=915550

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
16

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

2. PHM Manufacturing Process

Techniques and Metrics

2.1. Overview

Section 2 presents the Roadmapping Workshop results of a breakout group focused on the topic of PHM 

Manufacturing Process Techniques and Metrics. The section summarizes the discussions and activities 

performed by this breakout group. Participants identified goals, desired capabilities, challenges and 

barriers, and priority roadmap topics. A list of participants from all breakout groups is shown in 

Appendix A.

PHM techniques can have a dramatic impact on manufacturing operations by enabling pre-fault/prefailure maintenance actions based on need rather than the use of schedules (e.g., where maintenance is 

performed by time intervals irrespective of need). PHM enables a determination of the current system

condition via diagnostic analyses, and provides insight into expected future conditions through prognostic 

methods. 

PHM manufacturing process techniques and metrics encompasses a wide spectrum of process and 

equipment condition monitoring technologies and systems; the factors influencing conditions; issues 

affecting equipment criticality (e.g., cost of machine downtime, replacement costs); relevant condition 

monitoring parameters (such as temperature, pressure, and vibration) for various machine types; and 

correlation of possible faults and failures. PHM also supports smart manufacturing, where systems and 

equipment are highly-interconnected, by integrating

maintenance operations via data collection, diagnostics, 

prognostics, and usage monitoring.

In a smart manufacturing environment, PHM process 

techniques must consider entire systems rather than 

individual components. This adds to the complexity of 

PHM and necessitates advances in standards, data 

collection and analysis techniques, data management, 

system training, and software interoperability. As 

information and communications technologies become 

more integrated into manufacturing environments, the 

need for advanced PHM, including supporting standards 

and data management, will become increasingly important.

2.2. Goals 

The goals identified for PHM manufacturing process techniques and metrics emphasize condition 

monitoring, substantial improvements in reliability (reduction in non-scheduled breakdowns), improved 

Industrial laser cutting process

Photo Credit: DPC #65898405

__________________________________________________________________________________

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
17

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

safety, operator situational awareness, and overall equipment effectiveness (OEE). Table 2.1 provides 

highlights of the goals identified, organized by major emphasis category.

The objective would be to enable PHM techniques that are capable of evolving appropriately as new 

manufacturing equipment or processes are introduced. PHM systems should ultimately support enhanced 

product quality via improved manufacturing performance, i.e., PHM can be used to enhance product and 

process resiliency over the life cycle. Knowledge of what is failing or negatively impacting KPIs is 

essential to learn what can be translated into product and process improvements for existing or future 

systems. PHM system evolution can also lead to varying degrees of autonomy, enabling systems to selfmonitor and heal. 

Table 2-1. Goals for PHM Manufacturing Process Techniques and Metrics

NEAR-TERM

(1 to 2 Years)

MID-TERM

(3 to 5 Years)

LONG-TERM

(5 + Years)

Equipment Effectiveness / Monitoring

Technology 

 100% OEE

 Projection of factory environment/ 

situations 

─ Time-based using current situation, 

yet able to provide intelligent, 

dynamic, priority settings

─ Hypothetical prognostics that are 

scenario-based

 Cost-effective PHM systems and 

technologies

 Automated early detection and 

diagnostics that can be applied to an 

entire fleet, including robots, and 

machine tools

Data and Sensing/Control

 Seamless interconnection of databases 

─ Open, transparent data protocols

─ 100% actionable information –

enterprise-side and supply chain 

(notification, alerts, condition 

assessment, root cause, etc.), with a 

connection to spares/stores ordering

─ Common definitions for PHM-related 

terms

─ PHM-related information exchange 

(machine-performance info) 

between companies and equipment

manufacturers

 Accessibility to data and 

translations/interpretations for both 

systems and subsystems

Equipment Effectiveness/ Monitoring 

Technology

 PHM fits effectively into traditional steps

─ Calibration

─ Preventive maintenance vs. cyclebased (condition-based maintenance) 

 Better machine information sets (better 

thresholds)

 Robust, flexible system-wide prognostics

 Easily expandable, scalable PHM

 Reduction in acceptance testing 

requirements by evaluating process 

effects on product quality online

Data and Sensing/Control

 Sensor and data acquisition (DAQ) costs 

reduced by an order of magnitude over 5 

years

 Sensor and DAQ resolution/ sample rate 

increased by an order of magnitude at 

same or lower cost

 Sensor and data extraction (self-aware) 

covering 80% of critical data

Safety and Reliability

 Near-zero manufacturing line breakdown 

(non-scheduled)

 100% safety record

Operator Usability/Situational Awareness

 Total situational awareness

 Effective human-machine collaborations

Design and Life Cycle PHM

 PHM linked integrally into system design 

and product life cycle design

Equipment Effectiveness/ 

Monitoring Technology

 Autonomous systems 

(self-monitoring/healing)

 90% automated process 

for translating sensory 

data from manufacturing 

systems to actionable 

maintenance tasks

 Detection of any feature of 

interest with 1-2 sensors

Design and Life Cycle PHM

 Cradle to grave PHM, 

enabling enhanced 

product and process 

resiliency the life cycle; 

ability to predict impacts of

process changes on

products.

 Past history of equipment 

is linked back to 

manufacturing

 New machines, lasers, 

and tools can predict what 

happens rather than

retesting; systems are

“self” aware

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
18

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

2.3. Desired Capabilities 

A number of key capabilities were identified as important to support PHM for smart manufacturing 

processes; these are outlined in Table 2.2 and summarized below. Priority for capabilities was 

determined using a real-time voting scheme, described in Section 1.3.1, Workshop Process.

Robust PHM Systems

Robust PHM systems are needed to support increasingly complex and interconnected smart 

manufacturing systems. These PHM systems would ideally be reconfigurable, scalable, have robust

prognostics and diagnostics, operate in real-time, and be accessible on-line. They would include 

dynamically-reconfigurable tools with the capability for scenario-recognition and awareness to predict 

conditions and situational awareness. 

Agility is also a key element for robust PHM systems. This would include flexibility in configuration;

sensing and data analytics; and the ability to rapidly adjust to new hardware and software as the 

manufacturing process evolves. Distributed PHM concepts and standards need to be developed to support 

agility and flexibility. Smart sensing capabilities will be needed for machine collaboration related to 

PHM.

Data and Sensing/Controls

A high priority is capabilities that allow for the effective, secure collection, management, and translation 

of data into actionable information. Universal interfaces for machine data (e.g., machine readable), 

communication protocols, and data standards are key aspects. Standards are needed for the common 

measurement and classification of data. Data acquisition and storage capabilities are required at a 

sufficiently high resolution, while preserving the meaning of data. Semantic recognition of data and links 

between records should be coupled with relational knowledge preservation. Integrated networks and 

interoperability will be required to enable “seamless data flow” of communications for PHM. Such 

systems should also be efficient (e.g., only handle data once). Guidelines are needed to identify what 

specific data is important to extract the most value from all types of data and prioritize data. While large 

volumes of data are possible, not all of the data is critical to decision-making. Better “Big Data” 

distributed query and analytical tools are needed to enable plant-wide PHM; and intelligent analytics are 

necessary to determine what data is most relevant (e.g., universal interface semantics for data).

Data systems need to be coupled with cost-effective sensing and control systems that support PHM.

Control systems should provide real-time, closed-loop feedback and be flexible (adjustable); these can 

support ‘smart’ or intelligent machines that are self-aware and ultimately have the ability to self-adjust 

based on PHM inputs. Self-aware sensors and/or measurement systems will be needed to ensure data 

quality meets requirements for actionable decision-making. 

Integration of PHM into enterprise-wide functions (planning, production, resource allocation, etc.) is 

another desirable capability. Enterprise planning machine data acquisition could encompass machine 

work allocation, machine maintenance optimization, supplies ordering, resource planning, and more. This 

would support user-friendly decision tools that enable just-in-time scheduling for problem avoidance. 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
19

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 2-2. Desired Capabilities for PHM Manufacturing Process

Techniques and Metrics

Robust PHM Systems

High Priority  None identified

Medium Priority

 Reconfigurable and robust systems

 Distributed PHM concepts-standards to support agile systems, including distributed capabilities in

sensing and analysis

Lower Priority

 Bill of materials and maintenance records of all manufacturing process components and subsystems as part of product “birth record”; unique identification and association for parts/assembles

to enable electronic tracking

 Mobile tools and devices with on-board analysis capabilities

 Easy-to-use tools to enable self-aware machines and processes in a complex environment

 PHM standard similar to the International Standards Organization (ISO)

 Closed-loop PHM for improved process design using process life cycle information

 Human-robot (machine) collaborations, where humans and machines interact/learn

Data and Sensing/Controls

High Priority  None identified

Medium Priority

 Advanced data collection, management, and analytics for machine and process data, including ‘Big

Data’ (acquisition, storage, universal machine data/communications interfaces, data standards,

seamless data flow/analytics)

 Integrated, low-cost, lower-level DAQ systems coupled with cost-effective sensing, diagnostic and

prognostic systems and devices

 Integration of design, control, production, planning, product knowledge/information and diagnostics

into PHM (enterprise planning machine DAQ)

 Real-time, closed-loop feedback and adjustable control of manufacturing process supporting

intelligent machines that are self-aware and self-adjusting

 Cybersecurity protocols for sensors, network and development

Lower Priority

 Black box data capture at lower system levels

 Personalized processes that adapt to incoming variation in parts

 Metrology and sensing from image/video data (e.g., camera inventory); with perception beyond

pattern recognition, color intensity, etc.)

 Plug-and-Play devices and software to capture PHM

Models

High Priority  None identified

Medium Priority

 Combinable, physics-based operating models of common manufacturing components (e.g., pumps,

motors, pallet stop, paint spray)

 Combined physics-based and data-driven approaches

Lower Priority  None identified

Metrics

High Priority  None identified

Medium Priority

 Accuracy of PHM (e.g., improved uptime and availability, reliability, throughput availability)

 Statistics supporting metrics (e.g., fail-over rates, how long system can run without failure,

reliability, longevity of sensor)

 Flexible, reconfigurable metrics that adjust to the decision regime (e.g., metric options)

 Linking of specific technical parameters and high-level KPIs

 Metrics tied to specific goals (e.g., near zero disruption)

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
20

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 2-2. Desired Capabilities for PHM Manufacturing Process

Techniques and Metrics

 Metrics that are related to actual features of the process

 Data quality metrics (e.g., utility of data)

 Metrics to evaluate sensor bias, precision, reliability, accuracy

 Machine degradation (algorithms) metrics; assesses predictive power of algorithms

 Metrics connected with visualization tools (e.g., radar chart, graphics, dashboards)

 Metrics/standards for different levels of the enterprise system

Lower Priority  None identified

Metrics

A range of metrics were identified as supportive of PHM. Key characteristics of the metrics shown in 

Table 2.2 include measurement of: 

 overall accuracy of PHM systems

 flexibility (i.e., ability to adjust metrics as needed)

 performance related to goals, technical parameters, or KPIs

 unique machine/process features

 data quality and utility

 sensor performance and utility

Metrics are recommended at many levels, from machine to enterprise. New capabilities will be needed to 

enable measurement of some of the parameters feeding into key metrics.

2.4. Challenges and Barriers 

A number of challenges and barriers were identified that impede the effectiveness of PHM and 

development of advanced PHM for smart manufacturing. These are outlined in Table 2.3 and summarized 

below.

Data and Sensing/Controls

Data collection and extraction of useful information from data are impacted by a number of challenges.

Interoperability between systems (e.g., sensors, data, communications, machines) remains a significant 

issue and needs to be addressed to enable further advances in PHM for smart manufacturing. The quality, 

utility, and availability of data are insufficient for numerous reasons. Automated data collection is limited, 

which means the captured data can be unreliable. Continued proliferation of manual processes is a 

contributor; data collected from manual process is generally low resolution and inaccurate, making it 

difficult to build a good picture of the overall process. Manual processes typically require expert human 

setup, tuning, configuration, and interpretation of data. 

While technology exists to collect data, effective methods are not always available to track or interpret the 

data (or translate the data into actionable information). New technologies and methods are required to fill 

this gap. Sharing of PHM data and best practices is also problematic; disparate data formats, standards, 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
21

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

and customization of PHM make it difficult to share information among companies and even within 

enterprises.

Predictive Models

Prognostic techniques are relatively immature (i.e., they are still in the development stage), so models 

may lack sophistication, validation, and applicability. A major challenge for predictive models to support 

PHM is the lack of good data for validation of prediction (e.g., interpreting what deviation from baseline 

means). Models for prognostics/prediction, especially those linked to highly-interconnected systems with 

multiple components, are inherently complex and more difficult to integrate and validate. Networked,

predictive machine diagnostics are significantly lacking today. 

Table 2-3. Barriers / Challenges for PHM Manufacturing Process

Techniques and Metrics

Data and Sensing/Controls

High Priority  Lack of interoperability between sensors, data formats, and communications

Medium Priority

 Data meaning that is lost rather than captured

 Lack of real-time data

 Pervasiveness of manual processes in some industries; low resolution and inaccuracy of data

from manual processes; human requirements for setting up/tuning/interpreting data PHM in

manual processes

 Technology exists (e.g., accelerometer) but good methods to track/interpret data are lacking

 Limited sharing of PHM best practices, data, success stories, and training due to disparate

standards and information formats and business-sensitivity; customization of manufacturing

systems contributes to low re-usability of PHM analytics and ability to share results

 Lack of flexibility of design of manufacturing systems, DAQ systems, software to enable tradeoffs for flexibility and robustness

Lower Priority

 Lack of decision support for actionable/PHM insights for maintenance technicians and nontechnical operators

 Lack of off-the-shelf PHM products, technologies, and devices that can integrate sensor and

other data for PHM; economy of scale is lacking

Predictive Models

High Priority  Early development stage of prognostics; cost-benefits are unclear

Medium Priority

 Lab experiments and validations that do not match real environment tests

 Limits to prognostic and predictive power – models of processes are incomplete, not well

validated, and lack detail at the right level for prediction

 Complexity of systems modeling

 Lack of networked, predictive machine diagnostics

Lower Priority  None identified

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
22

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

2.5. Priority Roadmap Topics 

Based on the major challenges and desired capabilities identified, three priority roadmaps were developed

to support PHM manufacturing process techniques and metrics, as outlined below. Addressing these 

roadmaps will enable progress toward development and integration of PHM into smart manufacturing 

processes.

 Advanced Sensors for PHM in Smart Manufacturing – a multi-stage approach to sensor 

development, beginning with assessing available sensors and identifying gaps relevant to PHM 

applications; gaps would be addressed through development of new sensors and sensing 

capabilities and standards (Figure 2-1).

 PHM Data Format, Taxonomy and Architecture – development of data taxonomy and standards 

to support PHM across manufacturing sectors; the end result will be a warehouse of data and 

supporting standards for format, storage, organization, and interfaces (Figure 2-2).

 Enterprise-Wide PHM for Maintenance Planning – creates PHM systems that integrate data for 

maintenance planning with parts and other logistics; incorporates human machine interface 

(HMI) for decision support, with the potential for significant improvements in OEE (Figure 2-3). 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
23

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Limited understanding of the full spectrum of sensor capabilities, interfaces, and interoperability requirements for 

PHM; current PHM systems lack re-configurability, flexibility, scalability, and robustness to support dynamic re-tasking of 

machines, volume and process changes, and product variation typical of manufacturing enterprises.

Approach: This multi-step approach will develop an inventory of current sensors and identify gaps for PHM in terms of 

interoperability, scalability, flexibility, configuration, and other factors. Accessible libraries and taxonomies will then be 

developed for sensor use cases. Finally, multi-purpose sensors and related standards will be developed to achieve flexible and 

reconfigurable PHM systems.

FIGURE 2-1. ADVANCED SENSORS FOR PHM IN SMART 

MANUFACTURING

Stakeholders & Potential

Roles 

PHM Users Provide input, validate

use cases

PHM Providers/ 

Tech Developers

Prototyping, tool 

development

Technology 

Integrators

Validated test; sensors, 

software (SW), integration

Academia Conduct analysis,

develop SW, algorithms

Standards 

Committees

Develop standards,

pursue consensus

Government Support efforts, provide 

repository

Other Facilitate input, standards 

development

Benefits / Impacts

Impact Benefit

High Improves 

reliability/reduces failures:

Purpose of PHM

Medium Reduces costs: Industry 

payback over time

Medium Accelerates innovation: n/a

High Enhances industry 

competiveness: Improved 

capability

High Speeds process reconfigurability: Enabling 

technology 

High Improves maintenance 

scheduling: Purpose/ 

function of PHM

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Medium

Complexity of R&D High

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption High (but slow)

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

 Inventory sensors and DAQ interfaces used in /

needed for PHM systems (e.g., vibration,

temperature, torque, current)

 Describe/define re-configurability requirements for

typical manufacturing applications/operations

 Develop standards for data communications and

data analytics

 Repository of sensor

capabilities

 Gap definitions – scalability

 Gap definitions – reconfigurability

 75-80% of sensors included in

survey

 Assessment of >50% of known

current systems

 Pre-requisite for interoperability

and new systems

3-5

years

 Quantify gaps in re-configurability by development

of scenarios and use cases for scalability and

interoperability

 Identify gaps in sensor and DAQ capability and

interoperability

 Describe/define scalability requirements for

various manufacturing applications

 Library of use cases (full

spectrum)

 80% of plausible reconfigurations

and rescaling covered

5+ 

years

 Develop multi-purpose sensors/DAQ /interfaces

for use in manufacturing PHM systems

 Develop standards for data communication, data

analysis, prognostic algorithms

 Develop taxonomy of PHM systems and

capabilities

 At least 1-2 sensors for each

data type; all interfaces

supported in DAQ

 PHM handling warehouse of

tools created

 Taxonomy library

 Consensus achieved among

stakeholders

 Flexible and reconfigurable PHM

systems

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
24

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Lack of interoperability for sensors/data formats and types of communication; preservation of data meaning/semantics.

Approach: This roadmap will create the data taxonomy for PHM covering formats, storage, organization, semantics and other 

elements. Standards will be developed to support taxonomy as well as data interfaces and integration. The taxonomy and architecture 

will be applied to establish a data warehouse of PHM data sources.

FIGURE 2-2. PHM DATA TAXONOMY AND ARCHITECTURE

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

 Identify taxonomy data types and

dimensions (sources: design, machine,

process, product, materials)

 Create guidelines for data format,

storage, and organization (streaming,

stored); include semantic data

requirements (numeric, text, static vs

dynamic, etc.)

 Incorporate considerations for data

interfaces and data integration

 Requirements for taxonomy

 Formal description of PHM data

taxonomy

 Identification of sponsors

 Cover 90% of data sources from

outstanding industries

3-5

years

 Develop standards for semantic PHM

data

 Create tools to capture and organize

data, extract, and visualize information

(e.g., temperature, xyz-specific, value,

time-based, etc.)

 Major workgroups formed

 Taxonomy validated and tested

according to use cases/pilots by

participating industries

 Specification of data models

 Agreement across domains/

industries

 Applicability to 75% of industry

5+ 

years

 Create, organize, and manage data

repositories

 Data warehouse creation

 Expansion of repository and data

sources

 Used by businesses/ other

organizations

Stakeholders & Potential

Roles 

PHM Users Provide/use data, 

contribute to taxonomy

PHM Providers/ 

Tech Developers Provide/use data

Technology 

Integrators Use data

Academia Aggregate, collect data

Standards 

Committees

Develop formats,

standards

Government Coordinate efforts

Other

Benefits / Impacts

Impact Benefit

Medium Improves reliability/reduces 

failures: Supports failure 

prediction

Medium Reduces costs: Improves 

maintenance functions

Accelerates innovation: n/a

Medium Enhances industry 

competiveness: Reduces failure 

incidence, lowers costs

High Speeds process reconfigurability: Provides data to 

support process changes

High Improves maintenance 

scheduling: Improves prediction of 

maintenance requirements

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Low

Complexity of R&D Low

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
25

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Collection and analysis of data from various manufacturing sources (programmable logic controller (PLC), sensors, 

human). Limited methods for identifying the key data; difficulty interpreting meaning when processes change (actionable 

information) within context of the process change (contextualized data); lack of database with consistent format and data 

architecture (build, plan, definition, integration of data).

Approach: In the first phase, the impact of system elements with respect to breakdown will be prioritized and a maintenance 

planning PHM methodology developed. The second phase would integrate enterprise-wide logistics and real-time 

information. Algorithms will be needed to support multi-objective optimization with changing inputs and elements, analysis for 

performance assessment, and finally feedback and control.

FIGURE 2-3. ENTERPRISE-WIDE PHM FOR MAINTENANCE PLANNING

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

 Investigate current state-of-the-art

for functional descriptions of

system, manufacturing data flow

 Functional description of system elements with

inputs/outputs, connections to all elements and

contextual information

 Testbed demonstration of data architecture with

data collection (system and sub-system)

 Initial outlining of analysis and optimization linking

logistics and factory data

 At least 1 year of data

collected for at least 1

manufacturing system (or

large sub-systems)

3-5

years

 Develop algorithms using test-bed

data

 Algorithms for maintenance planning

 HMI visualization/dashboard for decision-support,

presenting, and optimization of solutions

 Demonstration of human machine interface (HMI)

tools user algorithms

 5% improvement in OEE

5+ 

years

 Develop GPS of where plant

elements, including tools, are

located in the plant; develop RFID

of parts for tracking across-theboard consistently

 Algorithms for near real-time actionable

information (in-process) and maintenance

planning (post and pre-process); industry 5.0

 Artificial-intelligence for self-monitoring

 HMI tools for near real-time Q&A using functional,

contextualized data

 10% improvement in OEE

Stakeholders & Potential

Roles 

PHM Users Provide data, test/deploy 

system, develop metrics

PHM Providers/ 

Tech Developers

Provide data, test /deploy 

system, develop metrics

Technology 

Integrators Test/deploy system

Academia Develop/test algorithms

Standards 

Committees n/a

Government Develop/test algorithms

Benefits / Impacts

Impact Benefit

Medium Improves reliability/reduces 

failures: Supports better failure 

prediction tools

Low Reduces costs: Balances costs 

enterprise-wide

Low Accelerates innovation: Eases 

adoption of new technology

Low Enhances industry 

competiveness: Reduces 

maintenance costs, bottom line

Speeds process reconfigurability: Support agility 

(and process changes)

High Improves maintenance 

scheduling: Supports better 

maintenance planning

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Medium

Complexity of R&D Medium

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
26

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

3. PHM Performance Assessment

3.1. Overview

Section 3 presents the Roadmapping Workshop results of a breakout group focused on the topic of PHM 

Performance Assessment. This includes assessing the performance of the PHM system itself, as well as 

the supporting technologies, data, and metrics needed to support such assessments. The section 

summarizes the breakout group’s discussions and activities. Participants identified goals, desired 

capabilities, challenges and barriers, and priority roadmap topics. A list of participants from all breakout 

groups is shown in Appendix A.

By predicting reliability, and monitoring and recording health relative to normal operating conditions,

PHM can help manage the safety and cost of manufacturing systems while improving manufacturing 

operations and product quality. A key aspect of PHM is assessing how well prognostics are performing 

their function.

Effectively assessing the performance of PHM requires standards, measurement capabilities, and systemrelevant metrics. The objective is to assess the performance of PHM systems relative to their predictive 

and monitoring capabilities, identification of failure or other events, and success in supporting effective 

maintenance practices and/or the application of corrective actions. The performance, in many cases, is 

dependent on the accuracy of the algorithms and predictive models underlying the PHM system.

One example is the accuracy of the prognostic 

horizon, which is defined as the difference between 

the time index when prognostic predictions first 

meet the specified performance criteria and the time 

for End of Life (EoL). Measuring effectiveness of 

the prognostic horizon could require examining 

whether the algorithm predicts within desired 

accuracy around the EoL. Another example is 

measuring whether algorithms stay within desired 

performance levels relative to specified parameters. 

Measuring PHM performance within a smart 

manufacturing environment creates new challenges and complexities, as systems become highly 

interconnected and interdependent. New metrics and methods for measuring PHM performance will be 

needed as smart manufacturing systems evolve, including ways to measure PHM at the equipment level, 

as well as at the overall system level. 

Photo Credit: DPC #65797175

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
27

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

3.2. Goals

Near-term goals for PHM performance assessment focus on evaluation of PHM as well as incorporating 

the key performance characteristics that are critical to manufacturing, including failure prediction/

recovery, defect reduction, and early detection of events. A supporting goal is developing 

models/simulations that can effectively identify where critical points are in the manufacturing process for 

PHM implementation. In the mid-term, it is desirable to ensure that PHM systems can achieve higher 

fault predictions of components or systems than currently possible today. Long-term goals for assessing 

PHM performance include evaluating specific characteristics, such as the ability for autonomous recovery 

from failure/fault conditions. Comprehensive understanding of the data needed to assess PHM 

performance is another long-term goal.

Table 3-1. Goals for PHM Performance Assessment

NEAR-TERM

(1 to 2 Years)

MID-TERM

(3 to 5 Years)

LONG-TERM

(5 + Years)

Desired PHM Performance 

Characteristics/Metrics

 Identification of data or performance 

characteristics critical to smart 

manufacturing and incorporation into 

PHM performance assessment

 Ability to predict failure of the 

component under study within 10 % 

of its entire life

PHM-Related Equipment 

Effectiveness/ Monitoring 

Technology

 Reduction in number of defective 

parts at the end of line by accurate 

anomaly detection and diagnosis 

(yield improvement of 20 %)

 Model-based identification of critical 

areas in a manufacturing system 

where PHM capability is needed; 

and integration in PHM performance 

assessment

Desired PHM Performance 

Characteristics/Metrics

 Ability for fault prediction at 

component and system level, 

with greater than 50 % likelihood

PHM-Related Equipment 

Effectiveness/ Monitoring 

Technology

 MTConnect available in 100 % of 

new machine tools to aid in PHM 

performance assessment and 

overall PHM system use

 Ability to produce actionable 

data to optimize repair/replace 

schedules

Desired PHM Performance 

Characteristics/Metrics

 Ability for autonomous recovery from fault 

conditions 

 Good understanding of how much/type of

data needed to test the PHM system

PHM-Related Equipment Effectiveness/ 

Monitoring Technology

 Effectiveness of self-prognostic machines 

for all critical manufacturers

 Integration of real-time analysis control

for assessment of PHM

Design and Life Cycle PHM for 

Performance Assurance

 PHM system designed into the product to 

enhance performance 

 Integration of PHM into the systems 

engineering process for better 

performance assurance

 Effective PHM system validation

3.3. Desired Capabilities

The desired capabilities identified for PHM performance assessment are outlined in Table 3.2 and 

summarized below. These encompass performance assessment of PHM systems at the equipment and 

system levels; response to failure modes; and validation of the accuracy of PHM techniques.

Performance of PHM, Diagnostics, and Maintenance

The most desired capability for measuring the performance of health monitoring, diagnostic, prognostic, 

and maintenance systems and components is the availability of pre-failure prognostic metrics. Other 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
28

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

important capabilities include making non-smart-manufacturing equipment intelligent enough to allow for 

monitoring and controlling capabilities, and cost-effectively modeling performance assessment before 

developing designs.

Performance of PHM in Response to Sources of Failure

When measuring the performance of PHM systems in response to sources of failure, one of the most 

desirable capabilities is the integration of manufacturing, maintenance and usage data, starting from the 

component level. It is important to not only consider the impacts of PHM at the equipment level, but also 

the performance of the overall system. Requisite data resources are needed to support these capabilities, 

such as an information repository of failure criteria, which could be used to inform and compare PHM 

systems.

Verification and Validation of Manufacturing PHM Techniques

Effective verification and validation of manufacturing PHM techniques could also benefit from more 

extensive and comprehensive data, as well as tools, models, methodologies, standard platforms for 

information dissemination, and guidelines for verification and validation. 

Table 3-2. Desired Capabilities for PHM Performance Assessment

Performance of PHM, Diagnostics, and Maintenance

High Priority  Pre-failure prognostic performance metric 

Medium Priority

 Ability to easily make non-smart-manufacturing equipment smart enough to be monitored 

 Model for assessment of performance prior to design 

 Industry standard digital sensor interface that are control system agnostic 

 Manufacturing equivalent of electronic health records 

 Usage-based and physics-based performance metrics (model-based systems) views 

Lower Priority

 Standard data models for process and equipment use by various data collection systems. 

Seamless synchronization for process changes.

 Probabilistic approach to characterize the effect of PHM (safety, availability, production, etc.).

 Modelling effect of faults on part quality

Performance of the PHM System in Response to Sources of Failure 

High Priority  Integration of manufacturing, maintenance and usage data, starting from the component level 

Medium Priority

 Consideration of the impacts of PHM in equipment level as well as overall system performance 

 Self-learning capabilities 

 Database of failure criteria to be used to inform and compare the PHM system 

 Better hardware for simulating PHM performance and uncertainty to drastically reduce 

computational cost 

 Standards and guidelines to ensure the success of PHM in the future of the manufacturing 

industry and our everyday life

 Modeling the impact on system performance of PHM results - Identify possible response

strategy and time requirement 

Lower Priority

 Systematic, standardized, and effective method for conducting or modeling accelerated life 

testing

 Plant-wide key performance indicator; PHM tools for production and asset utilization targets

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
29

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 3-2. Desired Capabilities for PHM Performance Assessment

Verification and Validation of Manufacturing PHM Techniques

High Priority  None identified

Medium Priority

 Verification and validation performed with limited data 

 Supply-chain-wide standards, techniques, and tools for PHM overall effectiveness index 

 Guidelines and standards for verification and validation of manufacturing PHM techniques 

 Better methods to identify systems of interest (not rate occurrence of failure or effect of 

downtime) 

 Standard platform for central domain experts to develop and teach diagnosis/prognostic 

algorithms across enterprise 

 Methods to record field maintenance actions and “close the loop” 

 Systems can connect with each other, i.e., Supervisory Control and Data Acquisition/

Computerized Maintenance Management System (SCADA/CMMS), CMMS with quality -

relational data

Lower Priority  Methodology to measure PHM system performance for new systems

3.4. Challenges and Barriers

Achieving the desirable capabilities and needs for PHM performance assessment requires overcoming 

several challenges and barriers, outlined in Table 3-3 and summarized below.

Availability of Data

The availability (or lack thereof) of historical data necessary to create diagnostic and prognostic models 

presents a challenge. It is difficult to accurately assess performance without “ground truth” for online 

performance metrics. Issues contributing to the lack of data include the costs associated with obtaining 

failure data, such as damaging systems and equipment, and a reluctance towards data sharing (partly 

because of organizations wanting to maintain a competitive advantage).

Standards

Another issue contributing to the lack of available data and hindering the idea of creating industry-wide 

datasets for developing predictive models is a lack of consistency in the classification of equipment and

failure codes. This is not only a problem for classifying information across multiple industrial facilities 

and organizations, but it also exists within companies themselves.

Design Challenges

It is difficult to gauge the tradeoffs between making design changes versus making partial system 

upgrades. The maintenance of PHM solutions should be taken into consideration when making decisions 

between the two choices.

Support and Awareness

The widespread use of PHM techniques could increase with greater support from leadership and the 

manufacturing community. The lack of buy-in is accompanied by low industry-PHM awareness,

experience, and training needed to apply principles and tools.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
30

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 3-3. Barriers and Challenges for PHM Performance Assessment

Availability of Data

High Priority

 Sparse datasets to develop models for diagnostics and prognostics, specifically for failing

equipment, and good historical diagnostic information

 Trying to assess performance without ground truth for online performance metrics

Medium Priority

 Lack of cost effective means to get test data (run-to-failure)

 Fear of sharing data; “give to get” maintenance data model could work

 Lack of comprehensive, integrated data from components, and the assembled asset

 Difficulty measuring/validating and implementing in a live production environment (i.e.,

minimizing possible interruptions)

Lower Priority  Lack of agreement on validated sensors and software and an agreed upon standard

Standards

High Priority  No consistency in classification of equipment, failure codes, etc., even within a company

Medium Priority

 Inventory to sales ratio is an hidden/undisclosed control group of manufacturing

 Lack of failure/fault characterization through modeling, simulation, and accelerated testing

 Lack of independent evidence to support standards development

 Lack of technology and standards for small and medium enterprises - common (broad market)

Lower Priority  None identified

Design Challenges

High Priority  None identified

Medium Priority

 Design changes versus partial system upgrades (maintenance of PHM solution)

 Lack of methods to roll-up component-specific performance to the system level

 Lack of more consolidated solutions for simplicity

Lower Priority  Use of “proven” PHM platforms integrated with other manufacturing operations/design

Support and Awareness

High Priority  Lack of continuous leadership/community support for implementation of PHM techniques

Medium Priority

 Low level of industry PHM awareness (education, skill, experience) to apply principles and tools

 Lack of communication and understanding of what’s available and capabilities

 Lack of buy-in and support from small and medium companies (risky and expensive)

Lower Priority  “Closing the loop” on recommendations; difficult to discover root cause/field action

 Tradition and culture that limit innovations (e.g., doing things the same way for generations)

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
31

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

3.5. Priority Roadmap Topics

Based on the major challenges and desired capabilities identified, six priority roadmaps were developed, 

as outlined below. 

 Overarching Architecture Framework for PHM with Standards and KPIs – Perform industryspecific benchmarks to define the current state, identify key performance indicators, and define

the standard PHM architecture. The framework could serve as a valuable reference for the smart

manufacturing industry.

 Cost Model for PHM Performance – Develop a cost-benefit analysis to better understand when

and where to apply PHM. The resulting model would take into consideration the costs associated

with equipment failures, collateral damage, lost revenue from machines being out of service, and

the cost of implementing PHM systems.

 Identification of PHM Performance Metrics – Develop online, prognostic performance metrics to

evaluate algorithm performance during manufacturing operation before system or component

failure, and inform control operations. These metrics would be identified at the component and

system levels and could help reduce equipment failure and improve asset utilization.

 Taxonomy for Applications – Taxonomy to provide consistency in the definition of components,

standard testing procedures, and interpretations of failure events. The resulting glossary could

improve internal and external communications and help obtain buy-in from management and the

manufacturing community.

 Determination of PHM Data and Information Needs – Collection of information about the most

prevalent characteristics for a PHM system, existing data collection strategies, and major causes

for failure and downtime. The effort would seek to provide standard inputs for PHM

configuration, architecture, and development and the resulting information repository could help

increase the adoption of PHM within the manufacturing community.

 Failure Data for Prognostics and Diagnostics – Methods for developing and collecting data to

support failure prognostics and diagnostics. Methodologies could result in a common database to

validate and verify diagnostics and prognostics of components or systems and facilitate standards

development.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
32

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Current status of the PHM framework in various industries is unclear; a PHM framework that includes 

standards is lacking, leading to inconsistencies in performance assessment.

Approach: The development of the framework would involve: 1) performing a benchmark for current state, 2) cataloging 

Key Performance Indicators (KPIs), and 3) defining PHM standard architecture and traceability. A standard framework 

will provide consistency in assessment of PHM performance and support new standards development. 

FIGURE 3-1. OVERARCHING ARCHITECTURE FRAMEWORK FOR PHM

WITH STANDARDS AND KPIS

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

w Benchmark the current state of

machine monitoring in select

industries, such as oil and gas,

automotive, aerospace, and

pharma/medical

w Identification of two key PHM scenarios to

benchmark from each industry, non-advanced and

advanced

w 80% of the benchmarks

completed

3-5

years

w Catalog the KPIs in the above

industries and map-out common

trends for prognostics and

diagnostics

w Catalog of KPIs based on benchmarking and best

practices

w Published catalog that is

accepted by 30% of industry

5+ 

years

w Define the standard PHM

architecture

w Create methods for asset traceability

and history recording

w Published system architecture and data flow

w Playbook for machine monitoring/benchmarking

w ISO standard traceability

w Creation of ISO documents

Stakeholders & Potential

Roles 

PHM Users Champion needs, 

problems, current state

PHM Providers/ 

Tech Developers

Provide historical 

successes and current 

capabilities

Technology 

Integrators

Adopt standard input on 

architecture

Academia Conduct literature studies, 

perform due diligence

Standards 

Committees Accept and publish

Government Provide technical support

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures

High Reduces costs

Medium Accelerates innovation

High Enhances industry 

competiveness 

Low Speeds process reconfigurability

High Improves maintenance 

scheduling

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Low

Complexity of R&D Low

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption Medium

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
33

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Subjectivity inherent in gathering risk data and predicting the value of a PHM system, especially since the 

complexity and differences of machines varies widely.

Approach: Cost data is needed for assessment of equipment failures, collateral damage, and lost opportunity (lost 

revenue from machines being out of service). In addition, the cost of implementing a PHM system must be evaluated. A 

database of cost information will be developed to support ROI determinations and comparison by fault modes.

FIGURE 3-2. COST MODEL FOR PHM PERFORMANCE

Roadmap Action Plan Milestones Targets/ Capabilities

1-2 

years

w Gather data on costs resulting from:

machine failures, machine repair, 

damage to products/surroundings, 

and factory downtime

w Determine cost of PHM systems 

development and integration

w Comparison of failure cost to PHM cost to determine 

return on investment (ROI)

w Determination of reasonable amortization

w Database of cost information and failure likelihood 

broken down by component w Cost models for two to three 

common machines

w Collection and analysis of 

sufficient data to determine a 

pattern for ROI calculations and 

build the business case for PHM

3-5 

years

w Refine model, broken down by fault 

mode

w Expand list of machinery

w Same as above, but more detailed

 List of fault modes

 Assessment of performance

 Actions taken

5+ 

years

w Develop inverse model w Capability for deriving requirements from ROI goals

Stakeholders & Potential

Roles 

PHM Users Provide data and

use cases

PHM Providers/ 

Tech Developers

Provide data and

use cases

Technology 

Integrators N/A

Academia Collaborate on database 

development

Standards 

Committees

Provide input to scientific 

community

Government

Collaborate on database 

development, review cost 

models

Benefits / Impacts

Impact Benefit

Low Improves reliability/reduces 

failures: Not to reduce failures per 

se

Medium Reduces costs: If implemented, 

reduces cost

High Accelerates innovation:

Integration would spur innovation

High Enhances industry 

competiveness 

High Speeds process reconfigurability 

High Improves maintenance 

scheduling 

Relative Success Factors 

Time to Achieve 

Results Low

Relative Cost of R&D Medium

Complexity of R&D Medium

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
34

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Lack of performance metrics to characterize efficacy of prognostics applied to a specific system before that 

system fails; insufficient information on key performance metrics for manufacturing at component and system levels.

Approach: Performance metrics are needed that can evaluate algorithm performance during operation (pre-failure) to 

inform system control and operations and maintenance planning. New performance metrics will be developed as 

needed. New and existing metrics will be evaluated for application to manufacturing systems.

FIGURE 3-3. IDENTIFICATION OF PHM PERFORMANCE METRICS

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

w Survey existing performance metrics that 

characterize performance of prognostic/PHM 

system itself and effectiveness when applied to 

system

w Identify necessary metrics to apply prognostics 

to manufacturing equipment and integrate with 

controls/O&M planning

w Understand gaps between existing metrics and

desired metrics

w State-of-the-art and gaps assessment for 

manufacturing PHM performance metrics

w White paper to present state-of-the-art

and gaps assessment

3-5

years

w Develop missing metrics

w Evaluate metrics across a variety of applications 

(equipment, systems) and PHM algorithms

w Investigate how to integrate performance 

metrics in control or operations, and 

maintenance planning systems

w New PHM performance metrics for 

manufacturing

w Demonstration of metrics on testbed or 

simulation

w Reduction of in-service failure

w Improved asset utilization

5+ 

years

w Integrate with generic PHM architecture to 

implement metrics

w Conduct demonstrations at pilot plant(s)

w Demonstration of metrics in real-world 

application

w Integration of metrics in PHM 

framework and manufacturing plant

Stakeholders & Potential

Roles 

PHM Users 

Provide input on needs;

Conduct pilot

demonstrations

PHM Providers/ 

Tech Developers

Develop metrics and 

algorithms; Implement 

metrics in PHM framework

Technology 

Integrators

Validate and test integration 

of metrics with framework

Academia

Develop metrics and 

algorithms; conduct testbed/

simulation demonstrations

Standards 

Committees

Develop and disseminate 

standards or guidelines for 

metrics

Government Support standards

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures: Better decisions can be 

made by considering PHM results 

and performance metrics

Medium Reduces costs: Consequence of 

failure reduction

Medium Accelerates innovation: Easier to 

compare competing PHM systems

for a specific application

Integration with control supports 

advanced control systems

High Enhances industry 

competiveness: Improved quality 

and productivity

Speeds process re-configurability: 

Some support through advanced 

control algorithms

High Improves maintenance scheduling: 

Provides more actionable information 

Relative Success Factors 

Time to Achieve 

Results Medium+

Relative Cost of R&D Low/Medium

Complexity of R&D Medium/High

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
35

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Lack of consistency in the classification of components, testing procedures, and interpretations of failure events.

Approach: This roadmap will focus on developing taxonomy for applications in collaboration with stakeholders to provide 

consistency in classification and enable standardization of performance assessment of PHM. The objective is to develop and enable

standard taxonomies covering 100% of critical equipment.

FIGURE 3-4. TAXONOMY FOR APPLICATIONS

Roadmap Action Plan Milestones Targets/ Capabilities

1-2 

years

Assumption: one company focus (initially)

w Compile differences between testing 

and point of contacts

w Correlate for consistency

w Identify critical equipment/develop 

critical assessment

w A “council” or other collaboration of the points of 

contact

w Upper management agreement on approach

w Critical assessments of 

inconsistencies in classifications 

for PHM testing and 

interpretation

3-5 

years

w Identify common stakeholders 

interpretations, jargon, language 

w Develop “glossary” of failure-codes 

and equipment classes

w Present/sell “glossary”

w Standardized agreement from Council

w Implementation through CMMS

w Acceptance/buy-in of users

w 100% critical equipment is 

standardized

5+ 

years

w Undertake continuous revision of 

“glossary” as technologies emerge

w Revisions by key points of contact 

w Collaborative agreement on taxonomy w 100% of compliance

Stakeholders & Potential

Roles 

PHM Users Provide data on test 

methods and criteria

PHM Providers/ 

Tech Developers

Provide data on test 

methods and criteria

Technology 

Integrators Validate, provide reviews

Academia N/A

Standards 

Committees Review taxonomy

Government Provide input and

guidance

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures: Better performance

Low Reduces costs: Indirect

Low Accelerates innovation: Indirect

Low Enhances industry 

competiveness: Indirect

High Speeds process reconfigurability: Better 

performance

High Improves maintenance 

scheduling: Better performance

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Low

Complexity of R&D Low

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption Medium

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
36

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: PHM solutions require tailoring and are not yet plug and play. User knowledge of systems (issues, failures, etc.) is 

lacking, and no standard method is available for calculating value and ROI. Event-driven data of good quality and integration of data 

(human actions) is lacking or insufficient; there is a general lack of knowledge of what is wanted from a PHM system.

Approach: The objective is to develop more or better standards for all aspects of the PHM process (data collection, data 

characterization, communication, integration, etc.). The approach includes standardization of record keeping and developing

standard criteria for which applications or components/machines should be applied (prioritization criteria).

FIGURE 3-5. DETERMINATION OF PHM DATA AND INFORMATION NEEDS

Roadmap Action Plan Milestones Targets/Capabilities

1-2

years

w Collect information about the most

prevalent causes or triggers of downtime

w Collect information about the existing data

collection; and characterization,

communication, etc. strategies

w Determine most prevalent wants and

needs for a PHM system (such as

functions)

w Collection of all needed information

w Repository of information established

w Survey of at least 30 different

organizations

3-5

years

w Define framework and key parameters

w Conduct sensitivity analysis

w Define inputs (extract features that best

inform PHM development)

w Framework for PHM aspects

w Framework that applies to 75% 

of industry

5+ 

years w Develop pilot study w Completed pilot study

w Guidelines for PHM solutions

w 30% reduction in development

and implementation time

w 30% increase in PHM adoption

Stakeholders & Potential

Roles 

PHM Users 

Identify most prevalent cause 

of downtime; provide existing 

data standards (if any); 

identify needs/functions for 

PHM systems

PHM Providers/ 

Tech Developers

Provide best practices, 

current strategies/techniques, 

and new strategies based on 

this work

Technology 

Integrators Validate and test framework

Academia

Develop framework and 

software, and conduct 

studies

Standards 

Committees Develop standards

Government Maintain database and 

standards; funding

Benefits / Impacts

Impact Benefit

Medium Improves reliability/reduces 

failures: Increased adoption

Medium Reduces costs: Established 

baseline/less development

High Accelerates innovation:

Low Enhances industry 

competiveness: Better 

understanding of all factors in PHM 

development/success

Low Speeds process reconfigurability: More standard 

approach leads to less flexibility

Medium Improves maintenance 

scheduling: Increased adoption 

and better understanding

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Low

Complexity of R&D High

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
37

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Stakeholders & Potential

Roles 

PHM Users 

Develop better validation 

and verification systems,

in-house; develop feedback 

on metrics, data

PHM Providers/ 

Tech Developers

Develop better PHM 

systems

Technology 

Integrators Validate PHM

Academia Test, develop data and

algorithms, survey papers

Standards 

Committees

Help develop standards

for metrics, formats, etc.

Government Establish Consortium

Benefits / Impacts

Impact Benefit

Medium Improves reliability/reduces 

failures:

High Reduces costs: Sharing cost 

across industry

High Accelerates innovation: Better 

access to validation and

verification data

Low Enhances industry 

competiveness: Indirect impact

Low Speeds process reconfigurability: Indirect impact

Low Improves maintenance 

scheduling: Indirect impact

Relative Success Factors 

Time to Achieve 

Results High

Relative Cost of R&D High

Complexity of R&D Medium

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption High

Roadmap Action Plan Targets/ 

Capabilities

Milestones

1-2

years

w Develop common database (organization

of data, awareness, process to

give/take/manage data)

w Create scaled-down testbeds (feasibility)

w Establish consortium (e.g.,

NIST/university) to look at PHM for 

specific systems (testbed)

w Common database (specifications for data

formats, process of managing data, etc.)

w Scaled-down testbed

w Identification of organizations for consortium

w Consortia with ongoing

participation

3-5

years

w Develop common database (qualification

of data/previous analysis results)

w Create scaled-down testbeds (testing for 

evidence)

w Common database (define qualification and

analysis metric)

w Scaled-down testbed (gather enough evidence,

public scrutiny, acceptance)

w Comprehensive, accessible

database

w Evidentiary testbeds accessible

to users

5+ 

years

w Develop common database

(implementation/testing)

w Conduct simulation modeling

process/benchmarking/initial

diagnosis/prognosis development

w Create scaled-down testbeds

(standardize)

w Common database (adopted standardized

system for public)

w Scaled-down testbed (standardized document

process)

w Simulation/modeling process (develop process,

evidence for adoption)

w Common database in active

use

w Adoption of standards

Barriers: Lack of good, accessible failure data for prognostics and diagnostics. Limited measurement and data collection 

methods and testbeds to provide data for PHM; lack of consistent data formats.

Approach: This project would develop methods and services to generate diagnostic and prognostic data sets for public 

consumption and validation and verification. Evidentiary testbeds would provide validation and data. 

FIGURE 3-6. FAILURE DATA FOR

__________________________________________________________________________________ 

PROGNOSTICS AND DIAGNOSTICS

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
38

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

4. PHM Infrastructure – Hardware,

Software, and System Integration

4.1. Overview

Section 4 presents the Roadmapping Workshop results of a breakout group focused on the topic of PHM 

Infrastructure – Hardware, Software, and System Integration. The section summarizes the discussions 

and activities conducted by this breakout group. Participants identified goals, desired capabilities, 

challenges and barriers, and priority roadmap topics. Some of the challenges identified extend beyond 

the scope of the PHM Infrastructure, such as the lack of a proven business case for PHM and the 

integration of PHM into business systems. A list of participants from all breakout groups is shown in 

Appendix A.

In recent years, there has been a concerted R&D effort to develop PHM to augment smart manufacturing 

capabilities across many industries. Part of this effort has focused on improving the PHM building blocks

– hardware, software, models and simulations (collectively known as the infrastructure) and the interplay

between these fundamental parts. The advent of PHM capabilities has led to the realization of a number of

sophisticated techniques and intelligent algorithms that can be applied to machinery data analysis, health

assessment and decision-making. These enhanced capabilities have improved product quality, enabled the

identification and performance of just-in-time

maintenance, helped avoid catastrophic equipment

failures and minimized equipment downtime.

While the current PHM state-of-the-art has enhanced the 

smart manufacturing mission, there are a variety of 

measurement science needs, challenges, and gaps that 

are hindering the widespread development and 

deployment of health monitoring, diagnostics, and 

prognostics technologies. Specific goals, capabilities, 

and challenges associated with PHM infrastructure 

development have been identified to advance this 

emerging space.

4.2. Goals

As PHM technologies continue to evolve, they provide richer and more relevant information for robust 

data-driven decision–making for improved performance, safety, reliability, and maintainability of 

instruments and equipment. These PHM technologies, in conjunction with smart manufacturing 

environments where cyber and physical systems are highly interconnected, are expected to improve the 

fidelity of manufacturing processes. Table 4-1 below highlights some high-level envisioned goals for 

PHM technology. These goals are separated into short, medium, and long timeframes as to when they are 

anticipated to be a commonplace.

Photo Credit: DPC #74486360

__________________________________________________________________________________

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
39

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 4-1. High-Level Goals for PHM

Hardware, Software, Integration, and Infrastructure

NEAR-TERM

(1 to 2 Years)

MID-TERM

(3 to 5 Years)

LONG-TERM

(5 + Years)

Maintenance

 Set of maintenance system

requirements

 Diagnosis certainty of 65%;

prognosis mean prediction certainty

of 25%

Data Management

 Machine tools with sensor data that

are accessible at sufficient sampling

frequency

 Established data exchange

standard (acquisition and archival)

 Standards for interoperability of

different data sources

 Open source or “maker-like”

movement for a common way to

‘speak’ to sensors

Design

 PHM as a component or system

design attribute; similar to other

design aspects: weight, cost,

performance trade-offs

Maintenance

 Diagnosis certainty of 80%;

prognosis mean prediction

certainty of 50%

 Machine/robot- auto-schedules/

orders maintenance/service

 No periodic maintenance – only

maintenance done based on PHM

indications

 Predict with greater than 90%

confidence on critical failure

modes, with better than two

month horizon

 Monitoring of physical system on

a regular basis

Hardware

 Identification of sensors to

facilitate PHM for standard

manufacturing equipment

 Integration of PHM with intelligent

control of machines/processes

Software/ Tools

 Requirements for complete PHM

systems and architectures end-toend

 Physics-based simulation models

to enable “What-if” scenarios that

are verified with real systems

Maintenance

 Diagnosis and prognosis certainty of

greater than 95%

 Level of interoperability so that

heterogeneous systems can respond to

impending failures

Data Management

 Requests for data security without

restricting data to those who need it

 Use of data as statistical input for

design tools to evaluate part cost,

“manufacturability”

 Six-Sigma probability of no false

positives

 Standards for interoperability of

different data sources

Infrastructure Security

 Creation of an evolving (adaptive)

security framework for PHM

Software/Tools

 Highly portable and easily configured

software prognostics tools

 Culture shift to enumerate/order

Computer Numerical Control (CNC)

features to facilitate PHM

measurements

4.3. Desired Capabilities

Researchers continue to build and improve on the functionalities of existing PHM systems all the while 

exploring what new capabilities PHM can perform. Table 4-2 highlights some specific desired capabilities 

for PHM systems and their importance in advancing PHM functionalities. The focus areas for this work 

span a number of areas.

Hardware

Enabling the use of PHM hardware and software in customized, agile, and flexible production lines is 

highly desirable. Embedded, self-powered PHM-related devices in-line with component manufacturing 

(sensors, corrosion detection, wire runs) will support this. Smart and/or self-aware and self-adapting 

sensing capabilities will be a key aspect.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
40

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 4-2. Desired Capabilities and Metrics for Infrastructure for PHM

Hardware

High Priority  Embedded, self-powered PHM-related devices in-line with component manufacturing (sensors, 

corrosion detection, wire runs)

Medium Priority

 ‘Prognostics-builder” toolset

- Functional capabilities

- Anomaly detection

- Banks of models

 Smart sensors that transmit on anomaly or change to save power

 Self-aware, self-adapting, self-repairing robots and systems 

 Long life/ replacement and upgrade time for manufacturing equipment/ sunk assets (e.g., 25 

years) and legacy equipment challenges; feasibility of legacy equipment retrofits 

Lower Priority

 Low cost, reconfigurable monitoring devices to adapt DAQ and base detection capabilities to be

reusable

 Motor current time-frequency statistics included in PHM evaluation

 Flexible manufacturing methods allowing for secondary systems to take over when ‘data centers’ 

fail 

 Secure, low power, wireless connectivity for sensors and processors (by domain) standard 

Data Analytics/ Software

High Priority  Prediction of critical “system-level” failure of an operation/factor

Medium Priority  Thresholds and failure models from data analytics as part of automated closed-loop process

 Flexible PHM solutions that learn, communicate, adapt and influence 

Lower Priority

 Actionable information that permits minimization of operational risk, scrap and energy use

 Development of crowd-sourced PHM profiles 

 Characteristic (standards-based) data models for equipment for procurement and engineering

 Corrosion health management analytics and life-remaining predictions

 Increased bandwidth for data storage/compression

 System failure simulations based on data operational testing capability (e.g., measurements from 

system are analyzed with past history for better predictions)

 Information security system with intrusion detection capability

 System performance data analysis to detect anomalies in performance of software/hardware 

 Capture of maintenance actions to validate and improve predictions 

 Verification and validation tools and “gold” standard data sets

Interoperability

High Priority  “Lego blocks” plug-and-lay reconfigurable PHM modules – with standard data structures 

Medium Priority

 Generalized wireless Network Capable Application Processor connectivity to help information 

continuity

 High bandwidth connection of PHM data

Lower Priority  None identified

Data Analytics/ Software 

A high priority is the ability to predict critical system level failures. This would require better, validated 

failure models based on data analytics along with verifiable failure thresholds. Capabilities are also 

needed to effectively integrate hardware and software with legacy systems and new complex, highly 

connected smart process/ manufacturing equipment.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
41

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Interoperability

Better capabilities are needed to ensure seamless interconnectivity between PHM and internal and 

external communications gear – wireless networks, transponders and receivers, etc. This will require 

interoperability among a range of hardware. Plug-and-play, reconfigurable PHM modules that are 

standardized will support greater interoperability of interconnection of PHM, devices, and equipment 

across the plant, enabling more intelligent diagnostics and prognostics.

4.4. Challenges and Barriers

Specific desired capabilities and goals were identified for how PHM can contribute and advance the 

mission and objectives of many smart manufacturing initiatives. However, many of these cannot be 

realized yet due to a number of existing challenges and barriers that need to be addressed. Table 4-3 lays 

out a number of these barriers for PHM use and integration into modern manufacturing systems, 

particularly infrastructure. These challenges are grouped into four broad categories.

Technology and Infrastructure Implementation

This includes the issues arising from the development of hardware, software, model and simulations 

packages, and the associated integration with equipment. The key challenges center around security 

requirements for new systems that include wireless or other advances requiring increased surveillance;

complexities of system integration across highly connected enterprises; and creating effective sensing 

with legacy equipment or equipment not designed for this capability. The long life of manufacturing 

equipment is another issue; improvements and replacements are slow to occur. Retrofits of advanced 

PHM on legacy equipment may not necessarily be affordable or practical. The same challenge is likely 

true for many smart manufacturing technologies.

Economic and Business Requirements

Issues arise due to the need to balance company operations versus business strategy. PHM is typically 

not well-integrated with equipment or process procurement requirements, and is also difficult to integrate 

with business systems. An underlying issue is the lack of a proven, readily justified business case for 

PHM based on a demonstrated value proposition. The availability of proven PHM software (with 

demonstrated cost benefits), for example, would help to establish a good business case. This is discussed 

in more detail in Chapter 5, Cross-cutting Challenges.

Measurement and Standards 

The standards environment for PHM in smart manufacturing is challenging. PHM integration can be

difficult because of too many standards; open standards are lacking or not sufficient. Many of the 

challenges identified focus on establishing baseline standards that the PHM industry can then design and 

build against. Particular areas where standards are lacking include software module standards and 

cybersecurity standards for robust, secure wireless sensors and networks. 

In the measurement arena, there is uncertainty on what to measure or control via PHM to support

consistent, reliable, and repeatable quality product. This is generally a process-specific challenge, i.e., 

better understanding of manufacturing processes is needed. However, there are common measurement 

requirements related to PHM that create challenges, such as defining usable metrics for PHM 

performance.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
42

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Human Elements 

The human element arises in business and manufacturing culture challenges. PHM development and 

implementation works best when championed or led by a management organization structure within an 

organization, i.e., a concerted effort toward PHM adoption. This requires the ability to articulate PHM 

needs and benefits throughout all levels of an organization.

For successful implementation, PHM information/data needs to be presented in a usable, actionable form 

to technicians and engineers – and they need to be trained how best to take action based on that 

information. Additional discussion on workforce issues is found in Chapter 5, Cross-cutting Challenges. 

Challenges inherently arise in the manufacturing environment where humans typically make these 

decisions (without automated systems) and are presented with new systems where these decisions are 

removed from operator control (issues of trust, reliability, etc.). 

Table 4-3. Barriers for PHM Infrastructure, Integration, and Utilization

Technology and Infrastructure Implementation 

High Priority

 Security concerns

- PHM requires more data collection leading to increased surveillance/monitoring

- Security concerns for wireless sensors

- Lack of understanding PHM cyber-security challenges/guidelines

Medium Priority

 System integration is difficult – especially across an enterprise

 Sensor placement due to equipment design limitations

 Lack of or insufficient existing machine data drives up cost of PHM due to the need to replicate

sensors and data acquisition

 Immature models that are constantly evolving

 Lack of availability of generic PHM software solutions

Lower Priority

 Vast differences in scale and types of manufacturing

 Prognostic metrics not universally agreed upon/available for validation

 Lack of existing information/data infrastructure

 Inability to provide actionable requirements to equipment suppliers by delivering the right data

and sensors

 Limited PHM capabilities due to factory IT and legacy equipment (e.g., WIN 95/NT, etc.)

− Availability of port(s) for PHM connectivity

− Non-invasive sensor data is only solution but there is a loss in process fidelity

 Current sensor technology limitations (size, power consumption, other limitations)

 Low-cost equipment limiting specific failure models for reliable RUL prediction

Economic and Business Requirements

High Priority

 Lack of PHM in procurement requirements and limited alignment with company/business

objectives

 Difficulty integrating PHM with business systems (e.g., ERP)

 Lack of proven PHM software ( i.e., a credible/reliable “check engine light” )

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
43

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Table 4-3. Barriers for PHM Infrastructure, Integration, and Utilization

Medium Priority

 High costs of sensors, IT infrastructure, and data acquisition; added expense of PHM wire run 

when capability does not have proven ROI 

 Lack of funding for open source PHM software/sensor development and analysis 

 Limited technology investment and maturation programs for PHM; finite capital investment 

budgets with competing priorities

 Limited capital for non-recurring engineering for high fidelity, high performance PHM (i.e., lack of 

inexpensive, good, fast PHM)

 Lack of plans for long-term ownership of data and analysis results (economics of data storage 

and related issues)

 Limited credibility of the PHM system when in operation

Lower Priority

 Developing low cost PHM solutions – layers/non-embedded solutions 

 Inability to ensure that PHM is a driver for component design 

 Limited technology availability- large business can afford dedicated PHM group, small business 

cannot

Measurement and Standards

High Priority  Limited implementation of PHM when advanced by an open source community (creating 

standards without royalties/free); lack of commitment to open standards 

Medium Priority

 Limited software module standards 

 Too many standards which limits integration of PHM (i.e., inability to coalesce on a smaller set of 

key standards)

 Limited ability to address cybersecurity standards for robust, secure wireless sensors, networks

 Uncertainty on what to measure and/or control leading to consistent, reliable, repeatable quality 

product 

 Insufficient understanding of manufacturing processes, e.g. additive methods

Lower Priority

 Lack of standards/metrics for PHM system performance 

 Lack of policy standards 

 Unused open database standards, such as those from the Machinery Information Management 

Open Systems Alliance (MIMOSA)

 Lack of wireless hardware and connectivity standards 

 Limited diagnostics and prognostics of machinery standards (ISO 13373, under development) 

 Ability to measure ongoing process health, “how well are we doing” rather than just failure/

impending failure 

 Limited methods for feeding the results of PHM (data)back to system/component design

 Lack of a source/forum information exchange that lists all applicable standards 

 No librarian/historian for national and ISO PHM standards reservoir 

Human Elements

High Priority  None identified

Medium Priority

 Limited management structure within an organization for PHM development and implementation; 

inability of company management organization structures to effectively understand/use PHM 

 Ability to present PHM information/data in a usable, actionable form to technicians and engineers

 Articulating the PHM needs at high levels in any organization

 Recognition by designers of PHM solutions that systems will change

Lower Priority

 Creating a paradigm shift in the middle management thought process when considering PHM

 PHM systems that consider people, organizations, training and supply chain 

 Limited culture in OEMs for adoption of open source PHM (e.g., for information sharing) 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
44

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

4.5. Priority Roadmap Topics

Of the capabilities and challenges in Tables 4-2 and 4-3 that were identified as most important, the 

following four topics were put into priority roadmaps. Some of the high priority capabilities and 

challenges were combined and covered in the roadmaps. 

 PHM as an Equipment Design Feature – In today's military platforms, reducing system size, 

weight, and power (SWaP) is critical for maximizing operational life and meeting budgetary 

constraints.5 Similarly, weight, cost and performance are often the key drivers that designers take 

into consideration when developing manufacturing systems. PHM is typically not thought of as a 

design feature due to limited awareness or unknown impact, but should be considered since a 

well-functioning PHM system can have bottom-line implications similar to other traditional ones 

like cost, weight, and performance. 

 Open-Source Community for PHM – An open source development model provides for complete 

access to a product's design, and full redistribution of that design, including subsequent 

improvements to the design6,7. Such a model shares the risk and development capital expense 

across a number of institutions while enabling unrestricted collaborations and establishing some 

fundamental baseline designs and standards. This type of collaboration is extremely beneficial for 

problem-solving as well. Collaboration for PHM developments would establish a common 

baseline to start from and build upon. 

 Embedded Sensors for PHM of Emerging Manufacturing Technologies – In general, when 

retrofitting currently-in-use equipment with value-added technologies such as PHM, there is a 

need to ensure that the technology installation process doesn’t degrade/damage equipment 

performance. Attention is also needed during installation to ensure that the technology performs 

to specification. Identifying innovative ways to embed and operate PHM systems seamlessly 

should support optimal performance of the PHM and the production equipment.

 PHM Infrastructure to Deliver Relevant Timely Information – Accurate, timely, and actionable 

information has been a goal for manufacturers. However, when inaccurate data and/or incorrect 

information is delivered to the wrong operations level, actions are delayed and can, in many 

instances, lead to detrimental actions that damage or decommission instruments and equipment. 

Relevant, verified, and targeted PHM systems aim to deliver accurate status updates to the 

appropriately-targeted recipient. 

 

5 http://www.altera.com/end-markets/military-aerospace/swap/mil-swap.html

6 Lakhani, K.R.; von Hippel, E. (June 2003). "How Open Source Software Works: Free User to User Assistance". 

Research Policy 32 (6): 923–943. doi:10.1016/S0048-7333(02)00095-1.

7 Gerber, A.; Molefo, O.; Van der Merwe, A. (2010). "Documenting open-source migration processes for re-use". In 

Kotze, P.; Gerber, A.; van der Merwe, A. et al. Proceedings of the SAICSIT 2010 Conference — Fountains of 

Computing Research. ACM Press. pp. 75–85. doi:10.1145/1899503.1899512. ISBN 978-1-60558-950-3

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
45

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Limited consideration of PHM as a design attribute during the many trade-offs made in a system and/or 

component design process – not viewed the same as weight, cost, performance, etc.

Approach: The approach includes conducting activities to support considerations of PHM in design, and to affect 

changes in current paradigms used during a platform, system, and/or component design definition process. This 

paradigm shift would enable the use of PHM capabilities and their information products to change design thinking to 

save weight, costs, redundancy, and increase performance.

FIGURE 4-1. PHM AS AN EQUIPMENT DESIGN FEATURE

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

w Study what is feasible in this area

w Focus on component weight reduction and cost

savings associated with excess material weight

and redundancies of component elements like,

actuators, engine disks

w Generate a focused demonstration project plan

w Identification of feasible PHM design

features

w Fully-scoped demonstration program

for a set of PHM design features

w Early demonstration of PHM

into key design features

3-5

years

w Conduct one or more demonstration programs to

show PHM can be integrated in design with little or 

no impact on structure or component safety

w Expand scope of this application to include

additional high value components

w Start multiple demonstrations to provide the

component designer the evidence that the

tradeoffs are safety and confidence

w Initial pilot demonstrations completed

w Project plan completed for additional

demonstrations with defined

components and schedule

w Multiple-equipment/component

demonstrations initiated

w Demonstration of practical

PHM value proposition for each

design

5+ 

years

w Continue multiple demonstration and test cases

w Get OEM and industry buy-in

w Demonstrations completed

w Test cases/best practices published

w PHM adopted in multiple

equipment sectors

Stakeholders & Potential

Roles 

PHM Users Work with OEMs to 

incorporate PHM in design

PHM Providers/ 

Tech Developers

Demonstrate designs –

structural health monitoring 

and PHM providers

Technology 

Integrators Integrate system designs

Academia

Conduct accelerated aging 

tests, fault simulations to 

generate model data, 

support cost-benefit 

analysis

Standards 

Committees N/A 

Government

Laboratories - conduct 

accelerated aging and 

other tests to generate 

model data, support costbenefit analysis

Other Material science inputs

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures: PHM in early stages of 

design reduces overall failure 

potential

High Reduces costs: Avoids costs

due to unpredicted equipment 

failures

High Accelerates innovation:

Supports equipment innovation

at early stages of design

High Enhances industry 

competiveness: Reduces costs, 

supports equipment agility

early-on

Medium Speeds process re-configurability: 

Supports agility via informed design 

for customization, flexibility

Medium Improves maintenance 

scheduling: Incorporates PHM and 

maintenance principles into design 

practices

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D High

Complexity of R&D Medium

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption High

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
46

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Stakeholders & Potential

Roles 

PHM Users Contribute frameworks to 

the community

PHM Providers/ 

Tech Developers

Build adapters and 

components

Technology 

Integrators

Disseminate and

contribute to framework, 

toolkits

Academia

Contribute to frameworks, 

generate data and 

contribute

Standards 

Committees

MT Connect, OPXC, Step 

NC/machine tool 

languages, standards to 

support open architecture

Government Support and guidance

Benefits / Impacts

Impact Benefit

Improves reliability/reduces 

failures: N/A

High Reduces costs: Open source

High Accelerates innovation:

Developers/users can focus on 

PHM algorithms

High Enhances industry 

competiveness: Enables

adoption of PHM to reduce 

maintenance costs, enhance

agility

Medium Speeds process reconfigurability: Greater agility

via ease of PHM use

Medium Improves maintenance 

scheduling: Better understanding 

of maintenance requirements

Relative Success Factors 

Time to Achieve 

Results Low

Relative Cost of R&D Low

Complexity of R&D Low

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption Medium

Barriers: Cost and complexity to implement prognostics and PHM on new equipment is high.

Approach: The objective is to develop an open source architecture, which will reduce costs and complexity for 

implementation and provide the foundation for an end-to-end PHM architecture. The approach includes collection of 

data, identification of systems and devices that should be covered, and development of an open community framework 

and architecture. 

FIGURE 4-2. OPEN SOURCE COMMUNITY FOR PHM

Roadmap Action Plan Milestones Targets/Capabilities

1-2

years

w Develop open drivers and adapters

for PHM

w Expand data collection infrastructure

with open source perspective

w Develop security, compression, fault

tolerance, and schema for open

architecture

w Consortium established with a charter to produce

open-source technology for PHM ; funding models

established

w Criteria established for users/members

w Open architecture schema developed

w Significant and increasing

number of users and members

(TBD)

3-5

years

w Identify systems/devices to be

covered

w Develop set of frameworks and

toolkits for users to interface with

equipment

w Mature and expand drivers, adapter 

breadth

w Self-funding business model for open community

resources established, sustained with support,

training and implementation

w Demonstration of usable frameworks and toolkits

w 50% of equipment, devices,

applicable systems covered by

an open community framework

5+ 

years

w Foster continuous development (like

Linux community)

w Frameworks revised and updated as technology

emerges

w Improvement in business models

w 80% of industry using the open

framework

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
47

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Stakeholders & Potential

Roles 

PHM Users Test/demonstrations of 

sensors

PHM Providers/ 

Tech Developers

Test/demonstrations of 

sensors

Technology 

Integrators

Component manufacturer 

contributes to 

test/demonstrations

Academia Material science 

assessment

Standards 

Committees Testing standards

Government Regulation of structural

life and funding

Other OEM involvement

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures: Sensor data enhances 

failure prediction/ prevention

High Reduces costs: Reduces failure 

incidence and related costs

High Accelerates innovation:

Supports new technology

adoption and agility/flexibility

High Enhances industry 

competiveness: Reduces costs 

due to failure

High Speeds process reconfigurability: Enables better 

response to process/product 

changes

Medium Improves maintenance 

scheduling: Validation for 

maintenance scheduling

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Medium

Complexity of R&D Medium

Risk Associated with 

R&D Medium

Likelihood of 

Success/Adoption High

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

w Conduct technology feasibility study

w Define which devices/sensors are suitable for 

embedded PHM

w Assess impact on structural integrity

w Map to new or existing manufacturing

processes

w Sensor and structure components for

demonstration identified

w Candidate manufacturing technology

process identified for demonstration

w Feasible and achievable cost

targets

3-5

years

w Conduct manufacturing demonstration with

embedded PHM; include manufacturing

processing capability

w Generate architecture definition

w Develop notional design integration and

requirements

w Technology demonstration and report

w Architecture for sensor and structure

integration

w Recommended design integration and

requirements report

w No degradation in structure and

component reliability

5+ 

years

w Integrate with actual manufacturing production

w Expand scope to include other sensor types and

components for manufacturing technologies

w Additional demonstration projects for emerging

technologies and for validation

w Results of manufacturing process

capabilities study

w Long term validation and verification

assessments

w High component reliability

Barriers: Utilizing embedded PHM sensors (in structure or surface mounted) wire runs, self-powering technology and 

other related devices during the manufacturing process for structures (structural components) is promising but 

challenging to implement due to complexity and cost, and possible impacts on structural integrity.

Approach: This effort will study the feasibility of embedded sensor and technology concepts for PHM, based on new 

and/or evolving manufacturing technologies like 3D printing; determine the sensors, wire runs, and devices that could be 

of most value.

FIGURE 4-3. EMBEDDED SENSORS FOR PHM OF EMERGING 

MANUFACTURING TECHNOLOGIES

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
48

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Barriers: Inability to make good decisions based on data; wrong information and detail at the wrong level.

Approach: The approach involves developing the appropriate information at each level to facilitate key operations and 

maintenance decisions. The outcome is a traffic light approach to data (green, yellow, red) for actionable decisions.

FIGURE 4-4. PHM INFRASTRUCTURE TO DELIVER RELEVANT,

TIMELY INFORMATION

Stakeholders & Potential

Roles 

PHM Users 

Provide inputs for ERP and 

rules for actionable 

decisions

PHM Providers/ 

Tech Developers

Develop models of

selected equipment; 

disparate data fusion

Technology 

Integrators

Integrate PHM decisions 

with ERP

Academia Develop cyber algorithms 

and models

Standards 

Committees

Develop metrics for 

decision success

Government Support development 

efforts

Benefits / Impacts

Impact Benefit

High Improves reliability/reduces 

failures: Better data for prevention

High Reduces costs: Reduced failures

Medium Accelerates innovation:

Innovations in PHM methods

Medium Enhances industry 

competiveness: Reduces costs, 

provides agility

High Speeds process reconfigurability: The right data is 

used to make quick decisions

High Improves maintenance 

scheduling: Improved decision 

making in real-time

Relative Success Factors 

Time to Achieve 

Results Medium

Relative Cost of R&D Medium

Complexity of R&D Medium

Risk Associated with 

R&D Low

Likelihood of 

Success/Adoption High

Roadmap Action Plan Milestones Targets/ Capabilities

1-2

years

w Develop tools to build “twin” or cyber-models of line

replaceable unit (LRU) parts/components to get RUL or 

mean time to failure (MTTF) predictions

w Determine what data is needed to model diagnostics &

prognostics

w Conduct needs assessment to determine information

needed at each operational level

w Understand prognostic needs at each level of operation

w Develop capability to fuse data from non-commensurate

data sources for use in diagnosis and prognosis

w Develop fusion methods for sensor data, information

algorithms and decision processes (e.g., reliability

methods)

w Identify parts/components that relate to machine

diagnostic/prognostic events

w Develop engineering economic models based on

enterprise wide metrics

w High fidelity analytic models and

required data sets assembled,

verified and validated for selected

machines

w Operational level decision needs

established

w Demonstrated ability to make a

limited set of decisions based on

disparate data

w Demonstrated ability to identify

specific degraded components

w Demonstration that PHM system

can provide appropriate direction to

enterprise resource planning (ERP) 

systems

w Models for 5 major 

manufacturing equipment

demonstrated, e.g., 5 types of

machine tools

w Demonstrated fusion of 5 to 6

data sources

3-5

years

w Develop cloud-based data repository and analytic

engine to refine decision-making

w Understand gaps in diagnostic and prognostic

algorithms for each level of operation

w Develop tools for component “twin” cyber models for 

LRU predictions

w Develop technology for adaptable alarms based on

condition

w Develop models to interface with enterprise systems

w Continue methods and other development

w Demonstrated limited set of

decisions made or refined from

cloud-based data

w Identification of specific prognostic

needs based on ability to make

accurate decisions

w Demonstrated “twin” cyber models

for selected machines

w Demonstrated alarm

reconfiguration usage conditions

w Expansion of data sets

demonstrated in 1-2 year 

milestones

w Decision accuracy improved

by PHM by 50% based on

model based simulation

w Proven 50% improvement in

alarm accuracy

5+ 

years

w Develop advanced usage-based models to construct

better decision making

w Validated usage models based on

real world experience and data

collected

w Increased decision making

ability by 80% overall

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
49

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

5. Crosscutting Challenges

There are a number of challenges that cut across the entire spectrum of PHM topics. These include:

Data Collection and Extraction of Information

Challenges exist for data collection and extraction of useful information from data (e.g., data quality, 

utility, and availability at the levels required), especially to generate timely, actionable intelligence with 

which to make appropriate decisions. Data collection is impeded by a lack of sensors and measurement 

methods for efficient and effective data collection that is tailored for the use of effective PHM for smart 

manufacturing systems. In addition, data and data standards inconsistencies make it difficult to uniformly 

apply PHM across the diverse spectrum of manufacturing equipment and systems; standardized data 

formats and taxonomies could help to encourage the use and improve the effectiveness of PHM. The best 

data collection comes from opportunistic failures. However, it is not practical to force machines/systems 

into a fault/failure state for the sole purpose of acquiring good PHM data. An ongoing challenge is how to 

generate accurate data for PHM without damaging equipment or impacting productivity.

Models, Simulation, and Visualization (MSV)

An overarching challenge is the limited availability of models to support PHM with validated accuracy 

and predictive capabilities. MSV is hampered by the wide variety of systems and equipment, the lack of 

integration of legacy systems (that are now becoming interconnected), and data availability. The lack of 

consistent, high quality data exacerbates modeling limitations; historical and/or practical/operating data is 

essential for model validation and accuracy. 

In addition to supporting predictive maintenance and system performance, models are needed to help

demonstrate the value of PHM and its use and performance in practice. It is difficult to build a business 

case for PHM without models that can reliably predict performance and other improvements (and their 

cost and other positive impacts).

Design Considerations

There are potential high-impact opportunities for improving the performance and maintainability of 

equipment and processes by incorporating PHM at the early design stage. Current equipment design 

philosophies do not adequately consider PHM and/or how it [PHM] could change over time. Designing a 

systems approach for PHM, such as that needed for smart manufacturing systems, is not effectively 

incorporated in today’s design practices. A concerted effort involving both users and OEMs might be 

needed to effectively integrate PHM at the equipment and systems design levels.

Workforce and Training

PHM has been in use for many decades. Newly emerging PHM tools and techniques for smart 

manufacturing are incorporating a greater level of sophistication, modern information technology (IT),

and networks that are highly connected and span the entire manufacturing enterprise. To operate 

effectively in the smart manufacturing environment, the next generation workforce will need new skills 

and training that are currently lacking (e.g., information-based decision making). Filling this gap will 

require a mix of technical vocational educational opportunities, new community college programs, 

technical training, and changes in engineering/science curricula in the higher education system. Currently, 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
50

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

there is a lack of human talent and skilled professionals trained by accredited institutions. Even with 

systems that are highly automated, domain experts will still be needed, who will require continuous 

training as technologies emerge.

Currently, PHM-specific majors and curricula are lacking in institutions of higher education. Majors 

should not be limited to Electrical Engineering, but should be inter-disciplinary (e.g., mechanical/

industrial engineering, systems engineering, business, information technology, etc.). Post-education, there 

is a lack of engineers qualified or experienced with PHM analytics. 

Human Factors

Human and cultural elements will come into play as more sophisticated PHM techniques emerge for the 

plant floor. Veteran operators are less likely to accept PHM technologies that make decisions based on 

machine thinking, especially if these same human operators have been responsible for recommending 

courses of actions (prior to the introduction of any PHM technologies). There may be resistance to the 

idea of machines making decisions with minimal human interaction, unless the value and reliability has 

been fully demonstrated. Keeping humans in the loop (and when to do so) will be a significant 

consideration. Non-automated, manual (human) recording of data, maintenance events, and failures could 

continue to limit development and validation of PHM systems.

Existing PHM techniques may be outdated and unable to take advantage of the full opportunity of smart 

manufacturing environments. Although PHM has been in use in some form for years, data-driven 

decision-making is not sufficiently embedded in today’s manufacturing culture and environment. One of 

the challenges is changing the operators’ perception so they trust data enough to act on it. In some cases, 

operators’ fear of being constantly monitored may be a factor in adoption.

Operators on the front line of manufacturing decision-making will need to understand how advanced 

PHM can provide increased value over traditional methods in this highly-connected environment. 

Demonstrating how advanced PHM can be integrated with intelligent equipment and processes to make 

their jobs easier and more productive could go a long way toward fostering acceptance and cultural 

change. 

Business Case for PHM

A strong business case is necessary for accelerating the development and adoption of PHM for smart 

manufacturing. Business models and a strong value proposition for PHM are currently lacking. This 

represents a priority challenge, as the business case directly impacts the ability to sell PHM to decisionmakers. Management support for PHM investments (R&D or technology) can be lacking and/or 

competing with other manufacturing priorities where the cost-benefits are more certain.

Reliable, credible methods for quantifying ROI are not readily available or well-justified. Good predictive 

models, data, methods, and metrics for measuring PHM performance are all key factors for building the 

business case – and limitations exist in all these areas (see also Figures 3.2 and 3.3). Demonstrating the 

value of PHM in practice can be challenging, since it often must be measured over equipment life and 

maintenance cycles. The cost of enabling technology and infrastructure can also appear high when 

compared with benefits. Today, there is no standard way to quantify ROI related to developing or 

implementing PHM. This includes quantifying the associated costs of sensors, IT equipment, data 

acquisition, and maintenance. There is a strong need to develop a universal framework for cost-benefit 

analysis of PHM in manufacturing, and to clearly demonstrate/assess the benefits of various components 

and infrastructure. This would potentially accelerate greater investment and adoption. 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
51

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

6. Next Steps

It is anticipated that this report will be used by the stakeholder community to guide future directions for 

the development of new technologies and infrastructure to support widespread use of PHM in smart 

manufacturing environments. Those organizations involved in conducting PHM-related R&D, developing 

new products and services, and directly involved with implementation of PHM systems are likely to find 

this report useful.

NIST is carefully reviewing the workshop findings to update the PHM4SMS project’s research direction 

to further align it with industry’s needs and priorities. The PHM4SMS project team is currently focused 

on three specific efforts that will be informed by the key findings highlighted in the priority roadmaps 

presented in this document. No single roadmap stands out as being entirely synchronized with the forthcoming research efforts; rather, numerous roadmap themes are echoed. Some of these themes include a 

focus on sensing, data collection and organization, consistent metrics and standards, and promotion of 

community-wide PHM capabilities. The roadmaps most relevant to these research efforts include:

w Breakout Topic: PHM Manufacturing Process Techniques and Metrics

 Advanced Sensors for PHM in Smart Manufacturing

 PHM Data Format, Taxonomy, and Architecture

w Breakout Topic: PHM Performance Assessment

 Overarching Architecture Framework for PHM with Standards and KPIs

 Identification of PHM Performance Metrics

 Failure Data for Prognostics and Diagnostics

w Breakout Topic: PHM Infrastructure – Hardware, Software, and Integration

 Open-Source Community for PHM

 PHM Infrastructure to Deliver Relevant Timely Information

Collectively, the three research efforts described below are expected to address numerous elements of 

these roadmaps in their long-term plans. Table 6-1 shows how the aforementioned priority roadmaps 

relate to the three NIST PHM4SMS research efforts. Since these research efforts are still relatively young, 

numerous details will be refined as these efforts begin to mature. 

Table 6-1 - Select Priority Roadmaps and their Relationship to NIST Research

Machine Tool 

Linear Axes

Systems-Level 

Diagnostics and 

Prognostics

PHM4Robotics

Topic: PHM Manufacturing Process Techniques and Metrics

Advanced Sensors for PHM   

PHM Data Format, Taxonomy, and Architecture  

Topic: PHM Performance Assessment

Overarching Architecture Framework for PHM with Standards & KPIs  

Identification of PHM Performance Metrics  

Failure Data for Prognostics and Diagnostics   

Topic: PHM Infrastructure - Hardware, Software, and Integration

Open-Source Community for PHM   

PHM Infrastructure to Deliver Relevant Timely Information   

NIST RESEARCH EFFORTS

PRIORITY ROADMAPS

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
52

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

It is important to note that each research effort is likely to touch the identified priority roadmaps in unique 

ways, at different time horizons, and at varying levels of intensity. Some similarities are also expected, as 

well. For example, each of the NIST research efforts is expected to leverage the Advanced Sensors for 

PHM roadmap which is going to include taking inventory of relevant sensors. There are likely to be 

overlapping sensors identified (e.g., sensors capable of measuring positional accuracy would be relevant 

to all three research efforts or sensors capable of measuring motor current), yet there will be differences. 

One example is that the breadth of identified sensors will be different; this is apparent in the componentlevel research of the Machine Tool Linear Axes Test bed as compared to the systems-level test bed 

encompassing numerous machine tools. 

6.1. Machine Tool Linear Axes Diagnostics

A linear axis is a vital subsystem of machine tools, which are often critical systems within many 

manufacturing operations. When installed and operating within a manufacturing facility, a machine tool

needs to stay online and in good condition for parts production. All machine tools degrade during 

operations, yet knowledge of that degradation is elusive; specifically, accurately detecting degradation of 

linear axes is a manual and time-consuming process. Thus, manufacturers need quick and automated 

methods for continual diagnosis of machine tool linear axes without disruptions to production.

This effort is focused on developing a sensor-based method to quickly estimate the degradation of linear 

axes and is supported by the development of a linear axes test bed, as depicted in Figure 6-1. This method 

leverages data collected from a NIST-developed ‘sensor box’ to detect translational and angular changes 

due to axis degradation, and the test bed allows assessment of the new method against a conventional

laser-based system. Simulations revealed that the multi-sensor-based method is capable of achieving test 

uncertainty ratios (TURs

8

) of at least 4:1.

Figure 6-1 – Rendered image of linear axis testbed for testing of sensor-based method.

Once the method is verified for diagnostics of linear axes, further tests may show the value of certain 

metrics for prognostic purposes to estimate the remaining useful life (RUL). If the data collection and 

analysis is integrated within a machine controller, the process may be seamless. Data can be periodically 

collected to enable diagnostics and prognostics of linear axes for optimization of maintenance scheduling 

8 The test uncertainty ratio (TUR) is the ratio of the tolerance to the uncertainty of the measurement. 

Typically, a TUR of at least 4:1 is required; the larger, the better for a measurement system.

Linear Slide

DC Motor with Encoder

Sensor Box

1

2

3

Laser

Laser Sensor Boxes

4

5

1

2

3

4

5

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
53

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

and part quality. Therefore, the method is expected to result in reduced production losses for a machine 

tool.

This method will also enable verification and validation of other (built-in or otherwise) PHM techniques 

that aim to characterize translation and rotational errors and degradation. Likewise, this method will 

produce reference data sets that can be used by PHM developers as test data so they do not have to risk 

damaging their own equipment or impacting their productivity. This method will ultimately lead to 

standards to measure and predict linear axes degradation. 

6.2. Systems-Level Monitoring, Diagnostics, and Prognostics

The health and status of equipment on the shop floor can have far-reaching effects on the overall 

operations of a manufacturing facility. In fact, many of the inefficiencies in manufacturing, such as those 

created by machine degradation and failure, occur at the systems level. Understanding and responding to 

these inefficiencies requires manufacturers to collect and assess the most appropriate data to forecast 

faults and failures accurately and efficiently with no disruption to operations. However, systems are 

inherently complex and critical components often fail to communicate with each other effectively even if 

their operations are intimately connected. Other challenges also exist that prevent manufacturers from 

using digital technologies to collect data on the shop floor, such as the time and expertise needed to 

implement data collection and the challenge of handling and interpreting “big data.” Many of these issues 

have been highlighted in the workshop, such as the need for open-source communities, architectures, and 

infrastructures that deliver formatted PHM data to drive timely, actionable intelligence.

To address many of the information science needs in the PHM community, the PHM4SMS project is 

currently developing a systems-level test bed composed of networked machines and sensors in an active 

manufacturing facility. Figure 6-2 provides an overview of the high-level design of this test bed. A major 

aspect of this effort is the development of a reference implementation that enables manufacturers to 

collect relevant PHM data from heterogeneous systems on the shop floor without disruption to operations. 

This research will focus on the protocols and tools needed to communicate data, information, and metrics 

across the component, sub-system, and system levels for diagnostics and prognostics in manufacturing. 

They can also enable the prediction of the system-level impacts of events occurring at a single component 

or sub-system and may enhance process management and control approaches. In addition to 

standardization activities for PHM, these test bed efforts support several of the roadmap activities 

identified by the workshop, including PHM Data Format, Taxonomy, and Architecture; Overarching 

Architecture Framework for PHM with Standards and KPIs; Open-Source Community for PHM; and 

PHM Infrastructure to Deliver Relevant Timely Information.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
54

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Figure 6-2 – Overview of test bed design to collect and transmit data from various machines and sensors

The systems-level test bed will be a source of data once established. The PHM4SMS project will use this 

data to generate reference datasets of fabrication and inspection data that may be leveraged to identify 

useful links for improved diagnostics and process monitoring capabilities. Such research helps develop 

advanced sensing capabilities, which have been identified as an important roadmap activity during the 

workshop. The test bed will also enable the development of reference datasets for validation of diagnostic 

and prognostic standards and technologies. The data generated by the systems-level test bed will also be 

released directly to the public to support the broader PHM research community. Both of these activities 

help fill an important need highlighted during the workshop for PHM data, which is often difficult to 

extract and share to encourage collaboration and further innovation. This data can also support modeling, 

simulation, and visualization efforts, which are all challenges found for a variety of PHM topics discussed 

during the workshop.

The data and infrastructure provided by the test bed can also support external efforts to build PHM 

capabilities. For example, NIST is collaborating with the University of Virginia to develop a hierarchical 

methodology aimed at decomposing complex manufacturing systems-of-systems into their physical 

elements and corresponding functional processes to identify the most critical PHM needs to minimize 

system downtime. This methodology can be potentially verified and validated using the data and 

information flowing from the test bed. PHM researchers can also use the test bed data to build 

demonstration applications that can help build a business case for PHM and show the value of this 

capability to industry. Such activities are necessary to accelerate the development and adoption of PHM 

for smart manufacturing, which was another crosscutting challenge discussed during the workshop. 

6.3. PHM for Robotics

Robotics are increasing in their capabilities and range of applications in manufacturing. PHM 

considerations of a robotic system extend beyond just the robot arm; nearly every robotic system features 

an end-effector, sensors, safety system(s), supporting/surrounding automation, controller, etc. Robotic 

systems, especially in smart manufacturing environments, are often marked by complex interactions 

among these elements. For example, a fault or failure that presents itself as unexpected or inappropriate 

robot behavior is likely to have resulted not from a robot arm failure, but rather from a failure elsewhere 

in the system (e.g., sensor failure, controller fault, etc.). NIST’s PHM4SMS project is developing test 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
55

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

methods, metrics, assessment protocols, and reference data sets to support the assessment of condition 

monitoring, diagnostic, and prognostic techniques within the robotics domain including how degradation 

impacts key elements of the robot system. This research will be centered around a PHM4Robotics test

bed that is currently under construction and expected to be operational and produce its first data sets in 

2016.

Several key features stand out as being necessary for a successful PHM system for robotics. NIST 

research will address the measurement science challenges hindering further development.

 Real-time monitoring or ‘quick test’ method to identify the health of the robotic system –

Manufacturers cannot afford long production shutdowns to their lines or workcells. In the trend

towards smaller product volumes and increasing product variety, quick testing of reconfigured

workcells can be critical to improving productivity and reducing shutdown time. Research in this

area will address elements from several priority roadmaps including Identification of PHM

Performance Metrics, production of Failure Data for Prognostics and Diagnostics, and PHM

Infrastructure to Deliver Relevant Timely Information.

 Closed-loop solution - To achieve maximum OEE, a PHM system needs to efficiently determine

the root cause and appropriate remedy (i.e., course of action) of any fault or failure. These actions

can be more effectively accomplished in a closed-loop solution (as compared to an open loop

solution). Research into such a solution will encompass elements from several priority roadmaps

including PHM Data Format Taxonomy, and Architecture; Overarching Architecture Framework

for PHM with Standards and KPIs; and PHM Infrastructure to Deliver Relevant Timely

Information.

 Intelligent sensing – New, advanced sensing clouds or fusion of existing sensors need to be

developed to address complex and/or reconfigurable robotic applications. This research will

address some of the elements found in the Advanced Sensors for PHM roadmap.

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
56

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Figure 6-3 – Key Building Blocks of the PHM4Robotics Test Bed

Figure 6-3 presents the key modules of the closed-loop PHM4Robotics test bed. NIST will be developing 

each one of these modules to serve as the foundation for much of its measurement science research. The 

development and integration of these modules will enable the design of relevant test methods and use case 

scenarios to assess PHM operating within robotic systems and enable the verification and validation of 

external modules. Likewise, the PHM4Robotics test bed will serve as a data production platform to 

generate reference data sets including those data presenting precursors to various faults and failures. 

Although the test bed will originally be constructed with a currently-available industrial robot, all of the 

modules and measurement science will be designed to be robot agnostic. The generation of reference data 

sets and purposeful focus of the measurement science to be robot agnostic will leverage the Open-Source 

Community for PHM priority roadmap. This will result in numerous contributions including 1) enabling 

correlation analyses for root cause fault detection since data will be captured from the component to the 

system levels, 2) producing reference data from a wide range of sensors, and 3) supporting the 

development of a standard platform for sensing, data storage, dissemination, etc.

The development and expansion of each module shown in Figure 6-3 will further address elements of the 

priority roadmaps. For example, as the sensing module is developed, the research team will inventory 

existing and potentially-relevant sensing technologies that can integrated with industrial robotic systems 

to promote richer situational awareness (addressing elements of the Advanced Sensors for PHM 

roadmap). This activity will lead to understanding the raw and output data formats to determine how this 

information can be appropriately structured and classified for broad use (addressing elements of the PHM 

Data Format, Taxonomy, and Architecture roadmap). 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
57

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

6.4. Summary

NIST research efforts are aimed at enhancing the industrial competitiveness of U.S. industry within this 

specific scope of manufacturing PHM. The research highlighted in this section provides a glimpse of both 

the overall efforts, and their specific status, to inform the PHM community. As progress is made on these 

efforts, NIST researchers will continue to evolve their plans to meet the needs of industry. 

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
58

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

7. Appendices

Appendix A. Contributors

Name Affiliation

Brian Antonishek NIST

Peter Beling University of Virginia

Michael Bodach Boeing

Patrick Brown IMS Center (University of Cincinnati)

Carl Byington Impact Tech/Sikorsky

Dan Carnahan Rockwell Automation

Jamie Coble University of Tennessee

Em Delahostria Rockwell Automation

Dave Edstrom Memex Automation

Neil Eklund Schlumberger

Miguel Veregas Franbrana University of Maryland

Kai Goebel NASA

David He University of Illinois-Chicago

Moneer Helu NIST

Andrew Hess The Hess PHM Group

Peng Y. Huang Cosen Saws

Andy Inman Toyota

Xiaoning Jin University of Michigan

Harry Kekedjian Ford Motor Company

Agnes Klucha UTC Aerospace Systems

Edzel Lapira Predictronics Corp.

Jay Lee IMS Center, University of Cincinnati

Jingshan Li University of Wisconsin - Madison

Yang Li University of Michigan

Linxia Liao Palo Alto Research Center (PARC)

Wentao Luan University of Maryland

Bruce Mahone SAE International

Dipanleus Maily University of Maryland

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
59

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Name Affiliation

K.C. Morris NIST

Bill Marscher Mechanical Solutions, Inc.

Tom Mooney Soar Engineering LLC

Noah Myrent Vanderbilt University

Chris Nemarich U.S. Navy Military Sealift Command

Ann Marie O'Connell Johnson Controls Inc.

Dan Oropeza Lockheed Martin

John Oskin Sage Clarity

Rahul Palnitkar Expert Microsystems

Nicholas Propes Global Technology Connection

Karl Reichard Penn State Applied Research Lab

Justinian Rosca Siemens Corporate Technology

Al Salour The Boeing Company

Peter Sandborn University of Maryland

David Siegel Predictronics Corp.

Will Sobel System Insights

Brinda Thomas Tesla Motors

Jonathan Vance The Boeing Company

Greg Vogl NIST

Brian A. Weiss NIST

Miguel Zambrana University of Maryland

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2
60

Measurement Science Roadmap for Prognostics and 

Health Management of Smart Manufacturing System

Appendix B. Acronyms

CBM Condition-based maintenance

CNC Computer Numerical Control

DAQ Data acquisition

EL NIST Engineering Laboratory

EPA Environmental Protection Agency

FMECA Failure mode, effects, and criticality analysis

HMI Human machine interface

HW Hardware

ISO International Standards Organization

IT Information technology

KPI Key performance indicator

LRU Line replaceable unit

MIMOSA Machinery Information Management Open Systems Alliance

MSV Models, Simulation, and Visualization 

MTConnect Manufacturing industry standard to facilitate the organized retrieval of process information from 

numerically controlled machine tools

MTTF Mean time to failure

N/A Not applicable

NIST National Institute of Standards and Technology 

NT Network

OEE Overall equipment effectiveness

OPXC Optical Path Crossconnect System

PHM Prognostics and Health Management

PHM4SMS PHM for Smart Manufacturing Systems

PLC Programmable logic controller

RFID Radio frequency identification

ROI Return on investment

RUL Remaining useful life

SCADA/CMMS Supervisory Control and Data Acquisition/ Computerized Maintenance Management System

SHM Structural Health Management

SMOPAC NIST Smart Manufacturing Operations Planning and Control program

STEP-NC Machine tool control language that extends the ISO 10303 STEP standards with the machining 

model in ISO 14649

SW Software

TUR Test Uncertainty Ratio

WIN Windows

__________________________________________________________________________________ 

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.AMS.100-2