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A review of diagnostic and prognostic capabilities and best practices for manufacturing

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Abstract

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

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Notes

  1. Official contribution of the National Institute of Standards and Technology (NIST); not subject to copyright in the United States. Certain commercial products, some of which are either registered or trademarked, are identified in this paper in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by NIST, nor does it imply that the materials, equipment, or software identified are necessarily the best available for the purpose.

Abbreviations

(APC):

Advanced process control

(ANN):

Artificial neural network

(CBM):

Condition-based maintenance

(CBA):

Cost-benefit analysis

(DES):

Discrete events system

(FMEA):

Failure mode and effects analysis

(FMECA):

Failure mode, effects, and criticality analysis

(FTA):

Fault tree analysis

(IVHM):

Integrated vehicle health management

(KPI):

Key performance indicator

(PCA):

Principal component analysis

(PDF):

Probability density function

(PHM):

Prognostics and health management

(PLC):

Programmable logic controller

(RUL):

Remaining useful life

(ROI):

Return on investment

(SVM):

Support vector machine

(TBM):

Time-based maintenance

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Authors and Affiliations

  1. Engineering Laboratory, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, MD, 20899-8220, USA

    Gregory W. Vogl, Brian A. Weiss & Moneer Helu

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  1. Gregory W. Vogl
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Correspondence to Gregory W. Vogl.

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Vogl, G.W., Weiss, B.A. & Helu, M. A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf 30, 79–95 (2019). https://doi.org/10.1007/s10845-016-1228-8

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