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Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 79–95 | Cite as

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

  • Gregory W. VoglEmail author
  • Brian A. Weiss
  • Moneer Helu
Article

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.

Keywords

Diagnostics Prognostics Maintenance Manufacturing Health management 

Terminology

(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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Copyright information

© Springer Science+Business Media New York (outside the USA) 2016

Authors and Affiliations

  • Gregory W. Vogl
    • 1
    Email author
  • Brian A. Weiss
    • 1
  • Moneer Helu
    • 1
  1. 1.Engineering LaboratoryNational Institute of Standards and Technology (NIST)GaithersburgUSA

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