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


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.


Diagnostics Prognostics Maintenance Manufacturing Health management 



Advanced process control


Artificial neural network


Condition-based maintenance


Cost-benefit analysis


Discrete events system


Failure mode and effects analysis


Failure mode, effects, and criticality analysis


Fault tree analysis


Integrated vehicle health management


Key performance indicator


Principal component analysis


Probability density function


Prognostics and health management


Programmable logic controller


Remaining useful life


Return on investment


Support vector machine


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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