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

Gregory W. Vogl (), Brian A. Weiss () and Moneer Helu ()
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Gregory W. Vogl: National Institute of Standards and Technology (NIST)
Brian A. Weiss: National Institute of Standards and Technology (NIST)
Moneer Helu: National Institute of Standards and Technology (NIST)

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 1, No 8, 79-95

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (18)

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DOI: 10.1007/s10845-016-1228-8

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