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Journal of Intelligent Manufacturing
Published in: Journal of Intelligent Manufacturing 1/2019

09-06-2016

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

Authors: Gregory W. Vogl, Brian A. Weiss, Moneer Helu

Published in: Journal of Intelligent Manufacturing | Issue 1/2019

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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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Footnotes
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.
 
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Metadata
Title
A review of diagnostic and prognostic capabilities and best practices for manufacturing
Authors
Gregory W. Vogl
Brian A. Weiss
Moneer Helu
Publication date
09-06-2016
Publisher
Springer US
Published in
Journal of Intelligent Manufacturing / Issue 1/2019
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-016-1228-8

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