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

Published

Author(s)

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

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 discusses 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.
Citation
Journal of Intelligent Manufacturing

Keywords

Diagnostics, Prognostics, Maintenance, Manufacturing, Health management

Citation

Vogl, G. , Weiss, B. and Helu, M. (2016), A review of diagnostic and prognostic capabilities and best practices for manufacturing, Journal of Intelligent Manufacturing, [online], https://doi.org/10.1007/s10845-016-1228-8 (Accessed May 27, 2024)

Issues

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Created June 9, 2016, Updated November 10, 2018