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Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

Published

Author(s)

Benjamin Y. Choo, Brian Weiss, Jeremy Marvel, Stephen C. Adams, Peter A. Beling

Abstract

Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. As a rule, PHM information is not yet fully utilized in higher-level decision-making in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for the optimal policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.
Citation
International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems

Keywords

Prognostics and health management, Preventive and predictive maintenance, Smart manufacturing

Citation

Choo, B. , Weiss, B. , Marvel, J. , Adams, S. and Beling, P. (2017), Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems, International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920634 (Accessed April 21, 2024)
Created February 9, 2017, Updated October 12, 2021