Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems
By Benjamin Y. Choo, Stephen C. Adams, B. Weiss, Jeremy A. Marvel, Peter A. Beling. 2020
13 citations recorded by the source corpus.
The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, 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 a 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 then 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 an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.
Subfields: Control and Systems Engineering, Medical Laboratory Technology
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Citations in the corpus
- Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication (2020, positive): “Inspired by [3,4], we developed this manufacturing line environment which consists of machines of different types and requires constant cooperation between agents.”
- Health-aware hierarchical control for smart manufacturing using reinforcement learning (2017, background): “proposed Adaptive Multi-scale PHM (AM-PHM), approach for system-wide health-aware decision making [10], [11].”