Adaptive Multi-scale PHM for Robotic Assembly Processes

Benjamin Choo, Amy LaViers, Jeremy Marvel, Brian A. Weiss, and Peter A. Beling
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Nationa Institute of Standards and Technology (NIST)
phmc_15_037.pdf482.9 KBAugust 21, 2015 - 12:03pm

Adaptive multi-scale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. AM-PHM leverages and integrates multi-level, hierarchical relationships and PHM information gathered from a manufacturing system. This enables the AM-PHM methodology to create 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.
This paper describes the AM-PHM concept and its application to a canonical example robotic assembly process involving two robots working in series. There are multiple solutions for each robot to complete their specified task and each robot can be reprogrammed to perform the other's task. A Systems Modeling Language (SysML) representation of the canonical process is created and semi-automated algorithms are used to extract hierarchical information and propagate PHM data up the structure. Results show that additional health information provided to the decision-makers allow for improved efficiency of the manufacturing system.

Publication Year: 
Publication Volume: 
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Submission Keywords: 
smart manufacturing
Fault Tree
remaining useful life (RUL)
Submission Topic Areas: 
Standards and methodologies
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