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用于机器人装配过程的自适应多尺度故障预测与健康管理

Adaptive Multi-scale PHM for Robotic Assembly Processes.

作者信息

Choo Benjamin Y, Beling Peter A, LaViers Amy E, Marvel Jeremy A, Weiss Brian A

机构信息

University of Virginia, Charlottesville, Virginia, 22904, USA.

National Institute of Standards and Technology, Gaithersburg, Maryland, 20899, USA.

出版信息

Proc Annu Conf Progn Health Manag Soc. 2015;6:037.

Abstract

Adaptive multiscale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. As a rule, PHM information is not used in high-level decision-making in manufacturing systems. AM-PHM leverages and integrates component-level PHM information with hierarchical relationships across the component, machine, work cell, and production 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. A description of the AM-PHM methodology with a simulated canonical robotic assembly process is presented.

摘要

自适应多尺度预测与健康管理(AM-PHM)是一种旨在支持智能制造系统中的预测与健康管理(PHM)的方法。通常,PHM信息在制造系统的高层决策中并不使用。AM-PHM利用并整合了组件级的PHM信息,以及制造系统中组件、机器、工作单元和生产线各级之间的层次关系。AM-PHM方法能够在制造过程层次结构的上下文中创建可操作的预测和诊断智能。在沿着层次结构节点的决策点上,基于系统当前和预测的健康状态的知识来做出决策。本文介绍了AM-PHM方法以及一个模拟的典型机器人装配过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37d1/5486223/e64c21da2bae/nihms866008f1.jpg

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