A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots

被引：0

作者：

Vallachira, Sathish ^{[1
,2
]}

Norrlof, Mikael ^{[3
]}

Orkisz, Michal ^{[4
]}

Butail, Sachit ^{[5
]}

机构：

[1] Robotics Service Intelligence Unit, ABB Ability Innovation Center, Karnataka, Bangalore

[2] Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, New Delhi

[3] ABB Robotics Division, Vasteras

[4] ABB Corporate Technology Center, Krakow

[5] Department of Mechanical Engineering, Northern Illinois University, DeKalb, 60115, IL

来源：

ASME Letters in Dynamic Systems and Control | 2022年 / 2卷 / 01期

关键词：

complex systems; fault detection and diagnosis; robotics;

D O I：

10.1115/1.4051565

中图分类号：

学科分类号：

摘要：

In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the related efficacy of the information-theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear-induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy-based approach is able to detect the axis of failure more than 80% of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time series analysis used to identify directional dependence among processes. Copyright © 2021 by ASME.

引用

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