A dual-model fault detection approach with application to actuators of robot manipulators

Te-Sheng Hsiao*, Mao Chiao Weng

*Corresponding author for this work

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Multiple-model (MM)-based methods have been successfully applied to many fault detection schemes; however systematic design of the associated model set remains an open question. The difficulty comes from the fact that using a large model set reduces the risk of undetected faults, but also increases the computation load drastically. In this paper we propose a dual-model fault detection (DMFD) algorithm aiming at solving the model set design problem, and apply it to detect actuator faults of robot manipulators. The DMFD algorithm is able to detect various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults, in a computationally efficient way. To evaluate the performance of the DMFD algorithm, upper bounds of the false alarm and missed detection probabilities are explicitly presented in terms of the tunable variables. Furthermore, experiments are conducted to demonstrate its ability in immediate detection of faults.

原文English
主出版物標題2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
頁面3718-3723
頁數6
DOIs
出版狀態Published - 1 十二月 2011
事件2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
持續時間: 12 十二月 201115 十二月 2011

出版系列

名字Proceedings of the IEEE Conference on Decision and Control
ISSN(列印)0191-2216

Conference

Conference2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
國家United States
城市Orlando, FL
期間12/12/1115/12/11

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    Hsiao, T-S., & Weng, M. C. (2011). A dual-model fault detection approach with application to actuators of robot manipulators. 於 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 (頁 3718-3723). [6160416] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2011.6160416