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

Content Provider	IEEE Xplore Digital Library
Author	Tesheng Hsiao Mao-Chiao Weng
Copyright Year	2011
Description	Author affiliation: Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, 30010, Taiwan (Mao-Chiao Weng) \|\| Department of Electrical Engineering, National Chiao Tung University, Hsinchu, 30010, Taiwan (Tesheng Hsiao)
Abstract	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.
Starting Page	3718
Ending Page	3723
File Size	328815
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heuristic algorithms Computational modeling Manipulator dynamics Joints Kinematics Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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