Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98511
Title: Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns
Authors: Yu, Ming
Wang, Danwei
Luo, Ming
Zhang, Danhong
Chen, Qijun
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Series/Report no.: Expert systems with applications
Abstract: This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method.
URI: https://hdl.handle.net/10356/98511
http://hdl.handle.net/10220/11115
DOI: 10.1016/j.eswa.2012.01.103
Rights: © 2012 Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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