NDLI: Fault diagnosis for high order LTI systems based on model decomposition

Content Provider	IEEE Xplore Digital Library
Author	Lihua Liu Xiukun Wei Xiaohe Liu
Copyright Year	2012
Description	Author affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, China (Xiukun Wei) \|\| Institute of Automation, Beijing Information Science and Technology University, 100192, China (Lihua Liu; Xiaohe Liu)
Abstract	Fault detection observer and fault estimation filter are the main tools for the model based fault diagnosis approach. The dimension of the observer gain normally depends on the system order and the system output dimension. The fault estimation filter traditionally has the same system dimension of the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real implementation on-board for applications. In this paper, the system dynamical model is first decomposed into a special structure. With the new model, a fault detection system can be designed such that only the residuals with the same dimension as the size of the faults are sensitive to the faults. The rest residuals are totally decoupled from the faults. A lower order (with the same size of the fault) fault estimation filter design approach is proposed. Further, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The proposed method is demonstrated by a simulation example.
Starting Page	3390
Ending Page	3395
File Size	623186
Page Count	6
File Format	PDF
ISBN	9781457720734
e-ISBN	9781457720741
DOI	10.1109/CCDC.2012.6244540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	filter Fault diagnosis observer model decomposition Fault detection high order Observers Robustness Vectors Matrix decomposition Servers
Content Type	Text
Resource Type	Article

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