Date: 26 Jan 2013

Fault diagnosis for high order systems based on model decomposition

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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 order as the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real time implementation on-board for applications. In this paper, the system dynamical model is first decomposed into two subsystems. The first subsystem has a low order which is the same as the fault dimension. The other subsystem is not affected by the fault directly. With the new model structure, a fault detection approach is proposed where only the residual of the first subsystem is designed to be sensitive to the faults. The residual of the second subsystem is totally decoupled from the faults. Moreover, a lower order fault estimation filter (with the same dimension of the fault) design algorithm is investigated. In addition, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The effectiveness of the proposed method is demonstrated by a simulation example.

Recommended by Editorial Board member Bin Jiang under the direction of Editor Myotaeg Lim.
This work is partly supported by Ph.D. Programs Foundation of Ministry of Education of China (Grant number: 2011000912 0037), Chinese 863 program (Contract No. 2011AA110503-6) and State Key Laboratory of Rail Traffic Control and Safety (Contract No.RCS2011ZZ005).
Xiukun Wei received his Ph.D. degree from Johannes Kepler University, Linz, Austria. Currently he is an associate professor at the State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, China. From 2006 to 2009, he was a PostDoc Researcher at Delft Center for System and Control, Delft University of Technology, Delft, The Netherlands. From 2002 to 2006, he held a Research Assistant position at the Institute of Design and Control of Mechatronical Systems, Johannes Kepler University. His research interests include fault diagnosis and its applications, Intelligent Transportation System, Control theory applications in a variety of fields such as Rail Traffic Control and Safety, Transportation.
Lihua Liu is an associate professor at Beijing Information and Technology university. Her research interests include control theory, fault diagnosis and their applications.
Limin Jia is a professor at the State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, China. His research interests include Intelligent Control, System Safety, Fault Diagnosis and their applications in a variety of fields such as Rail Traffic Control and Safety, Transportation and etc.