Fault diagnosis for switching system using Observer Kalman filter IDentification

In this paper we propose a strategy for fault detection and isolation without any fixed model of the system to be supervised. The proposed approach is based on the identification of the parameters characterizing the system without any a priori knowledge. Our contribution consists in developing a specific identification scheme that is insensitive to a certain type of faults. The identified parameters are then invariant to the presence of actuator or sensor faults. Thereafter, a fault estimation procedure is proposed in order to detect sensor or actuator faults. The paper ends with a simulation example which highlights the effectiveness of the proposed approach.

[1]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[2]  Michèle Basseville,et al.  Detection of abrupt changes , 1993 .

[3]  S. Lecœuche,et al.  MODELLING OF NON STATIONARY SYSTEMS BASED ON A DYNAMICAL DECISION SPACE , 2006 .

[4]  Didier Maquin,et al.  Sliding mode multiple observer for fault detection and isolation , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[5]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[6]  M. Darouach,et al.  Full-order observers for linear systems with unknown inputs , 1994, IEEE Trans. Autom. Control..

[7]  Giorgio Battistelli,et al.  Receding-horizon estimation for switching discrete-time linear systems , 2005, IEEE Transactions on Automatic Control.

[8]  Stéphane Lecoeuche,et al.  Online clustering of switching models based on a subspace framework , 2008 .

[9]  Richard W. Longman,et al.  Improvement of observer/Kalman filter identification (OKID) by residual whitening , 1992 .

[10]  Frédéric Kratz,et al.  Finite memory generalised state observer for failure detection in dynamic systems , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[11]  J. Ragot,et al.  Identification of switching systems using change detection technique in the subspace framework , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[12]  Didier Maquin,et al.  Finite memory observer for switching systems: Application to diagnosis , 2004 .

[13]  J. Juang.  Applied system identification , 1994 .

[14]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.