Fault Diagnosis of Nonlinear Systems using LPV Interval Observers

In this paper, an interval model-based fault diagn osis method for nonlinear system described by means ofLinear Parameter Varying(LPV) model is proposed. The methodology is based on computi ng residuals, indicators that are obtained comparing measure d inputs and outputs signals with analytical relationships, which are obtained by system modeling. The p roposed detection method uses a LPV interval observeras a passive robust strategy to generate adaptive threshold s by propagating uncertainty to the residuals. The isolation task considers the knowledg e about the faulty system behavior using the fault sensitivity concept. The innovation of this methodol ogy is based on the use of the fault estimation to isolate the faults. Finally, the minimum detectable and i solable fault of the proposed method are characterized. An application based on quadruple-tank pro cess is used to validate the proposed fault diagnosis approach.

[1]  Pierre Apkarian,et al.  Self-scheduled H∞ control of linear parameter-varying systems: a design example , 1995, Autom..

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

[3]  Dan T. Horak.  Failure detection in dynamic systems with modeling errors , 1987 .

[4]  M. Athans,et al.  Gain Scheduling: Potential Hazards and Possible Remedies , 1992, 1991 American Control Conference.

[5]  Karl Henrik Johansson,et al.  The quadruple-tank process: a multivariable laboratory process with an adjustable zero , 2000, IEEE Trans. Control. Syst. Technol..

[6]  Vicenç Puig,et al.  Observer gain effect in linear interval observer-based fault detection , 2010, 2007 46th IEEE Conference on Decision and Control.

[7]  Vicenç Puig,et al.  Passive Robust Fault Detection of Dynamic Processes Using Interval Models , 2008, IEEE Transactions on Control Systems Technology.

[8]  J. Bokor,et al.  Failure detection for quasi LPV systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[9]  F. Schmid,et al.  A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[10]  Roderick Murray-Smith,et al.  Multiple Model Approaches to Modelling and Control , 1997 .

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