On the Detection of Small Parameter Variations in Linear Uncertain Systems

The problem of detecting small parameter variations in linear uncertain systems, with the possibility of injecting an input signal to enhance detection, is considered. A constructive method for the construction of an optimal input signal for achieving guaranteed detection with specified precision is presented. The method is an extension of the multi-model approach used for the construction of auxiliary signals for failure detection. The application to the problem of incipient fault detection is investigated.

[1]  Marios M. Polycarpou,et al.  Incipient fault diagnosis of dynamical systems using online approximators , 1998 .

[2]  Stephen L. Campbell,et al.  A Multi-Model Approach to Failure Detection in Uncertain Sampled-Data Systems , 2005, Eur. J. Control.

[3]  François Delebecque,et al.  Rapid Model Selection and the Separability Index , 2000 .

[4]  R. Nikoukhah,et al.  Robust detection of incipient faults: an active approach , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[5]  Keith R. Godfrey,et al.  Perturbation signals for system identification , 1993 .

[6]  D. Choe,et al.  Model based failure detection using test signals from linearizations: A case study , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[7]  Ramine Nikoukhah,et al.  Auxiliary Signal Design for Failure Detection , 2004 .

[8]  Hartmut Bossel,et al.  Modeling and simulation , 1994 .

[9]  M. Zarrop,et al.  Input design for detection of abrupt changes in dynamical systems , 1994 .

[10]  L. Blackmore,et al.  Finite Horizon Control Design for Optimal Model Discrimination , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[11]  Rolf Isermann,et al.  Fault-diagnosis systems : an introduction from fault detection to fault tolerance , 2006 .

[12]  Bin Jiang,et al.  Active fault tolerant control for a class of nonlinear systems , 2003 .

[13]  Alexander G. Parlos,et al.  Incipient fault detection and identification in process systems using accelerated neural network learning , 1994 .

[14]  Stephen L. Campbell,et al.  Auxiliary signal design for active failure detection in uncertain linear systems with a priori information , 2006, Autom..

[15]  Mo-Yuen Chow,et al.  Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part I-Methodology , 1995, IEEE Trans. Ind. Electron..

[16]  Paul M. Frank,et al.  Dynamic Model Based Incipient Fault Detection Concept for Robots , 1990 .

[17]  Feza Kerestecioglu,et al.  Change Detection and Input Design in Dynamical Systems , 1993 .

[18]  Xue Jun Zhang,et al.  Auxiliary Signal Design in Fault Detection and Diagnosis , 1989 .

[19]  François Delebecque,et al.  Detection signal design for failure detection: a robust approach , 2000 .

[20]  Rihard Karba,et al.  Robust fault detection based on compensation of the modelling error , 2002, Int. J. Syst. Sci..

[21]  Henrik Niemann,et al.  A Setup for Active Fault Diagnosis , 2006, IEEE Transactions on Automatic Control.

[22]  Katsuji Uosaki,et al.  Optimal Input Design for Autoregressive Model Discrimination with Constrained Output Variance , 1983, 1983 American Control Conference.

[23]  Michael A. Demetriou.  Robust adaptive techniques for sensor fault detection and diagnosis , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[24]  Hong Wang,et al.  Filter‐based fault detection and diagnosis using output PDFs for stochastic systems with time delays , 2006 .

[25]  Mo-Yuen Chow,et al.  Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors: Part II-Application , 1995, IEEE Trans. Ind. Electron..

[26]  Mo-Yuen Chow.  Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection , 1998 .

[27]  Hans Henrik Niemann.  ACTIVE FAULT DIAGNOSIS IN CLOSED-LOOP UNCERTAIN SYSTEMS , 2006 .

[28]  M. A. Demetriou,et al.  Fault detection and diagnosis of a class of actuator failures via online approximators , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[29]  Ramine Nikoukhah,et al.  Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems , 1998, Autom..

[30]  Jung-Shan Lin,et al.  Sensorless speed tracking control with backstepping design scheme for permanent magnet synchronous motors , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[31]  Jean-Philippe Chancelier,et al.  Modeling and Simulation in Scilab/Scicos , 2006 .

[32]  François Delebecque,et al.  Auxiliary signal design for robust multimodel identification , 2002, IEEE Trans. Autom. Control..

[33]  Marios M. Polycarpou,et al.  A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems , 2002, IEEE Trans. Autom. Control..

[34]  M. Simandl,et al.  Rolling horizon for active fault detection , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[35]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.