Optimal observation strategies for model-based fault detection in distributed systems

In model oriented diagnostics of real-world systems, the problems of structural identification and parameter estimation are of crucial importance. They require a properly designed schedule of measurements in such a way as to obtain possibly the most informative observational data. The aim of this work is to develop a novel approach to fault detection in distributed systems based on the maximization of the power of parametric hypothesis test, which verifies the nominal state of the considered system. The optimal locations of sensors are determined using the performance index operating on the Fisher Information Matrix. A general scheme is then proposed and tested on a computer example regarding an advection-diffusion problem.

[1]  Maciej Patan,et al.  OPTIMAL ACTIVATION STRATEGY OF DISCRETE SCANNING SENSORS FOR FAULT DETECTION IN DISTRIBUTED-PARAMETER SYSTEMS , 2005 .

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

[3]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[4]  D.G. Dudley,et al.  Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.

[5]  Maciej Patan,et al.  Optimal Observation Strategies for Parameter Estimation of Distributed Systems , 2004 .

[6]  N. Sun.  Inverse problems in groundwater modeling , 1994 .

[7]  Douglas W. Nychka,et al.  Case Studies in Environmental Statistics , 1998 .

[8]  Douglas W. Nychka,et al.  Design of Air-Quality Monitoring Networks , 1998 .

[9]  Rolf Isermann,et al.  Preface to the special section of papers on supervision, fault detection and diagnosis of technical systems , 1997 .

[10]  D. Ucinski.  Optimal measurement methods for distributed parameter system identification , 2004 .

[11]  Robert M. Pap,et al.  Fault Diagnosis , 1990, Bayesian Networks in Fault Diagnosis.

[12]  Anthony C. Atkinson,et al.  Optimum Experimental Designs , 1992 .

[13]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[14]  W. Cholewa,et al.  Fault Diagnosis: Models, Artificial Intelligence, Applications , 2004 .

[15]  D. Ucinski.  Optimal Selection of Measurement Locations for Parameter Estimation in Distributed Processes , 2000 .

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

[17]  Alison B. Flatau,et al.  Dynamic smart material and structural systems , 2002 .

[18]  Michael A. Demetriou.  Activation Policy of Smart Controllers for Flexible Structures with Multiple Actuator/Sensor Pairs , 2000 .

[19]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[20]  Andrej Pázman,et al.  Foundations of Optimum Experimental Design , 1986 .

[21]  Hans-Paul Schwefel,et al.  Advances in Computational Intelligence , 2003, Natural Computing Series.

[22]  Marc M. J. van de Wal,et al.  A review of methods for input/output selection , 2001, Autom..

[23]  Paul H. Garthwaite,et al.  Statistical Inference , 2002 .

[24]  Harvey Thomas Banks,et al.  Smart material structures: Modeling, estimation, and control , 1996 .

[25]  Steven E. Rigdon,et al.  Model-Oriented Design of Experiments , 1997, Technometrics.

[26]  P. Laycock,et al.  Optimum Experimental Designs , 1995 .

[27]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .

[28]  C. G Glueke,et al.  Numerical Models in Groundwater Pollution , 2002 .

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

[30]  D. Ucinski.  Optimal sensor location for parameter estimation of distributed processes , 2000 .

[31]  Paul M. Frank,et al.  New developments using AI in fault diagnosis , 1996 .

[32]  Michael Jackson,et al.  Optimal Design of Experiments , 1994 .

[33]  Werner G. Müller,et al.  Collecting Spatial Data: Optimum Design of Experiments for Random Fields , 1998 .

[34]  Carlos S. Kubrusly,et al.  Sensors and controllers location in distributed systems - A survey , 1985, Autom..