Detecting faults in dynamic and bounded stochastic distributions: an observer based technique

Author

Wang, Hong

Author_Institution

Dept. of Paper Sci., Univ. of Manchester Inst. of Sci. & Technol., UK

Volume

1

fYear

2001

fDate

2001

Firstpage

482

Abstract

Presents an approach to detect and diagnose faults in the dynamic part of a class of stochastic systems. Such a group of systems are subjected to a set of crisp inputs but the outputs considered are the measurable probability density functions of the system output, rather than the system output alone. A new approximation model is developed for the output probability density functions so that the dynamic part of the system is decoupled from the output probability density function. A nonlinear adaptive observer is constructed to detect and diagnose the fault in the dynamic part of the system. Convergency analysis is performed for the error dynamics,raised from the fault detection and diagnosis phase and an applicability study on the detection of the unexpected changes in the 2D grammage distributions in the paper forming process is included

Keywords

convergence; fault diagnosis; observers; paper industry; probability; stochastic systems; 2D grammage distributions; approximation model; bounded stochastic distributions; convergency analysis; crisp inputs; dynamic distributions; error dynamics; fault diagnosis; measurable probability density functions; nonlinear adaptive observer; observer based techniques; paper forming process; stochastic systems; unexpected changes; Control systems; Density measurement; Fault detection; Fault diagnosis; Nonlinear dynamical systems; Probability density function; Signal detection; Spline; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2001. Proceedings of the 2001

Conference_Location

Arlington, VA

ISSN

0743-1619

Print_ISBN

0-7803-6495-3

Type

conf

DOI

10.1109/ACC.2001.945591

Filename

945591