Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-10T07:59:14.575Z Has data issue: false hasContentIssue false

A Model-Based Fault Detection and Diagnosis SchemeforDistributed Parameter Systems: A Learning Systems Approach

Published online by Cambridge University Press:  15 September 2002

Michael A. Demetriou*
Affiliation:
Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.; mdemetri@wpi.edu.
Get access

Abstract

In this note, fault detection techniques based on finite dimensionalresults are extended and applied to a class of infinite dimensionaldynamical systems. This special class of systems assumes linearplant dynamics having an abrupt additive perturbation as the fault.This fault is assumed to be linear in the (unknown) constant (and possiblyfunctional) parameters.An observer-based model estimate is proposed which servesto monitor the system's dynamics for unanticipated failures,and its well posedness is summarized.Using a Lyapunov synthesis approach extended and applied to infinitedimensional systems, a stable adaptive fault diagnosis(fault parameter learning) scheme is developed. The resulting parameteradaptation rule is able to “sense” the instance of the fault occurrence.In addition, it identifies the fault parameters using the additionalassumption of persistence of excitation. Extension of the adaptivemonitoring scheme to incipient faults (time varying faults) is summarized.Simulations studies are used to illustrate the applicabilityof the theoretical results.

Information

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable