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A multi‐model method to fault detection and diagnosis: Bayesian solution. An introductory treatise
Author(s) -
Berec Luděk
Publication year - 1998
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/(sici)1099-1115(199802)12:1<81::aid-acs474>3.0.co;2-b
Subject(s) - fault detection and isolation , computer science , fault (geology) , bayesian probability , range (aeronautics) , action (physics) , algorithm , artificial intelligence , engineering , quantum mechanics , physics , aerospace engineering , seismology , actuator , geology
In the paper, a method for solving fault detection and diagnosis problems in sampled‐data stochastic systems is presented. As a main methodology tool the Bayesian view on uncertainty is exploited. The method can be classified as of a multi‐model type. It requires to supply mathematical models of the system dynamics, each describing the situation when a particualr fault separately acts on the system or when the system behaves normally (i.e. as desired). At the stage of research, discrete‐time stochastic causal input–output non‐parametrized models are supported. As discrete‐time courses, model of the actual system behaviour is recursively estimated and a decision on the actually acting fault is given. The presented method solves both the fault detection and diagnosis tasks simultaneously. Three illustrative examples show the method in action, possibly demonstrating a range of its applications. © 1998 John Wiley & Sons, Ltd.