Premium
Mixed‐data multi‐modelling for fault detection and isolation
Author(s) -
Kárný Miroslav,
Nagy Ivan,
Novovičová Jana
Publication year - 2002
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/acs.672
Subject(s) - fault detection and isolation , isolation (microbiology) , computer science , probabilistic logic , task (project management) , simple (philosophy) , exponential function , bayesian probability , bayes' theorem , computational complexity theory , algorithm , data mining , artificial intelligence , engineering , mathematics , philosophy , mathematical analysis , systems engineering , epistemology , microbiology and biotechnology , actuator , biology
Early recognition/isolation of a faulty behaviour of a dynamic system is the main task of a fault detection and isolation (FDI). FDI methods based on adaptive probabilistic models with multiple modes represent a theoretically well justified way of solution. Their use is severely restricted by an inherent computational complexity. The complexity problem is addressed here by employing an efficient quasi‐Bayes estimation algorithm. It is directly applicable to the mixture of components created as products of factors belonging to the exponential family. It opens a novel way to deal adaptively with mixed continuous–discrete, dynamically related data. The presented theory and algorithmization are illustrated by a simple simulation example. Copyright © 2001 John Wiley & Sons, Ltd.