Paper Info
Title
Sequential multiple hypothesis testing and efficient fault detection-isolation in stochastic systems
Abstract
This paper develops information-theoretic bounds for sequential multihypothesis testing and fault detection in stochastic systems. Making use of these bounds and likelihood methods, it provides a new unified approach to efficient detection of abrupt changes in stochastic systems and isolation of the source of the change upon its detection. The approach not only generalizes previous work in the literature on asymptotically optimal detection-isolation far beyond the relatively simple models treated but also suggests alternative performance criteria which are more tractable and more appropriate for general stochastic systems
Year
DOI
Venue
2000
10.1109/18.825826
IEEE Transactions on Information Theory
Keywords
Field
DocType
optimisation,efficient fault detection-isolation,error probability,fault detection,stochastic systems,efficient detection,general stochastic system,stochastic system,new unified approach,information-theoretic bound,stochastic systems isolation,sequential multiple hypothesis testing,information theory,asymptotically optimal detection-isolation,likelihood methods,efficient fault detection,information-theoretic bounds,alternative performance criterion,abrupt change detection,generalizes previous work,abrupt change,performance criteria,error statistics,signal detection,sequential analysis,density functional theory,multiple hypothesis testing,system testing,bayesian methods,indexing terms
Information theory,Detection theory,Fault detection and isolation,Algorithm,Multiple comparisons problem,Artificial intelligence,Asymptotically optimal algorithm,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
46
2
0018-9448
Citations 
PageRank 
References 
22
2.69
10
Authors
1
Name
Order
Citations
PageRank
Tze Leung Lai18915.87