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A bayesian approach to fault isolation - Structure estimation and inferencePrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2006 (English)In: IFAC Proceedings Volumes (IFAC-PapersOnline): Volume 6, Issue PART 1, 2006, Vol. 6, no PART 1, 450-455 p.Conference paper (Refereed)
##### Abstract [en]

##### Place, publisher, year, edition, pages

2006. Vol. 6, no PART 1, 450-455 p.
##### Keyword [en]

Diagnosis, Fault isolation, Fault location, Inference, probability, Bayesian approaches, Bayesian inference, Conditional probability distributions, Illustrative examples, Network techniques, Structure estimation, Training data, Bayesian networks, Inference engines, Plant management, Probability distributions, Fault detection
##### National Category

Control Engineering
##### Identifiers

URN: urn:nbn:se:kth:diva-55399ScopusID: 2-s2.0-77953121255ISBN: 978-390266114-2OAI: oai:DiVA.org:kth-55399DiVA: diva2:471636
##### Conference

6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2006, Beijing, China, 29 August - 1 September 2006
#####

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##### Note

References: De Kleer, J., Mackworth, A.K., Reiter, R., Characterizing diagnoses and systems (1992) Artif. Intell., 56 (2-3), pp. 197-222; De Kleer, J., Williams, B.C., (1992) Diagnosis with Behavioral Modes, pp. 124-130; Gertler, J.J., (1998) Fault Detection and Diagnosis in Engineering Systems, , Marcel Decker. New York; Jaynes, B.T., (2001) Probability Theory - The Logic of Science, , Camebridge University Press. Cambridge; Jensen, X., (2001) Bayesian Networks, , Springer-Verlag. New York; Lerner, U., (2002) Hybrid Bayesian Networks for Reasoning about Complex Systems, , PhD thesis. Stanford University. Stanford University; Lerner, U., Parr, R., Koller, D., Biswas, G., Bayesian fault detection and diagnosis in dynamic systems (2000) AAAI/IAAI, pp. 531-537; Lu, T.-C., Wojtek Przytula, K., Methodology and tools for rapid development of large bayesian networks (2005) DX 2005, pp. 89-94; Schwall, M., Gerdes, C., A probabilistic approach to residual processing for vehicle fault detection (2002) Proceedings of the 2002 ACC, pp. 2552-2557; Wolf, D., (1995) Mutual Information As A Bayesian Measure of Independence. QC 20120104Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

This paper considers a Bayesian inference method for fault isolation. Given a set of residuals, and a set of possible faults, the task is to calculate the probability distribution of the faults. The method requires the conditional probability distribution of how the residuals respond given the possible faults. Especially important is to know the structure of this conditional probability distribution since it facilitates the use of efficient Baysian network techniques for the inference. The conditional probability distribution, and in particular its structure, is estimated from training data using a Bayesian approach. The approach is evaluated on a simple but illustrative example, where it is shown that the estimated structure and the distributions capture the dependencies that are important to make the correct isolation decisions.

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