Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks

P Weber, D Theilliol and C Aubrun

Journal of Risk and Reliability, 2008, vol. 222, issue 2, 161-172

Abstract: The decision making in fault diagnosis methods generally relies on the analysis of fault signature vectors. The current paper presents a new approach of decision making for the signature vectors for various identical or similar faults. The main contribution of the paper consists in the fusion between the reliability and the evaluation of the residuals in order to increase the fault isolation efficiency. The decision making, formalized as a Bayesian network, is established with a priori knowledge on fault signatures, false alarm and missing detection probability, online component state estimation computed by a Bayesian fusion of the component reliability, and measurements. The effectiveness and performances of the method are illustrated on a heating water process corrupted by various faults.

Keywords: model-based fault diagnosis; Bayesian networks; reliability; Markov chains; decision making (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:222:y:2008:i:2:p:161-172

DOI: 10.1243/1748006XJRR96

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