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

Author

Listed:
  • P Weber
  • D Theilliol
  • C Aubrun

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.

Suggested Citation

  • P Weber & D Theilliol & C Aubrun, 2008. "Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks," Journal of Risk and Reliability, , vol. 222(2), pages 161-172, June.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:2:p:161-172
    DOI: 10.1243/1748006XJRR96
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    Cited by:

    1. Codetta-Raiteri, Daniele & Portinale, Luigi, 2017. "Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 639-651.
    2. Medina-Oliva, G. & Weber, P. & Iung, B., 2013. "PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 38-56.
    3. Hu, Bin & Seiler, Peter, 2015. "Pivotal decomposition for reliability analysis of fault tolerant control systems on unmanned aerial vehicles," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 130-141.

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