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Fault detection of univariate non-Gaussian data with Bayesian network

Abstract : The purpose of this article is to present a new method for fault detection with Bayesian network. The interest of this method is to propose a new structure of Bayesian network allowing to detect a fault in the case of a non-Gaussian signal. For that, a structure based on Gaussian mixture model is proposed. This particular structure allows to take into account the non-normality of the data. The effectiveness of the method is illustrated on a simple process corrupted by different faults.
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Submitted on : Monday, September 13, 2010 - 2:04:07 PM
Last modification on : Wednesday, October 20, 2021 - 3:19:19 AM
Long-term archiving on: : Tuesday, December 14, 2010 - 2:48:30 AM


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  • HAL Id : inria-00517031, version 1



Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Fault detection of univariate non-Gaussian data with Bayesian network. IEEE International Conference on Industrial Technology (ICIT'10), 2010, Vina del Mar, Chile. ⟨inria-00517031⟩



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