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Bayesian network for the characterization of faults in a multivariate process

Abstract : The main objective of this paper is to present a new method of detection and characterization with a bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T² statistic. The second one is our previous work on the detection of fault with bayesian networks [2], [3], notably on the modelization of multivariate control charts in a bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing deciding if a fault is appeared in the process. More, this structure permits the identification of the variables that are responsible (root causes) of the fault. A particular interest of the method is the fact that the detection and the identification can be made with a unique tool: a bayesian network.
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Submitted on : Monday, September 13, 2010 - 1:56:40 PM
Last modification on : Wednesday, October 20, 2021 - 3:19:19 AM
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  • HAL Id : inria-00517026, version 1



Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Bayesian network for the characterization of faults in a multivariate process. 11th International Conference on Quality and Dependability (CCF'08), 2008, Sinaia, Romania. ⟨inria-00517026⟩



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