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Fault detection and isolation of faults in a multivariate process with Bayesian network

机译:贝叶斯网络的多元过程中的故障检测和故障隔离

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The main objective of this paper is to present a new method of detection and isolation 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 T~2 statistic. The second one is a previous work on the detection of fault with Bayesian networks [2], notably on the modeling of multivariate control charts in a Bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing to decide if a fault has appeared in the process. This structure permits the isolation of the variables implicated in the fault. A particular interest of the method is the fact that the detection and the isolation can be made with a unique tool: a Bayesian network.
机译:本文的主要目的是提出一种利用贝叶斯网络进行检测和隔离的新方法。为此,将两个原始作品组合在一起。第一个是李等人的工作。 [1]提出了T〜2统计量的因果分解。第二个是关于使用贝叶斯网络[2]进行故障检测的先前工作,特别是关于贝叶斯网络中的多元控制图建模的工作。因此,在多元过程的背景下,我们提出了一种原始的网络结构,可以确定过程中是否出现了故障。这种结构允许隔离与故障有关的变量。该方法特别令人感兴趣的是,可以使用一种独特的工具进行检测和隔离:贝叶斯网络。

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