Fault detection and pathway analysis using a dynamic Bayesian network
Fault detection and pathway analysis using a dynamic Bayesian network
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DOI：
10.1016/j.ces.2018.10.024
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发表时间：
2019-02-23
影响因子：
4.7
通讯作者：
Imtiaz, Syed
中科院分区：
文献类型：
--
作者：
Amin, Md Tanjin;Khan, Faisal;Imtiaz, Syed
提出了一种基于动态贝叶斯网络（DBN）的故障检测、根本原因诊断和故障传播路径识别方法。所提出的方法从监测的过程数据中生成证据，并使用这些信息来更新捕获过程知识的DBN。提出了一种新的基于动态贝叶斯异常指数（DBAI）的控制图检测方法。在检测到故障之后，使用DBN的平滑推理来诊断根本原因，并且从过程变量之间的因果关系来识别故障传播路径。所提出的方法应用于二元蒸馏塔和连续搅拌罐加热器（CSTH）。结果表明，该方法能够准确地检测出故障并诊断出故障的根本原因。结果进行了比较的休哈特控制图，主成分分析（PCA）和静态BN的性能。比较研究证实，所提出的方法是一个更有效的故障检测和诊断（FDD）的工具。(C)2018爱思唯尔有限公司版权所有
A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault propagation pathway identification scheme is proposed. The proposed methodology generates evidence from monitored process data and uses the information to update the DBN that captures the process knowledge. A new dynamic Bayesian anomaly index (DBAI) based control chart is proposed for detection purpose. Following the detection of the fault(s), root cause(s) is diagnosed using the smoothing inference of a DBN, and fault propagation pathway is identified from the cause-effect relationships among the process variables. The proposed methodology is applied to a binary distillation column and a continuous stirred tank heater (CSTH). The result shows that it can detect the fault and diagnose the root cause of the fault precisely. The result has been compared to the performance of the Shewhart control chart, principal component analysis (PCA) and static BN. The comparative study confirms that the proposed methodology is a more efficient fault detection and diagnosis (FDD) tool. (C) 2018 Elsevier Ltd. All rights reserved.