A novel dynamic bayesian network‐based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis
A novel dynamic bayesian network‐based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis
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DOI：
10.1002/aic.14013
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发表时间：
2013-07
期刊：
影响因子：
3.7
通讯作者：
Jie Yu;Mudassir M. Rashid
中科院分区：
文献类型：
--
作者：
Jie Yu;Mudassir M. Rashid
提出了一种新的网络化过程监测、故障传播识别和根本原因诊断方法。首先，根据工艺先验知识和工艺分析确定工艺网络结构;网络模型参数，包括不同节点的条件概率密度函数，然后估计从过程运行数据来表征监控变量之间的因果关系。在此基础上，提出了基于贝叶斯推理的异常似然指数，用于化工过程异常事件的检测。在检测到过程故障后，新的动态贝叶斯概率和贡献指数被进一步开发从监测变量的转移概率，以识别具有显着扰动的主要故障影响变量。利用动态贝叶斯贡献指数，设计了从下游到上游的故障传播路径的统计推理规则。以这种方式，所识别的传播路径中的结束节点可以被捕获为过程故障的根本原因变量。同时，所识别的故障传播序列提供了一个深入的理解，在整个过程中的故障的相互作用的影响。所提出的方法被证明使用说明性的连续搅拌釜式反应器系统和田纳西伊士曼化工过程与故障传播识别结果相比，对那些基于转移熵的监测方法。结果表明，该方法能够准确地检测出异常事件，识别故障传播路径，并诊断出故障的根本原因变量。© 2013美国化学工程师学会AIChE J，59：2348-2365，2013
A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference-based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in-depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy-based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013