Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes
Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes
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DOI：
10.1109/access.2017.2766235
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发表时间：
2017-10
期刊：
影响因子：
3.9
通讯作者：
Jie Dong;Mengyuan Wang;Xiong Zhang;Liang Ma;Kai-xiang Peng
中科院分区：
文献类型：
--
作者：
Jie Dong;Mengyuan Wang;Xiong Zhang;Liang Ma;Kai-xiang Peng
本文提出了一种综合故障诊断方法，以处理故障定位和传播路径识别。首先根据系统的先验知识构造系统的因果图。在此基础上，提出了一种基于偏相关系数的相关指数（CI），用于定量分析因果图中变量之间的相关性。为了实现准确的故障检测结果，建议CI监测概率主成分分析。在故障检测的基础上，引入加权平均值的概念，利用重构贡献图和因果图来识别故障传播路径。最后，新提出的方案将实践与真实的工业HSMP数据，其中的各个步骤以及完整的框架进行了广泛的测试。
In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient is proposed to analyze the correlation of variables in causality graph quantitatively. To achieve accurate fault detection results, the proposed CI is monitored by probability principal component analysis. Moreover, the concept of weighted average value is introduced to identify fault propagation path based on reconstruction-based contribution and causality graph after detecting a fault. Finally, the new proposed scheme would be practiced with real industrial HSMP data, where the individual steps as well as the complete framework were extensively tested.