追踪
根本原因分析
根本原因
断层(地质)
计算机科学
过程(计算)
图形
数据挖掘
算法
故障检测与隔离
工程类
人工智能
可靠性工程
理论计算机科学
操作系统
地质学
地震学
执行机构
作者
Yuying He,Le Yao,Zhiqiang Ge,Zhihuan Song
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/tim.2023.3273686
摘要
Fault tracing technology, including root-cause diagnosis and propagation analysis, has become a growing hot spot in the field of industrial process monitoring. However, it is currently limited by the use of restricted alarm sequence data and the analysis without fault propagation analysis. To solve these problems, this article proposes a novel fault tracing method, namely causal topology-based variable-wise generative model (CTVGM). The CTVGM is first established according to the topological order of the variable causal graph. It contains a series of causal functions that are trained with normal data. Then, fault samples can be restored by the CTVGM to build up a diagnosis index called the recovery ratio (RR), which is used to determine the root causes. Meanwhile, the fault propagation paths are inferred by the recovery routes. In addition, a hierarchical CTVGM-based fault tracing strategy is designed to reduce the computation burden and enhance the modeling efficiency for large-scale complicated processes. The effectiveness of the proposed fault tracing method is verified on a numerical example and the Tennessee Eastman process case. Compared with existing methods, the results show that the proposed method not only achieves more accurate root-cause diagnosis performance but also obtains fault tracing results that are highly consistent with the process mechanisms.
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