基于Liang-Kleeman信息流和图形拉索的故障根本原因分析
Xiangdong Liu1, Jie Liu1, Xiaohua Yang1
1School of Computer Science, University of South China, Hengyang 421001, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种使用图形拉索 (Glasso) 和梁克利曼信息流 (LKIF) 的新根源原因分析方法. 通过克服转移方法的局限性,LKIF-Glasso方法可以提高故障诊断的准确性.
科学领域:
- 工业系统工程 工业系统工程
- 数据科学数据科学数据科学
- 错误诊断 错误诊断 在
背景情况:
- 根源原因分析对于系统故障诊断至关重要,识别故障位置和原因.
- 传统的因果分析方法,如转移,可以产生偏差的结果,导致不准确的故障识别.
- 现有的方法难以应对工业数据的高维度和冗余性.
研究的目的:
- 为工业系统开发更准确,更可靠的根本原因分析方法.
- 解决转移在故障诊断的因果推断中的局限性.
- 为了利用信息流来进行卓越的故障检测和传播分析.
主要方法:
- 一种新的根源原因分析方法,将图形拉索 (Glasso) 结合起来,用于缩小维度,并将梁-克利曼信息流 (LKIF) 结合起来,用于因果推理.
- 通过减少冗余性来处理大规模的高维度工业数据,Glasso的应用.
- 使用LKIF计算信息流的强度,并推断故障追踪变量之间的因果关系.
主要成果:
- LKIF-Glasso方法有效地识别出故障的根本原因,并在田纳西东曼模拟平台上可视化故障传播.
- 比较实验表明,信息流 (LKIF) 在根源原因分析准确性方面表现优于转移.
- 剥离器步骤故障的详细分析解释了信息流在传输的优越性.
结论:
- 拟议的LKIF-Glasso方法在工业故障诊断的准确性和可靠性方面取得了重大进展.
- 与转移相比,信息流是复杂工业系统中因果推理的更强大的方法.
- 该方法为了解和减轻工业过程中故障提供了强大的工具.
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