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Two-dimensional explainability method for fault diagnosis of fluid machine

摘要:The safe operation of the fluid machine is greatly affected by fault states. With the development of data collection technology in process industrial systems, data-based methods are widely applied in fault diagnosis. The observed data of the fluid machine belongs to typical multivariable time series, so the Euclidean features related to the observed timestamps and the non-Euclidean features related to the observed variables need to be extracted simultaneously by the fault diagnosis method. However, the existing diagnostic studies do not involve the explainability analysis of the diagnostic process, which makes it hard to evaluate the contribution of these features to the accurate diagnosis. An explainable diagnosis model based on temporal and graph convolutional neural network is proposed. The class activation map algorithm is improved to perform explainability analysis of the diagnosis process related to the observed variables and timestamps. Using the simulation data of the fluid machine, features related to observed variables and timestamps of six operating states are fully extracted. Through data experiments, this method can be utilized to achieve high-precision fault diagnosis, and can intuitively display the contribution of each observed variable and its each timestamp to network decision-making. This helps to trace system faults and has significant benefits for process safety assurance. ? 2023

ISSN号:0957-5820

卷、期、页:卷178:1148-1160

发表日期:2023-10-01

影响因子:0.000000

期刊分区(SCI为中科院分区):二区

收录情况:SCI(科学引文索引印刷版),EI(工程索引),SCIE(科学引文索引网络版)

发表期刊名称:Process Safety and Environmental Protection

参与作者:李雨

通讯作者:刘珈铨,贺思宸,张鑫儒,于巧燕,杨凯

第一作者:侯磊

论文类型:期刊论文

论文概要:刘珈铨,侯磊,贺思宸,张鑫儒,于巧燕,杨凯,李雨,Two-dimensional explainability method for fault diagnosis of fluid machine,Process Safety and Environmental Protection,2023,卷178:1148-1160

论文题目:Two-dimensional explainability method for fault diagnosis of fluid machine

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