A Graph Convolutional Shrinkage Network-based Fault Diagnosis Method for Industrial Process

Yuan Xu, Xun Zou, Wei Ke, Qun Xiong Zhu, Yan Lin He, Ming Qing Zhang, Yang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

With the increasing demand of safety monitoring for industrial process, fault diagnosis is facing greater challenges. In the paper, a graph convolutional shrinkage network is proposed for fault diagnosis. First, considering the correlation between process variables, Pearson correlation coefficient is used to convert the time series to graph structure with nodes and edges. Second, a graph convolutional shrinkage layer with anti-noise ability is designed, in which Chebyshev kernel is used as convolution kernel to extract the feature information of graph structure. In addition, a soft thresholding shrinkage layer is added to help adaptively determine the filtering thresholds of graphic feature information. Third, the graph classification for fault diagnosis is performed using the readout layer, fully connected layer and softmax layer. Finally, the proposed method's effectiveness and robustness are evaluated using TE process data. The experimental results demonstrate that the proposed fault diagnostic approach is reliable and capable of correctly identifying the fault states in noisy situations.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1069-1074
Number of pages6
ISBN (Electronic)9798350321050
DOIs
Publication statusPublished - 2023
Event12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, China
Duration: 12 May 202314 May 2023

Publication series

NameProceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

Conference

Conference12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Country/TerritoryChina
CityXiangtan
Period12/05/2314/05/23

Keywords

  • Fault Diagnosis
  • Graph Convolutional Network
  • Graph Convolutional Shrinkage Layer
  • TE Process

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