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Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Zihao Lei
  • Feiyu Tian
  • Yu Su
  • Guangrui Wen
  • Michael Beer

Research Organisations

External Research Organisations

  • Xi'an Jiaotong-Liverpool University
  • University of Liverpool
  • Guangzhou University

Details

Original languageEnglish
Article number110684
JournalReliability Engineering and System Safety
Volume255
Early online date26 Nov 2024
Publication statusPublished - Mar 2025

Abstract

In recent years, unsupervised domain adaptation (UDA) has gained widespread application in addressing intelligent fault diagnosis under variable operating conditions. However, how to effectively model data structure information and integrate it into UDA has hindered the application of intelligent fault diagnosis in the industry. To solve this issue, a multi-scale and multi-structure information-embedded unsupervised graph transfer network for fault diagnosis is proposed. Specifically, a novel node feature extractor is first designed for feature embedding. To better fuse multi-scale information and obtain more effective features, a multi-scale convolutional layer and a hybrid attention module are utilized. Secondly, an adaptive similarity graph-constructing method based on the inner-product kernel is adopted to convert the node features into graph data. Next, the graph neural network (GNN) is introduced to obtain graph-structured information. Finally, a joint domain adaptation module is designed to cope with the covariance drift problem in cross-domain fault diagnosis. The proposed method exhibited state-of-the-art performance in the experiments of three case studies.

Keywords

    Graph neural networks, Intelligent fault diagnosis, Transfer learning, Unsupervised domain adaptation, Variable operating conditions

ASJC Scopus subject areas

Cite this

Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions. / Lei, Zihao; Tian, Feiyu; Su, Yu et al.
In: Reliability Engineering and System Safety, Vol. 255, 110684, 03.2025.

Research output: Contribution to journalArticleResearchpeer review

Lei Z, Tian F, Su Y, Wen G, Feng K, Chen X et al. Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions. Reliability Engineering and System Safety. 2025 Mar;255:110684. Epub 2024 Nov 26. doi: 10.1016/j.ress.2024.110684
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abstract = "In recent years, unsupervised domain adaptation (UDA) has gained widespread application in addressing intelligent fault diagnosis under variable operating conditions. However, how to effectively model data structure information and integrate it into UDA has hindered the application of intelligent fault diagnosis in the industry. To solve this issue, a multi-scale and multi-structure information-embedded unsupervised graph transfer network for fault diagnosis is proposed. Specifically, a novel node feature extractor is first designed for feature embedding. To better fuse multi-scale information and obtain more effective features, a multi-scale convolutional layer and a hybrid attention module are utilized. Secondly, an adaptive similarity graph-constructing method based on the inner-product kernel is adopted to convert the node features into graph data. Next, the graph neural network (GNN) is introduced to obtain graph-structured information. Finally, a joint domain adaptation module is designed to cope with the covariance drift problem in cross-domain fault diagnosis. The proposed method exhibited state-of-the-art performance in the experiments of three case studies.",
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AU - Lei, Zihao

AU - Tian, Feiyu

AU - Su, Yu

AU - Wen, Guangrui

AU - Feng, Ke

AU - Chen, Xuefeng

AU - Beer, Michael

AU - Yang, Chunsheng

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KW - Graph neural networks

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