TY - JOUR

T1 - Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions

AU - Lei, Zihao

AU - Tian, Feiyu

AU - Su, Yu

AU - Wen, Guangrui

AU - Feng, Ke

AU - Chen, Xuefeng

AU - Beer, Michael

AU - Yang, Chunsheng

PY - 2024/11/26

Y1 - 2024/11/26

N2 - 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.

AB - 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.

KW - Graph neural networks

KW - Intelligent fault diagnosis

KW - Transfer learning

KW - Unsupervised domain adaptation

KW - Variable operating conditions

UR - http://www.scopus.com/inward/record.url?scp=85210534873&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2024.110684

DO - 10.1016/j.ress.2024.110684

M3 - Article

VL - 255

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

M1 - 110684

ER -