Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations


  • Zhe Wang
  • Xingqiu Li
  • Haidong Shao
  • Te Han

Related Research Unit(s)


Original languageEnglish
Article number110891
Journal / PublicationKnowledge-Based Systems
Online published7 Aug 2023
Publication statusPublished - 25 Oct 2023


Intelligent fault diagnosis has attracted intensive efforts in machine predictive maintenance. However, the structural information from multi-sensor signals has not been fully investigated. In this study, a novel temporal–spatial graph neural network with an attention-aware module (A-TSGNN) is proposed to accomplish multi-source information fusion. First, the graph structure naturally organizes the diverse sensors. The graph convolution model realizes the feature representation in the spatial dimension. Then, time-dependent learning is applied in the temporal dimension, and a temporal–spatial learning framework is built. An additional attention module is designed to learn the flexible weights and model the importance of individual sensors and their correlations. Experiments on a wind turbine dataset achieves an accuracy of 0.9669 and an F1-score of 0.9649. For the gearbox dataset, the values are 0.9927 and 0.9920, respectively. The overall macro-average area under the curve metrics reach a perfect score of 1.00 for both datasets, indicating exceptional performance. The adaptive attention mechanism is also discussed to verify the superiority of the A-TSGNN. Furthermore, comparisons with the single-sensor scheme and other fusion models demonstrate the stable performance of the proposed method. The A-TSGNN provides a potential model for comprehensively utilizing multi-sensor data, showing a promising prospect. © 2023 Elsevier B.V.

Research Area(s)

  • Attention mechanism, Deep learning, Fault diagnosis, Graph neural network, Information fusion