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He, D., Wu, J., Jin, Z., Huang, C., Wei, Z. and Yi, C. (2025) AGFCN: A Bearing Fault Diagnosis Method for High-Speed Train Bogie under Complex Working Conditions. Reliability Engineering & System Safety, 258, Article 110907.
https://doi.org/10.1016/j.ress.2025.110907

has been cited by the following article:

• TITLE: Adaptive Dynamic K-NN Spatio-Temporal Graph Convolutional Network for Bearing Fault Diagnosis

  AUTHORS: Xiuwei Hu, Ting Fang, Wenhui Chang, Cunrui Pang, Tonghui Wang

  KEYWORDS: Rolling Bearing, Fault Diagnosis, Graph Convolutional Network, Adaptive Dynamic KNN, Spatio-Temporal Feature Fusion

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.6, June 25, 2026

  ABSTRACT: Rolling bearing fault diagnosis is an important means to ensure the reliable operation of mechanical equipment. Aiming at the problems of poor working condition adaptability, over-smoothing in deep networks, and insufficient mining of fault temporal evolution features existing when the traditional K-nearest neighbor graph convolutional network is applied to bearing fault diagnosis, this paper proposes an adaptive dynamic K-NN spatio-temporal graph convolutional network (AdaDKNN-STGCN). Experimental validations are implemented on the CWRU bearing dataset. The proposed model attains a test accuracy of 99.78% in ten-class classification. It exhibits excellent generalization capability and anti-interference performance under noisy and variable load environments, which provides a reliable solution for intelligent fault diagnosis of rotating machinery bearings.