Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (5): 62-70.DOI: 10.12068/j.issn.1005-3026.2025.20230300

• Mechanical Engineering • Previous Articles     Next Articles

A Fault Diagnosis Method of Rolling Bearings Based on GRM-IConvNeXt Model

Heng-fa LUO, Tian-zhuang YU, Shi-hua ZHOU()   

  1. School of Mechanical Engineering & Automation,Northeastern University,Shenyang 110819,China.
  • Received:2023-11-01 Online:2025-05-15 Published:2025-08-07
  • Contact: Shi-hua ZHOU

Abstract:

Aiming at the problems of complex bearing operating conditions, weak generalization ability and low accuracy of model recognition of traditional deep learning fault diagnosis methods, a rolling bearing fault diagnosis method based on the GRM-IConvNeXt model is established. Firstly, a coding method of global relationship matrix (GRM) is proposed, which can transform one-dimensional vibration signals into two-dimensional images by taking the advantage of preserving the original signal features. Then, an improved ConvNeXt (IConvNeXt) model for small sample classification of bearing fault diagnosis is constructed, and a convolution kernel with a size of 5×5, multiple BN layers and Hardswish activation function are selected to enhance the feature extraction performance. At the same time, weights are adaptively generated according to the GRM image features through the CBAM(convolutional block attention module) mechanism. The experimental results show that the GRM-IConvNeXt model has good feature extraction ability and generalization under off-design conditions and small samples.

Key words: rolling bearing, global relationship matrix(GRM), IConvNeXt model, CBAM(convolutional block attention module), fault diagnosis

CLC Number: