A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3967
• Main Authors: Zhou, Zijun, Ai, Qingsong, Lou, Ping, Hu, Jianmin, Yan, Junwei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.06.2024; MDPI
• Subjects: Accuracy; Bearings; convolutional neural network; Data processing; Data transmission; Deep learning; Edge computing; Entropy; Fault diagnosis; Fourier transforms; gramian angular field; Industrial Internet of Things; Internet of Things; Machine learning; Neural networks; Sensors; Signal processing; Support vector machines; Time series; vision transformer; Wavelet transforms; Workloads; gramian angular field; fault diagnosis; sensors; vision transformer; convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24123967

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices.