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

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 t...