A One-Dimensional Vision Transformer with Multiscale Convolution Fusion for Bearing Fault Diagnosis
Chaoyang Weng, Baochun Lu, Jiachen Yao
Abstract
Aiming at the problem that traditional convolutional neural networks (CNN) based fault diagnosis methods cannot capture the temporal information of rolling bearings, a one-dimensional Vision Transformer with Multiscale Convolution Fusion (MCF-1DViT) is proposed in this paper. To automatically and effectively enrich multiscale features from the collected vibration signals, the multiscale convolution fusion (MCF) layer is designed to capture the fault features in multiple time scales. Then, the improved Vision Transformer architecture is introduced to learn long-term time-related information with Transformer, which can significantly improve the diagnosis accuracy and antinoise ability. Finally, experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results show that the proposed method can obtain superior diagnosis performance compared with the existing methods.