MobiKanViT: Feature-Enhanced Lightweight CNN in Mobile Edge Computing for Real-Time Bearing Fault Diagnosis

As an important part of mechanical equipment, rolling bearing holds significant importance in the normal operation of machinery. However, the parameter and computation of fault diagnosis approaches based on deep learning technique are huge, and most methods are diagnosed in the cloud, which can lead to time delays and non-real-time. To overcome these issues, in edge computing scenarios, a real-time bearing fault diagnosis network MobiKanViT is proposed in this paper. The network is a lightweight and low-latency vision fault diagnosis network with enhanced discriminative feature learning capability. The Efficient Multi-scale Attention (EMA) module is introduced to enhance the ability of feature recognition. The ordinary convolution module is substituted with the Kolmogorov-Arnold Networks (KAN) convolution module to solve the problem of large parameter and computation. The MobiKanViT is compressed and quantized with depth parameter γ to make the model more lightweight and easy to be deployed on edge devices. Verification experiments were conducted on two sets of experimental equipment, and three mobile phones were selected as mobile edge computing platforms. The experimental results show that a depth parameter of γ = 0.5 and INT8 quantization yield the most effective results for MobiKanViT. When juxtaposed with current large model techniques, the suggested approach decreases memory consumption by an average of 94.6%, while simultaneously boosting inference speed by about 15.87 times. In comparison to existing lightweight models, this new method also improves diagnostic accuracy by an average of 2.6%.