An intelligent method of roller bearing fault diagnosis and fault characteristic frequency visualization based on improved MobileNet V3

In recent years, the lightweight neural network models have been gradually applied to fault diagnosis. In order to solve the problems about computation bottleneck of the pointwise convolution module which is widely used in lightweight networks, and explore how to effectively evaluate the quality of extracted features as well as deeply merge traditional fault diagnosis methods into deep learning, this paper proposed a diagnosis model named butterfly-transform (BFT)-MobileNet V3. BFT-MobileNet V3 was based on MobileNet V3, and consisted of BFT module and a novel algorithm called Deep-SHAP. This model not only had the advantages of low time complexity and high accuracy compared with the original network, but also had a novel feature that was able to automatically figure out the fault characteristic frequency and visualize the quality of extracted features. The experimental results showed that the time complexity of the BFT-MobileNet V3 model proposed in this paper decreases from to while keeping a high accuracy rate. With the same time complexity, BFT-MobileNet V3 also had a higher accuracy rate than other networks. Meanwhile, with the Deep SHAP algorithm, the proposed model can accurately calculate the fault feature frequency of the roller bearings as well as intuitively visualize the quality of extracted features.