A novel intelligent fault diagnosis method for commercial vehicle pneumatic braking system

The application of convolutional neural network (CNN) has greatly broaden the application range of intelligent fault diagnosis, which is beneficial to the stability of industrial production. However, traditional CNN based fault diagnosis methods cannot capture the global features. To address the problem, this paper proposed a method called PSO-Conformer to extract features integrating global and local information. First, convolutional layers are uesd for local feature extraction, and then using Encode module of Transformer to extract global features. The diagnostic performance of the model can be significantly improved and the model hyperparameters are optimized using the PSO algorithm. The proposed method is applied to the fault diagnosis of pneumatic brake systems of commercial vehicles and compared with several methods based on deep learning. And this paper use the t-Distributed Stochastic Neighbor Embdedding (TSNE) to visualize the output features of models in two dimensional plane and four evaluation indexes based on confusion matrix are used to evaluate the model. The results show that the diagnostic performance of the proposed method is better than the existing method.