Research on bearing fault diagnosis under mixed working condition based on Vision Transformer hybrid model

In order to solve the problem that the bearing vibration signal identification under a single working condition is difficult to be applied to the fault identification and diagnosis of rolling bearing in the actual production, this paper proposes a classification method of bearing fault mode based on mixed working condition. In the actual industrial production, bearings are often in a variety of complex working states. The bearing fault diagnosis research under mixed working conditions can more accurately restore the working conditions in real industrial scenarios, and improve the practicability and reliability of fault diagnosis. First of all, the timing is realized through the gram angle field conversion, and the time dependence and correlation of the original time series of bearing vibration are retained, which realizes the rich representation of bearing vibration signal and efficient fault type identification. Secondly, in order to improve the fault identification rate, the advantages of EfficientNet and Vision Transformer (ViT) models in representation learning and feature extraction are given full play through the cross-domain fusion of EfficientNet and ViT models.Comparing the experiment of various classification methods show that the method proposed in this paper can effectively extract the fault characteristics of vibration signal, identify the correct rate is significantly higher than the typical classification method, in the migration experiment of two data sets, the classification method of mixed working condition has achieved good migration effect, indicating that the fault mode recognition method of mixed working condition is easier to be applied in the actual production.