Bearing Fault Diagnosis of WDCNN-LSTM in Siamese Network

Abstract

In this paper, a Siamese network-based WDCNN + LSTM model was used to diagnose bearing faults using a few shot learning algorithm. Recently, deep learning-based fault diagnosis methods have achieved good results in equipment fault diagnosis. However, there are still limitations in the existing research. The biggest problem is that a large number of training samples are required to train a deep learning model. However, manufacturing sites are complex, and it is not easy to intentionally create equipment defects. Furthermore, it is impossible to obtain enough training samples for all failure types under all working conditions. Therefore, in this study, we propose a few-shot learning algorithm that can effectively learn with limited data. A Few shot learning algorithm and Siamese network based WDCNN + LSTM model bearing fault diagnosis, which can effectively learn with limited data, is proposed in this study.