A Novel Fault Diagnosis Method of Rolling Bearings Combining Convolutional Neural Network and Transformer

Abstract

Efficient and accurate fault diagnosis plays an essential role in the safe operation of machinery. In respect of fault diagnosis, various data-driven methods based on deep learning have attracted widespread attention for research in recent years. Considering the limitations of feature representation in convolutional structures for fault diagnosis, and the demanding requirements on the quality of data for Transformer structures, an intelligent method of fault diagnosis is proposed in the present study for bearings, namely Efficient Convolutional Transformer (ECTN). Firstly, the time-frequency representation is achieved by means of short-time Fourier transform for the original signal. Secondly, the low-level local features are extracted using an efficient convolution module. Then, the global information is extracted through transformer. Finally, the results of fault diagnosis are obtained by the classifier. Moreover, experiments are conducted on two different bearing datasets to obtain the experimental results showing that the proposed method is effective in combining the advantages of CNN and transformer. In comparison with other single-structure methods of fault diagnosis, the method proposed in this study produces a better diagnostic performance in the context of limited data volume, strong noise, and variable operating conditions.