Fault diagnosis method based on EWT and improved ConvNeXt networks

Due to the interference of strong noise, feature extraction faces the challenge of limited information, which is not conducive to motor equipment fault diagnosis. This paper proposes a fault diagnosis method based on the empirical wavelet transform (EWT) and an improved ConvNeXt network. The modal components were extracted from the signals of different sensors using empirical wavelet transform, noise was removed, and then the signals were reconstructed. Secondly, the short-time Fourier transform (STFT) was used to convert the one-dimensional signal after noise reduction and reconstruction into a two-dimensional time-frequency spectrum image that enhanced signal features. Single-channel images generated by a single sensor were fused to form multi-channel images, thereby boosting the feature extraction capability of the ConvNeXt network. Additionally, the Ghost convolution module and the efficient local attention mechanism (ELA) were introduced into the ConvNeXt-T (ConvNeXt-Tiny) network, further enhancing the network’s performance. Experimental validation was conducted on application examples of various fault diagnostic devices, and comparisons were made with existing mainstream deep learning methods such as SE-InceptionV3, CBAM-ResNet, and CNN-LSTM etc. Experimental results confirmed under different noise environments and variable operating conditions, the proposed method achieved better diagnostic accuracy and enhanced generalization performance.