Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

Abstract

In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

“…Attention mechanisms have effectively solved this problem. For example, Song et al 21 constructed a new deep transfer learning network model for planetary gearbox fault diagnosis by integrating one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods; Zhao et al 22 utilized attention mechanisms to share information between channels, and their constructed rotor system fault diagnosis model demonstrated excellent load adaptability and noise resistance; Cheng et al 23 designed a lightweight and efficient channel attention mechanism (LECA) combined with one-dimensional convolution, achieving good gearbox fault diagnosis results; Zhang et al 24 applied attention mechanisms in unsupervised fault diagnosis, developing a semi-supervised attention mechanism to solve the problems of insufficient fault diagnosis data and false alarms in online fault diagnosis.…”