A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis

Abstract

In recent years, great progress has been made in intelligent bearing fault diagnosis based on transfer learning (TL). However, the huge number of parameters is ignored when using large convolutional neural network (CNN), and the input length of different bearings are almost not take into account. The high-energy hardware economic cost and time consumption caused by slow operation of large CNN have brought great difficulties to the engineering practice. Therefore, inspired by envelope demodulation and lightweight network signal processing methods, a novel lightweight TL network is proposed, which can adaptively select the input length (IL) and accurately identify the bearing health states under different work conditions. Firstly, an innovative adaptive IL selection strategy considering bearing differences is proposed to replace manually fixed IL. Secondly, a TL network containing group convolution and instance normalization is constructed to make the network lightweight and operate faster. Thirdly, maximum mean discrepancy is introduced to align the feature distribution between source domain and target domain. Lastly, 81 tasks are carried out on the across-domain datasets to validate the practicability of the proposed method. The results between accuracy and lightweight demonstrate that the proposed method is superior to other four state-of-the-art TL CNN, including three TL CNN and a lightweight model, under identical conditions.