Unsupervised transfer learning for intelligent health status identification of bearing in adaptive input length selection

Abstract

Input length (IL) is an important element in transfer learning (TL) network for intelligent health status identification of bearing (IHSIB). However, fixed IL are used in most studies. In this paper, a TL network via adaptive IL selection module for IHSIB (AILTLN) is proposed, which includes adaptive IL module, feature extractor module, health status identification module, and domain discriminator module. Firstly, an adaptive IL selection module based on envelope spectrum analysis is proposed. The module varies with bearing structure, motor speed, and sampling frequency. Secondly, group convolution, transposed convolution, and instant normalization are constructed in feature extractor. Thirdly, softmax cross-entropy loss function and maximum mean discrepancy are used for health status identification and domain alignment. The TL results of open bearing dataset and high-speed train bearing experiment show that AILTLN is better than the other existing methods in the TL of IHSIB. The ablation study shows the reuse of low-dimensional features and the adaptive IL help to improve to the accuracy of the proposed method.