Gearbox fault diagnosis based on transfer learning and weighted multi-channel fusion

Here,aiming at problems of large fluctuation of fault recognition accuracy of single sensor of gearbox, low data utilization, low reliability and insufficient generalization ability of fault diagnosis model under multi-working condition, a gearbox fault diagnosis method based on weighted fusion of multi-channel data and deep transfer model was proposed. Firstly, in order to fully excavate information of multi-channel data of gearbox, a multi-channel fusion method based on information entropy weighting was proposed. The information entropy method was used to calculate fusion weights of various channels' data,and sampling data of various channels were weighted and fused. Secondly,fusion data of source domain were used to pre-train deep transfer model,the model,s parameters obtained with pre-training were taken as initialization parameters of target domain model, parameters of feature extractor of the target domain model were frozen, and fusion data of the target domain were used to fine-tune parameters of the target domain model' s classifier, realize transfer of the deep transfer model from source domain to target domain,and adapt to new target sample recognition task. Finally,gearbox multi-working condition transfer diagnosis test results showed that the proposed method can effectively be applied in gearbox fault diagnosis; compared with the traditional transfer learning methods balanced distribution adaptation (BDA),transfer component analysis(TCA),joint distribution adaptation(JDA),joint geometric and statistical alignment (JGSA) and geodesic flow kernel ( GFK) and the deep transfer learning methods adaptive batch normalization (AdaBN), multi-kernel maximum mean discrepancy ( MK-MMD) and deep convolutional transfer learning network ( DCTLN ) which are 8 currently commonly used methods, the proposed method has higher average transfer diagnosis accuracy and good generalization performance under variable working conditions. © 2023 Chinese Vibration Engineering Society. All rights reserved.