文献类型：期刊文献

英文题名：A bearing fault diagnosis method for hydrodynamic transmissions integrating few-shot learning and transfer learning

作者：Sun, Dong Yang, Xudong Yang, Hai

第一作者：孙栋;Sun, Dong

通信作者：Yang, XD[1]

机构：[1]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China.

年份：2025

卷号：15

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-105006714653);WOS:【SCI-EXPANDED(收录号:WOS:001499638000020)】；

基金：This work is supported by National Key R&D Program of China (No. 2019YFB1312704), Enterprise project: High power hydraulic transmission and key component supporting construction project (No. 0611-1600130456 C).

语种：英文

外文关键词：Hydrodynamic transmission; Bearing fault diagnosis; Few-shot learning; Transfer learning; Attention mechanism

摘要：To address the insufficient generalization capability of bearing fault diagnosis models caused by scarce vibration data from high-power hydrodynamic transmission testbeds, this study proposes a diagnostic method integrating deep few-shot learning with transfer learning. First, a Siamese Wide Convolutional Neural Network (Siamese-WDCNN) is constructed based on public bearing datasets to extract essential features of vibration signals through few-shot contrastive learning. Second, we introduce a transfer learning strategy to address cross-condition generalization challenges. This approach adapts pre-trained model parameters from the CWRU dataset to real industrial hydrodynamic transmission data. We then fine-tune the model using limited target-domain samples to optimize performance. Experiments evaluating the generalization capability under variable operating conditions compare diagnostic performance across SVM, WDCNN, WDCNN + TL, FSL + TL, and FSL + TL + AM methods. Results demonstrate that FSL + TL achieves an accuracy of 85.30% under mixed operating conditions. Further optimization by incorporating an attention mechanism (FSL + TL + AM) elevates accuracy to 88.75%, effectively enhancing the generalization capability of the bearing fault diagnosis model. This validates the engineering practicality of the proposed method and explores a viable pathway for industrial equipment health monitoring.