Abstract：For mechanical fault diagnosis under different working conditions, the transfer learning method has obvious effect compared with deep learning method, but the single source domain transfer fault diagnosis still has negative transfer and poor model generalization ability. Therefore, this paper proposes a machine fault diagnosis method based on multi-source domain deep transfer learning. Firstly, the anchor adapter is constructed to obtain the multi-source domain-target domain adapter data pairs. Secondly, the classifier and prediction results of each data pair are obtained based on the deep domain adaption transfer learning network model. Finally, the weighted integration method is adopted to ensemble classifier for the final fault diagnosis and identification. The proposed method fully ensembles multi-source domain fault feature information, extracts domain invariant features, avoids negative transfer problems, and improves the generalization ability of the model. A rolling bearing dataset is used to verify the performance of the proposed method. The results show that the fault diagnosis classification accuracy of multi-condition migration is significantly higher than that of any single condition migration, and the maximum improvement is 8.78%. Compared with other methods, the proposed method has better accuracy and generalization ability.
杨胜康1，孔宪光1，王奇斌1，程涵1，李中权1,2. 基于多源域深度迁移学习的机械故障诊断[J]. 振动与冲击, 2022, 41(9): 32-40.
YANG Shengkang1, KONG Xianguang1, WANG Qibin1, CHENG Han1, LI Zhongquan1,2. Mechanical fault diagnosis based on multi-source domain deep transfer learning. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(9): 32-40.
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