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- W4387443640 abstract "As a key component of motor, the operating status of rolling bearings directly affects the overall function and performance of each mechanism system of mechanical equipment The traditional rolling bearing intelligent fault diagnosis method has problems such as requiring a large amount of in-service data for fault analysis, poor generalization capability, etc. In addition, it is extremely costly to obtain labeled rolling bearing high quality condition data. Transfer learning is one of the methods that can effectively solve the above problems. In this paper, we propose a transfer learning model based on Coral transfer loss function as a rolling bearing fault diagnosis method. The model uses ResNet50 as the feature extractor, Coral loss function as the transfer loss function, and CrossEntropyLoss as the classification loss function. Finally, we validated the feasibility of the proposed method by using the Case Western Reserve University bearing dataset under variable operating conditions, the model was validated with correct transfer rates of over 90% for all twelve transfer types; which greatly surpasses other typical transfer learning fault diagnosis methods, which proved the rationality of the proposed method." @default.
- W4387443640 created "2023-10-10" @default.
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- W4387443640 date "2023-08-28" @default.
- W4387443640 modified "2023-10-11" @default.
- W4387443640 title "A Domain Adaptation Method Based on Deep Coral for Rolling Bearing Fault Diagnosis" @default.
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- W4387443640 doi "https://doi.org/10.1109/sdemped54949.2023.10271495" @default.
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