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Unsupervised domain adaptation transfer learning for the fault diagnosis in rotating machinery |
ZHOU Xiangqi,FU Zhongguang,GAO Yucai |
Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education, North China Electric Power University, Beijing 102206, China |
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Abstract Fault diagnosis is of great importance in the domain of rotating machinery, and the development of deep learning and transfer learning has provided new avenues in order to enhance the precision and resilience of fault diagnosis. In the context of fault diagnosis in rotating machinery, an unsupervised domain adaptation transfer learning method based on Domain-Adversarial Neural Network (DANN) and Multiple Kernel Maximum Mean Discrepancy (MK-MMD) is proposed. Firstly, vibration signal data from both the source working condition and the target working condition are gathered and converted into frequency domain signals utilizing the Fast Fourier Transform (FFT). Then, a ResNeXt-50 feature extractor is constructed, and DANN and MK-MMD methods are employed for feature mapping and domain adaptation, enabling transfer learning from the source working condition to the target working condition. The experimental findings validate that the proposed method enhances the accuracy of fault feature recognition. and exhibits better robustness in transfer experiments across different working conditions.
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Received: 17 July 2023
Published: 28 May 2024
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Corresponding Authors:
Unsupervised domain adaptation transfer learning for the fault diagnosis in rotating machinery
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