This paper proposes a fault diagnosis framework for very low-speed bearings using transfer learning. To handle large domain discrepancies between general-speed and very lowspeed bearings, the quantitative analysis is performed using operating information and geometries of bearings. From this analysis, the domain discrepancy can be quantitively compared under various speed conditions. Furthermore, a transfer learning technique is proposed to reduce the analyzed discrepancies. The domain discrepancy can be significantly aligned by integrating the operating information and geometries of bearings into transfer learning. Also, the proposed framework is not tailored for the specific algorithm; this means that the framework can be applied to any transfer learning technique regardless of the architecture. The proposed method is validated using two bearing datasets under general-speed and very low-speed conditions. The results show that the domain discrepancy can be quantitatively measured for transfer learning. Additionally, the proposed fault diagnosis framework outperforms the existing methods by aligning domain discrepancies of bearing datasets under large different speeds.
Bearing fault diagnosis, Very low-speed bearing, Transfer learning, Domain alignment
Li, C., Zhang, S., Qin, Y., & Estupinan, E. (2020). A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, vol. 407, pp. 121-135. doi: 10.1016/j.neucom.2020.04.045
Ding, P., Jia, M., & Zhao, X. (2021). Meta deep learning based rotating machinery health prognostics toward few-shot prognostics. Applied Soft Computing, vol. 104, doi:10.1016/j.asoc.2021.107211
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