Sequential Domain Adaptation for Fault Diagnosis in Rotating Machinery

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Published Sep 4, 2023
Yong Chae Kim Jin Uk Ko Jinwook Lee Taehun Kim Joon Ha Jung Byeng D. Youn

Abstract

Fault diagnosis of the machinery system is essential to minimize the damage to the industrial field. Recently, with the development of computer and IoT technology, deep learning-based fault diagnosis has been widely researched. However, due to the domain shift, which changes the distribution of data under different operating conditions of the machinery system, the performance of the deep learning-based fault diagnosis algorithm decreases. This paper proposes a sequential domain adaptation to alleviate different distributions between different operating conditions. The proposed method has been validated in open-source datasets and shows a high performance compared to the other models.

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Keywords

fault diagnosis, rotating machinery, domain adaptation

References
Lee, J., Kim, M., Ko, J.U., Jung, J.H., Sun, K.H., & Youn, B.D. (2022). Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery. Reliability Engineering & System Safety. vol.218. doi.org/10.1016/j.ress.2021.108186.

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Guo, L., Lei, Y., Xing, S., Tan, T., & Li, N. (2018). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, vol.66(9) pp. 7316-7325. doi.org/10.1109/TIE.2018.2877090
Section
Regular Session Papers