Time Shifting Data Augmentation to Alleviate Class-Imbalance Problem for Cross-Domain Bearing Fault Diagnosis

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Published Sep 4, 2023
Donghwi Yoo Minseok Choi Hyunseok Oh

Abstract

This paper presents a new cross-domain fault diagnostic method for rolling element bearings with class-imbalanced datasets. The key idea to alleviate the class imbalance problem is the incorporation of the data augmentation strategy. This study proposes a new data augmentation technique, namely, time shifting data augmentation (TS- DA). Synthetic data is generated to balance the number of normal and fault data. The validity of the proposed method is evaluated using a dataset from the bearing testbed. The results show that the proposed method augments different types of bearing fault data effectively and outperforms existing methods under the class imbalance problem.  

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Keywords

Bearing fault diagnosis, Data augmentation, Artificial intelligence

References
Long, M., Cao, Y., Wang, J., & Jordan, M. (2015). Learning transferable features with deep adaptation networks. International Conference on Machine Learning, pp. 97-105.

Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. International Conference on Machine Learning, pp. 1180-1189.
Section
Special Session Papers