Data augmentation of rolling bearing faults based on SDP and W-DCGAN

Abstract

Abstract: When the traditional generative adversarial network is used to solve the fault diagnosis of rolling bearing with small samples, low quality of generated samples and unstable training process are the main defects of the network. Therefore, a new method combining symmetrized dot pattern (SDP) and Wasserstein-distance deep convolutional generative adversarial network (W-DCGAN) is proposed to augment the bearing data with small samples. First, the SDP with optimized parameters is used to transform the one-dimensional time-domain signal to the two-dimensional image suitable for inputting into W-DCGAN. Then, the controlled variable method is used to optimize the hyperparameters of the generator and discriminator to determine the basic structure of the W-DCGAN. Meantime, in order to enhance the stability of training process of W-DCGAN, spectral normalization is added into discriminator to improve the quality of generated samples. The performance of the method is verified by an open rolling bearing dataset. The results show that the quality of the generated samples of the proposed method is significantly improved. The structural similarity and peak signal-to-noise ratio of the generated samples relative to the original samples increase by 10.12% and 12.46%, respectively. For the rolling bearing fault identification, the proposed method has the highest accuracy of fault identification, reaching 99.47%, indicating its effectiveness in small sample data augmentation andpattern recognition for rolling bearing fault.

Key words: rolling bearing, small sample, data augmentation, W-DCGAN, symmetrized dot pattern

CLC Number:

SONG Hui-xian, DENG Lin-feng, ZHENG Yu-qiao. Data augmentation of rolling bearing faults based on SDP and W-DCGAN[J]. Journal of Lanzhou University of Technology, 2025, 51(6): 55-64.

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