Fault diagnosis of rolling bearings based on mixed-cutout imbalance data augmentation and the SwinNet network

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (6) : 64-74.

HUO Jiuyuan1,2，LI Yufeng1，CHANG Chen1，LI Chaojie1，XU Jihao1

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Abstract

In the field of fault diagnosis for rolling bearings, this study addresses the challenges of limited fault samples and imbalanced fault categories caused by an abundance of healthy samples. Furthermore, it tackles the issues of noise and artificial label interference present in the environment. To overcome these challenges, a fault diagnosis model named SwinNet-MCIDA, combining hybrid imbalance data augmentation and the SwinNet network, is proposed.Firstly, drawing inspiration from image classification data augmentation techniques, the MCIDA algorithm is employed to crop and mix imbalanced data, generating new fault samples to augment the dataset. The enhanced dataset is then transformed into time-frequency images using wavelet transform. These images are fed into the SwinNet network model, which combines a convolutional neural network and a Swin Transformer encoder, for feature extraction and fault classification, enabling efficient diagnosis of rolling bearing faults. The experimental results demonstrate that the proposed SwinNet-MCIDA fault diagnosis method not only effectively addresses the issue of imbalanced fault categories in the field of rolling bearing fault diagnosis, but also effectively handles the presence of environmental noise and artificial label interference in fault data.

Key words

rolling bearing / fault diagnosis / data augmentation / convolutional neural network(CNN) / Swin Transformer

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HUO Jiuyuan1,2，LI Yufeng1，CHANG Chen1，LI Chaojie1，XU Jihao1. Fault diagnosis of rolling bearings based on mixed-cutout imbalance data augmentation and the SwinNet network[J]. Journal of Vibration and Shock, 2024, 43(6): 64-74

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