针对在滚动轴承故障诊断领域中存在的故障样本较少，健康样本丰富所导致的故障类别失衡问题以及环境中存在噪声与人为噪声标签干扰等问题，提出了一种基于混合裁剪失衡数据增强与SwinNet网络相结合的故障诊断模型（SwinNet-MCIDA）。首先，借鉴图像分类数据增强方法，利用混合裁剪失衡数据增强算法对失衡类别的数据进行裁剪、混合处理生成新的故障样本来增加样本量，构造出增强数据集，然后对增强数据集进行小波变换转换成时频图像，将所得图像输入到卷积神经网络与Swin Transformer编码器相结合的SwinNet网络模型中，进行特征提取和故障分类，从而实现滚动轴承故障的高效诊断。实验结果表明，本文所提出的SwinNet-MCIDA故障诊断方法不仅可以很好地解决滚动轴承故障诊断领域故障类别失衡问题，而且也可以很好地应对故障数据中存在环境噪声问题与人为噪声标签干扰问题。
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.
关键词
滚动轴承 /
故障诊断 /
数据增强 /
卷积神经网络 /
Swin Transformer
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Key words
rolling bearing /
fault diagnosis /
data augmentation /
convolutional neural network(CNN) /
Swin Transformer
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