基于改进门控卷积神经网络的不平衡滚动轴承故障诊断

IF 5.3 Q1 ENGINEERING, MECHANICAL International Journal of Hydromechatronics Pub Date : 2023-01-01 DOI:10.1504/ijhm.2023.130520
Changsheng Xi, Jie Yang, Xiaoxia Liang, Rahizar Bin Ramli, Shaoning Tian, Guojin Feng, Dong Zhen
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引用次数: 4

摘要

为了提高深度学习模型处理不平衡数据的能力,提出了一种基于改进门控卷积神经网络(IGCNN)的故障诊断方法。首先,提出了一种改进的门控卷积层用于特征提取,并采用批处理归一化(BN)层调整数据分布,增强模型的泛化性能;然后,将多个门控卷积层和池化层学习到的特征馈送到全连接层进行故障类型识别。最后,利用标签分布感知边际损失函数(LDAM)调整模型,使其对少数类更敏感,减轻不平衡数据对模型的影响。利用两个轴承数据集进行了实验验证。结果表明,该方法比其他故障诊断方法具有更强的鲁棒性,在严重不平衡数据集中具有更高的识别精度。
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An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data
To improve the ability of the deep learning model to handle imbalanced data, a fault diagnosis method based on improved gated convolutional neural network (IGCNN) is proposed. Firstly, an improved gated convolution layer is proposed for feature extraction, with the batch normalisation (BN) layer applied to adjust the data distribution and enhance the generalisation performance of the model. Then, the feature learned by multiple gated convolution layers and pooling layers is fed to the fully connected layer for fault type identification. Finally, the label-distribution-aware margin (LDAM) loss function is employed to adjust the model being more sensitive to the minority class and mitigate the influence of imbalanced data on the model. Experimental validation is conducted using two bearing datasets. Results show that the proposed method is more robust than other fault diagnosis methods, with higher recognition accuracy in severely imbalanced dataset.
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来源期刊
CiteScore
7.60
自引率
0.00%
发文量
32
期刊最新文献
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