Tang Ziyi, Hou Xinhao, Huang Xinheng, Wang Xin, Zou Jifeng
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China.
Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China.
Sensors (Basel). 2024 Jun 30;24(13):4251. doi: 10.3390/s24134251.
Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.
域适应技术对于解决实际轴承故障诊断中因运行条件变化导致的训练数据与测试数据分布差异至关重要。然而，在数据分散且分布差异明显的复杂条件下，迁移故障诊断面临重大挑战。因此，本文提出了CWT-SimAM-DAMS，一种基于SimAM和自适应加权策略的轴承故障诊断域适应方法。该方案首先使用连续小波变换（CWT）和非锐化掩模（USM）进行数据预处理，然后使用集成了SimAM模块的残差网络（ResNet）进行特征提取。这与基于联合最大均值差异（JMMD）和条件对抗域适应网络（CDAN）域适应算法提出的自适应加权策略相结合，更有效地最小化源域和目标域之间的分布差异，从而增强域适应性。该方法在两个数据集上得到验证，实验结果表明它提高了轴承故障诊断的准确率。
