基于特征融合和混类增强的深度学习滚动轴承故障诊断

Deep Learning for Rolling Bearing Fault Diagnosis Based on Feature Fusion and Hybrid Enhancement

轴承故障诊断在维护旋转机械设备和规避重大灾难事故等方面起着至关重要的作用.针对现有故障诊断模型无法适应实际工业应用中变化的工作负载的问题,提出了一种基于特征融合和混类增强的故障诊断方法.首先,在原始信号的基础上融合时频特征、工况特征和时间差分特征形成新的特征信号;然后,采用相空间重构理论将信号特征转换为图像信号,在训练时通过混类增强拓展数据的分布;最后,利用残差网络进行故障诊断分析.在CWRU数据集上的实验结果表明,该方法在同工况下的预测精度高达100%,在变工况下的平均预测精度高达93.28%,域适应性强.

Bearing fault diagnosis plays a vital role in maintaining rotating machinery and avoiding major disasters.Given that the existing fault diagnosis model cannot adapt to the changing working loads in actual industrial applications,a fault diagnosis method based on feature fusion and hybrid enhancement is proposed.For this purpose,new feature signals are generated by fusing time-frequency features,working condition features,and time difference features into the original signal.Then,the phase space reconstruction theory is applied to convert the feature signals into image signals,and data distribution is expanded through hybrid enhancement during training.Finally,the residual network is used for fault diagnosis analysis.The experimental results on the Case Western Reserve University(CWRU)dataset show that the prediction accuracy of this method under invariable working conditions is up to 100%and its average prediction accuracy under changing working conditions reaches 93.28%,which indicates that the proposed method has a remarkable domain adaptability.