基于卷积时空交互融合网络的列车轴承故障诊断

Train Bearing Fault Diagnosis Based on Convolution Temporal-spatial Mutual Fusion Network

[目的]轴箱轴承运行环境的多元复杂性使得采用单一传感器的轴承故障诊断方法难以取得良好的效果。针对此问题,特开展了基于卷积时空交互融合网络的列车轴承故障诊断的研究。[方法]采用多传感器融合方法,在水平-竖直振动数据集上提出了一种CTS-MFN(基于卷积时空交互融合网络)用于轴承故障诊断。将ECA(高效通道注意力)模块、LSTM(长短期记忆)网络和相似性距离约束引入卷积自编码器,使模型能够提取具有模态间交互信息的时间-空间注意特征;在利用MLP(多层感知机)对各模态时空特征进行融合推断。[结果及结论]通过对比试验、消融研究、泛化性能分析等试验,证明了所提模型的有效性。

[Objective]The diverse and complex operating environment of axle box bearings makes it challenging for bearing fault diagnostic methods to achieve satisfactory results with the single sensor.To address this issue,research is conducted on train bearing fault diagnosis based on convolutional temporal-spatial mutual fusion network.[Method]A multi-sensor fusion approach is adopted,applying the proposed CTS-MFN(a convolutional temporal-spatial mutual fusion network)for bearing fault diagnosis upon horizontal and vertical vibration datasets.The ECA module(efficient channel attention),LSTM(long short-term memory network),and similarity distance constraints are introduced to the convolutional autoencoder,enabling the model to extract temporal-spatial attention features that include inter-modal interaction information.An MLP(multilayer perceptron)is then used to fuse and infer temporal-spatial features from each modality.[Result&Conclusion]Comparative experiments,ablation studies,and generalization performance analysis demonstrate the effectiveness of the proposed model.