基于多尺度CNN与双阶段注意力机制的轴承工况域泛化故障诊断

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (2) : 267-278.

故障诊断分析

基于多尺度CNN与双阶段注意力机制的轴承工况域泛化故障诊断

乔卉卉1，赵二贤1，郝如江*1，刘婕2，刘帅1，王勇超1

作者信息 +

Operating condition domain generalization fault diagnosis method for bearings based on a multiscale convolutional neural network and the two-stage attention mechanism

QIAO Huihui1, ZHAO Erxian1, HAO Rujiang*1, LIU Jie2, LIU Shuai1, WANG Yongchao1

Author information +

文章历史 +

摘要

变工况条件下，基于深度学习的列车轮对轴承故障诊断模型的训练集与测试集通常来自不同的工况，不同工况振动信号数据分布差异引起的领域漂移问题导致模型准确率降低。基于域适应的变工况轴承故障诊断方法需要获取目标工况域的样本数据参与训练，这在工程实际中难以实现，因此无法实现未知工况的轴承故障诊断。针对以上问题，本文提出了一种基于多尺度CNN与双阶段注意力机制网络模型（TSAMCNN）的轴承工况域泛化故障诊断方法，其中多尺度特征提取模块从多个尺度上提取时域振动信号中更丰富的故障信息；然后双阶段注意力模块从通道和空间两个维度自适应地增强故障敏感特征并抑制工况敏感特征和无用特征，最终提取工况域不变故障特征，从而实现工况域泛化轴承故障诊断。通过变转速和变负载列车轮对轴承故障诊断实验，证明了TSAMCNN模型可提高变工况条件下轴承故障诊断的准确率、抗噪性能和工况域泛化能力。此外，对双阶段注意力机制的权重向量和模型各模块提取的特征进行可视化分析，提高了模型可解释性。

Abstract

Under variable operating conditions, the training set and testing set of the deep learning-based train wheelset bearing fault diagnosis model usually come from different operating conditions, and the domain shift problem caused by the distribution difference of vibration signals under different operating conditions leads to the diagnosis accuracy reduction. The domain adaptation-based bearing fault diagnosis method under variable operating condition needs to obtain sample of the target domain to participate in the training process, which is infeasible in engineering practice. Thus the domain adaptation-based method is not suitable for bearing fault diagnosis of unseen conditions. Aiming at the above problems, an operating condition domain generalization fault diagnosis method based on multi-scale CNN and two-stage attention mechanism network model (TSAMCNN) is proposed in this paper. First, the multi-scale feature extraction module can extract richer fault information in the time-domain vibration signals from multiple scales. Then, the two-stage attention module can adaptively enhance the fault-sensitive features and suppress the condition-sensitive and useless features from both the channel and space dimensions. The model is ultimately capable of extracting condition-invariant fault features, which is the key to achieving the operating condition domain generalization fault diagnosis. Through the variable speed and the variable load bearing fault diagnosis experiments, it is proved that the proposed TSAMCNN model can improve the accuracy of bearing fault diagnosis, noise immunity performance and operating condition domain generalization ability. In addition, the weight vectors of the two-stage attention mechanism and the features extracted by each module of the model are visualised and analysed to improve the model interpretability.

导出引用

乔卉卉1, 赵二贤1, 郝如江1, 刘婕2, 刘帅1, 王勇超1. 基于多尺度CNN与双阶段注意力机制的轴承工况域泛化故障诊断[J]. 振动与冲击, 2025, 44(2): 267-278

QIAO Huihui1, ZHAO Erxian1, HAO Rujiang1, LIU Jie2, LIU Shuai1, WANG Yongchao1. Operating condition domain generalization fault diagnosis method for bearings based on a multiscale convolutional neural network and the two-stage attention mechanism[J]. Journal of Vibration and Shock, 2025, 44(2): 267-278

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