Abstract: Aiming at the problems that traditional fault diagnosis methods cannot adaptively select features and are difficult to cope with load changes and noise interference,an end-to-end fault diagnosis method based on attention mechanism was proposed.The spatial features of the original vibration signal were extracted through Convolutional Neural Network (CNN),and the temporal features were extracted based on the Bidirectional Long Short-Term Memory Network (BiLSTM).The attention mechanism was used to judge the importance of the hidden layer state at each time of BiLSTM and give the corresponding weight.The hidden layer state at all times was weighted and summed,and the Softmax layer was used as the classifier for fault diagnosis.The data collected by VALENIAN-PT500 and public data were used for experimental verification.The results showed that the proposed method had high diagnostic accuracy and strong generalization,and could maintain good fault diagnosis performance under variable load and noise interference.

Key words: bearing fault diagnosis, attention mechanism, convolutional neural network, bidirectional long short-term memory network

摘要： 针对传统故障诊断方法无法自适应选择特征以及难以应对负载变动、噪声干扰的问题,提出一种基于注意力机制的端对端故障诊断方法,通过卷积神经网络(CNN)对原始振动信号进行空间特征提取,基于双向长短时记忆网络(BiLSTM)提取时序特征,利用注意力机制判断BiLSTM各时刻隐藏层状态的重要性并赋予相应的权重,对所有时刻的隐藏层状态进行加权求和,并以Softmax层作为分类器进行故障诊断。利用VALENIAN-PT500实验台采集的数据和公开数据进行实验验证,结果表明,所提方法诊断精度高、泛化性强,在变负载和噪声干扰条件下能保持良好的故障诊断性能。

关键词: 轴承故障诊断, 注意力机制, 卷积神经网络, 双向长短时记忆网络