电子测量与仪器学报

谢锦阳,姜媛媛,王力.基于 RA-LSTM 的轴承故障诊断方法[J].电子测量与仪器学报,2022,36(6):213-219

基于 RA-LSTM 的轴承故障诊断方法

RA-LSTM based bearing fault diagnosis method

DOI：

英文关键词:bearing fault diagnosis reverse attention mechanism LSTM

基金项目:安徽省重点研究与开发计划(202104g01020012)、安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助

作者	单位
谢锦阳	1. 安徽理工大学人工智能学院
姜媛媛	2. 安徽理工大学电气与信息工程学院，3. 安徽理工大学环境友好材料与职业健康研究院(芜湖)
王力	2. 安徽理工大学电气与信息工程学院

Author	Institution
Xie Jinyang	1. School of Institute of Artificial Intelligence, Anhui University of Science and Technology
Jiang Yuanyuan	2. School of Electrical and Information Engineering, Anhui University of Science and Technology，3. Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology
Wang Li	2. School of Electrical and Information Engineering, Anhui University of Science and Technology

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中文摘要:

为解决轴承故障诊断时网络模型不能对一维振动信号中的特征按照重要程度分配不同的权重,导致无法提取具有代表性意义的特征,进而影响诊断模型的精确度与鲁棒性的问题。提出基于反向注意力机制( reverse attention mechanism,RA)的特征突出处理方法,通过将特征进行注意力反向与剪枝,降低非重要特征占比,从而对重要特征进行凸显。并通过长短期记忆网络(LSTM)进一步学习特征之间的时间信息后通过全连接层进行故障类型分类。通过实验选取了最优数据截取长度、剪枝超参数并对信号添加噪声后模型的稳定性进行了验证。实验结果表明所提出的 RA-LSTM 轴承故障诊断方法具有优异的故障诊断性能,故障诊断精度能达到 100%,且在添加噪声后模型的诊断能力仍具有优异的鲁棒性。

英文摘要:

In order to solve the problem that the diagnostic model cannot assign different weights to the features in the one-dimensional vibration signal according to their importance, which leads to the failure to extract representative features and thus affects the accuracy and robustness of the diagnostic model. A feature highlighting method based on reverse attention amplification mechanism ( RA) is proposed to reduce the proportion of non-important features by reversing attention and pruning the features, so as to highlight the important features. The long short term memory (LSTM) network is used to learn the temporal information between the features and to classify the fault types through the fully connected layer. The optimal data interception length, pruning hyperparameters and the stability of the model after adding noise to the signal are selected experimentally. The optimal data interception length, pruning hyperparameters are experimentally selected and verified the stability of the model after adding noise to the signal. The experimental results show that the proposed RA-LSTM bearing fault diagnosis method has excellent fault diagnosis performance, with fault diagnosis accuracy reaching 100% and excellent robustness even after the addition of noise.

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