基于非降维注意力机制的滚动轴承故障诊断方法

Rolling Bearing Fault Diagnosis Method Based on Non-dimensionlity Reduction Attention Mechanism with Aggregate Residual Network

针对传统轴承故障诊断算法中存在的故障特征提取困难、模型泛化性差以及噪声环境下诊断准确率低等问题,提出一种可移植非降维注意力机制与深度残差神经网络相结合的故障诊断方法。该方法使用非降维注意力机制对残差块生成的特征图重新分配权重,对特征图采用局部而非全局的跨通道通信方式,自适应学习邻近通道的注意力分数,以增强故障轴承的特征识别率。使用凯斯西储大学轴承故障数据集对本文方法进行验证,实验表明,融合非降维注意力机制的残差网络可以准确识别出不同负载下混有噪声的故障轴承样本,在12 dB信噪比噪声情况下的准确率为99.5%,具有较强的抗噪性能和一定的泛化性能。

Aimed at the difficulties of extracting fault features,poor model generalization,and low diagnostic accuracy under noisy environments in traditional bearing fault diagnosis algorithms,a fault diagnosis method,which combines a portable non-dimensionlity reduction attention mechanism with deep residual neural network,was proposed.This method uses a non-dimensionality reduction attention mechanism to redistribute the weights of the feature maps generated by residual block.Simultaneously,local cross-channel communication methods rarher than global cross-channel communication methods are adopted in achieving the effect of non-dimensionality reduction and adaptively learning the attention scores of neighboring channels.Case Western Reserve University’s bearing fault datasets were used to verify the method.Experiments results show that the residual network fused with non-dimensionality reduction attention mechanism can accurately identify faulty bearing samples disturbed by noise under different loads.Specifically,the diagnosis accuracy under 12 dB signal-to-noise ratio is 99.5%,with strong anti-noise performance and certain generalization performance.