基于多注意力机制的端到端滚动轴承故障诊断方法

Multi-Attention Mechanism Based End-to-End Rolling Bearing Fault Diagnosis Method

针对传统滚动轴承故障诊断中复杂的特征提取问题,利用深层残差网络能够增强诊断模型非线性表征能力的特点,通过引入通道注意力与空间注意力机制,提出一种基于多注意力机制端到端的滚动轴承智能故障诊断方法。首先,通过原始振动加速度信号经过积分运算得到速度和位移;然后,将3者组合成具有特征增强的图像,输入至结合了多注意力机制的深层残差网络实现特征提取;最后,利用多分类函数完成滚动轴承故障分类。在本地实验室轴承数据集上进行了验证,结果表明,所提方法的诊断准确率达到了97.50%。验证了基于多注意力机制端到端的滚动轴承智能故障诊断方法的可行性和有效性,可为滚动轴承的精确故障诊断提供支持。

To address the complex feature extraction problem in traditional rolling bearing fault diagnosis,an end-to-end rolling bearing intelligent fault diagnosis method based on a multi-attention mechanism is proposed by introducing channel attention and spatial attention mechanism using the feature that deep residual network can enhance the nonlinear characterization ability of the diagnosis model.Firstly,the vibration velocity and displacement signals are obtained by integrating the original vibration acceleration signal.Secondly,the three types of signals are combined into an image with feature enhancement and input to a deep residual network combined with a multi-attention mechanism for feature extraction.Finally,a multi-classification function is used to complete the rolling bearing fault classification.The validation was carried out on a local laboratory-bearing dataset,and the results showed that the diagnostic accuracy of the proposed method reached 97.50%.The feasibility and effectiveness of the end-to-end rolling bearing intelligent fault diagnosis method based on a multi-attention mechanism are verified,which can support the accurate fault diagnosis of rolling bearings.