基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究

刘恒畅1,2,姚德臣1,2,杨建伟1,2,张骄3

振动与冲击 ›› 2021, Vol. 40 ›› Issue (10) : 95-102.

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (10) : 95-102.
论文

基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究

  • 刘恒畅1,2,姚德臣1,2,杨建伟1,2,张骄3
作者信息 +

Fault diagnosis of rolling bearings based on a multi branch depth separable convolutional neural network

  • LIU Hengchang1,2,YAO Dechen1,2,YANG Jianwei1,2,ZHANG Jiao3
Author information +
文章历史 +

摘要

针对传统滚动轴承故障诊断方法存在抗噪性差、需要人工特征提取、计算量较大、对运行设备要求高的问题,提出一种基于多分支深度可分离卷积神经网络(MBDS-CNN)的滚动轴承故障诊断方法,利用深度可分离卷积和权重剪枝技术对模型尺寸进行压缩,通过多分支结构保证模型的精度,避免梯度消失现象的发生。使用模型尺寸、诊断精度、预测速度作为评价指标对模型进行评估。试验结果证明,基于多分支深度可分离卷积神经网络的滚动轴承故障诊断,可以在噪声环境下有效识别轴承不同部位故障程度,提高了诊断效率,降低了对运行设备性能的要求。

Abstract

Aiming at the disadvantages of traditional rolling bearing fault diagnosis methods, such as poor robust, need for artificial feature extraction, large amount of computation, and high requirements for the running equipment, a fault diagnosis method for rolling bearings based on a multi branch depth seperable convolutional neural network (MBDS-CNN) was proposed.Using the depth separable convolution and weight pruning technology to compress the model size, the multi-branch structure ensures the accuracy of the model and avoids the phenomenon of gradient disappearance.The model was evaluated by using a test set, using the model size, diagnostic accuracy, and prediction speed as evaluation indicators.The experimental results show that the fault diagnosis method for rolling bearings based on the MBDS-CNN can effectively identify the fault degree of different parts of the bearing in the noise environment, improve the diagnostic efficiency and reduce the performance requirements of the running equipment.

关键词

滚动轴承 / 故障程度 / 抗噪性 / 卷积神经网络(CNN) / 故障诊断

Key words

rolling bearing / fault degree / anti-noise / convolutional neural network(CNN) / fault diagnosis

引用本文

导出引用
刘恒畅1,2,姚德臣1,2,杨建伟1,2,张骄3. 基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究[J]. 振动与冲击, 2021, 40(10): 95-102
LIU Hengchang1,2,YAO Dechen1,2,YANG Jianwei1,2,ZHANG Jiao3. Fault diagnosis of rolling bearings based on a multi branch depth separable convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(10): 95-102

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