基于多尺度深度卷积神经网络的故障诊断方法

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (18) : 204-211.

论文

卞景艺，刘秀丽，徐小力，吴国新

作者信息 +

Fault diagnosis method based on a multi-scale deep convolutional neural network

BIAN Jingyi,LIU Xiuli,XU Xiaoli,WU Guoxin

Author information +

文章历史 +

摘要

针对机电装备故障诊断需要大量专家经验、故障特征识别困难的问题，在一维深度卷积神经网络基础上进行改进，构建多尺度一维深度卷积神经网络(M1DCNN)，提出基于多尺度一维深度卷积神经网络的故障诊断方法:首先在网络输入层构建多个含有不同尺寸卷积核通道的特征提取层，对一维时序信号中故障特征进行多尺度特征提取，丰富智能体诊断信息，将所提取特征通过输入到包含多尺寸卷积核以及多样池化层中进行特征处理，最后合并多通道所处理的特征，使网络完成自我学习，从而实现故障诊断。将该方法应用到西储大学轴承故障数据及行星齿轮箱的故障数据诊断实验，结果表明该方法具有诊断精度高、鲁棒性强的特点，相较于一维卷积神经网络准确率提高1.25%，与反向传播神经网络、循环神经网络相比准确率平均提高3%以上，对网络特征提取效果进行可视化分析，结果表明该方法特征提取效果与诊断精度优于一维卷积神经网络。

Abstract

Aiming at the fault diagnosis of mechanical and electrical equipments that requires a lot of expert experience and usually has difficulty in fault identification, a method based on a multi-scale one-dimensional deep convolutional neural network (M1DCNN) was proposed to improve the original network algorithm by introducing the multi-scale processing.Firstly, several feature extraction layers with different scale convolution kernel channels were constructed in the network input layer, and the fault features in one-dimensional time series signals were extracted by multi-scale feature extraction to enrich the diagnostic information.Then, the extracted features were input into the multi-scale convolution kernel and multiple pooling layers for feature processing.Finally, the features processed by multi-channels were combined to enable the network to complete self-learning to achieve fault diagnosis.The method has been applied to the bearing fault data and planetary gearbox fault data at Case Western Reserve University.The results show that the method has the characteristics of high diagnostic accuracy and strong robustness.Compared with the original one-dimensional convolutional neural network, the accuracy rate is improved by 1.25%, and the accuracy rate is increased by more than 3% on average compared with the BP neural network and recurrent neural network.A visual analysis on the effect of network feature extraction was carried out and the results show that the model feature extraction effect and the diagnostic accuracy of the proposed method are better than the conventional one-dimensional convolutional neural network.

导出引用

卞景艺，刘秀丽，徐小力，吴国新. 基于多尺度深度卷积神经网络的故障诊断方法[J]. 振动与冲击, 2021, 40(18): 204-211

BIAN Jingyi,LIU Xiuli,XU Xiaoli,WU Guoxin. Fault diagnosis method based on a multi-scale deep convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(18): 204-211

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