一种基于改进的LeNet-5模型滚动轴承故障诊断方法研究

吴晨芳1,杨世锡1,黄海舟2,顾希雯1,隋永枫3

振动与冲击 ›› 2021, Vol. 40 ›› Issue (12) : 55-61.

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (12) : 55-61.
论文

一种基于改进的LeNet-5模型滚动轴承故障诊断方法研究

  • 吴晨芳1,杨世锡1,黄海舟2,顾希雯1,隋永枫3
作者信息 +

An improved fault diagnosis method of rolling bearings based on LeNet-5

  • WU Chenfang1,YANG Shixi1,HUANG Haizhou2,GU Xiwen1,SUI Yongfeng3
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摘要

针对滚动轴承故障样本不完备问题,提出一种基于改进的LeNet-5模型的卷积神经网络故障诊断方法。该方法将包含多种转速的滚动轴承振动原始时域信号以二维灰度图形式作为模型输入,根据信号特点确定输入尺寸,通过卷积操作自适应提取特征,引入批归一化操作提高模型泛化能力,再用softmax分类器实现故障分类识别,最后采用t-分布邻域嵌入算法(t-SNE)直观的展示该方法的特征提取效果。开展滚动轴承多故障实验,分析模型优化的合理性和有效性。实验结果表明,通过对四种转速下的滚动轴承故障数据进行训练和识别,该方法能在有限转速的轴承故障样本中学习其共性特征,可以实现滚动轴承故障的准确分类,并且对其他转速的故障数据同样具有有效性,拓宽了轴承故障诊断的转速泛化能力。将该方法与BP神经网络(BP neural network, BPNN)和支持向量机(support vector machine, SVM)算法进行对比,结果验证了该方法有较好的鲁棒性和泛化能力。研究成果可为保障滚动轴承的可靠性以及设备的安全运行提供参考和借鉴。
 

Abstract

This paper proposed a convolutional neural network fault diagnosis method based on improved LeNet-5, aiming at the incompleteness of rolling bearing fault samples.This method takes the original time-domain vibration signals of rolling bearing containing multiple speeds as the input of the model in the form of two-dimensional grayscale images.The input size is determined according to the signal characteristics, and features are extracted adaptively through convolution operations.This method introduces a batch normalization operation to improve the model and uses the softmax classifier to implement fault classification and recognition.Finally, the t-distribution neighborhood embedding algorithm (t-SNE) is used to objectively demonstrate the feature extraction effect of the method.The rationality and effectiveness of the improved model were verified by the multi-fault experimental analysis of rolling bearings.Experimental results show that by training the rolling bearing fault data at four speeds can learn the common characteristics of bearing fault samples with limited speed, accurate classification of rolling bearing faults can be achieved.And the fault data at other speeds are also valid, which broadens the speed generalization ability of rolling bearing fault diagnosis.The BP neural network (BPNN) and support vector machine (SVM) algorithms were compared with the method proposed in this paper, which proves that the method has good robustness and generalization ability.This work can provide reference and reference for ensuring the reliability of rolling bearings and the safe operation of equipment.

关键词

故障诊断
/ 卷积神经网络 / LeNet-5 / 转速泛化 / 滚动轴承

Key words

fault diagnosis / CNN / LeNet-5 / speed generalization / rolling bearing

引用本文

导出引用
吴晨芳1,杨世锡1,黄海舟2,顾希雯1,隋永枫3. 一种基于改进的LeNet-5模型滚动轴承故障诊断方法研究[J]. 振动与冲击, 2021, 40(12): 55-61
WU Chenfang1,YANG Shixi1,HUANG Haizhou2,GU Xiwen1,SUI Yongfeng3. An improved fault diagnosis method of rolling bearings based on LeNet-5[J]. Journal of Vibration and Shock, 2021, 40(12): 55-61

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