吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (5): 1635-1641.doi: 10.13229/j.cnki.jdxbgxb20200702

• 车辆工程·机械工程 • 上一篇    

基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断

院老虎1(),连冬杉1,张亮2,刘义3   

  1. 1.沈阳航空航天大学 航空宇航学院,沈阳 110136
    2.北京宇航系统工程研究所,北京 100076
    3.北京机械工业自动化研究所有限公司,北京 100120
  • 收稿日期:2020-09-14 出版日期:2021-09-01 发布日期:2021-09-16
  • 作者简介:院老虎(1978-),男,副教授,博士.研究方向:飞行器健康监测与故障诊断.E-mail:ylhhit@126.com
  • 基金资助:
    国家自然科学基金项目(11302134)

Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine

Lao-hu YUAN1(),Dong-shan LIAN1,Liang ZHANG2,Yi LIU3   

  1. 1.College of Aerospace Engineering,Shenyang Aerospace University,Shenyang 110136,China
    2.Beijing Institute of Aerospace Systems Engineering,Beijing 100076,China
    3.Beijing Machinery Industry Automation Research Institute Co. ,Ltd. ,Beijing 100120,China
  • Received:2020-09-14 Online:2021-09-01 Published:2021-09-16

摘要:

针对旋转机械故障诊断浅层学习方法的高级特征提取问题和实际工程中可利用故障样本数量较少对诊断精度影响大的问题,提出了一种基于密集连接卷积网络(DenseNet)和支持向量机(SVM)的旋转机械故障诊断方法。首先,使用连续小波变换(CWT)将振动信号段转换为时频图像样本;然后,将试验样本输入DenseNet网络模型进行深层特征的提取;最后,将提取到的特征输入SVM模型进行训练,从而实现旋转机械的故障诊断。仿真结果表明:与其他先进模型相比,本文方法得到了更高的诊断准确率,证明了该方法的有效性和可行性。

关键词: 航空工程, 密集连接卷积网络, 支持向量机, 飞行器, 机械部件, 故障诊断

Abstract:

Aiming at the difficulty of deep feature extraction in the shallow learning methods of rotating machinery fault diagnosis and the problem of the small number of available fault samples in practical engineering, a rotating machinery fault diagnosis method based on densely connected convolutional network (DenseNet) and support vector machine (SVM) is proposed, which provides a new idea for the fault diagnosis of key mechanical components of aircraft. First, the continuous wavelet transform(CWT) is used to convert vibration signal segments into time-frequency image samples. Then, the experimental samples are input into the DenseNet model for feature extraction. Finally, the extracted features are used for the training of SVM, so as to realize the fault diagnosis of rotating machinery. The simulation results show that the proposed method has higher diagnostic performance than other advanced models, which proves the effectiveness and feasibility of the method.

Key words: aeronautical engineering, densely connected convolutional network, support vector machine, aircraft, mechanical components, fault diagnosis

中图分类号: 

  • V267

图1

试验流程图"

图2

数据划分"

图3

图像转换"

图4

DenseNet网络模型"

表1

齿轮箱样本类别描述"

故障类型实测转速/ (r·min-1

训练/测试

样本个数

类别号
正常88030/81
大齿轮点蚀88030/82
大齿轮断齿87830/83
小齿轮磨损88130/84

图5

故障诊断结果"

表2

各模型诊断准确率及训练时间"

模型准确率/%训练时间/s
本文99.68±0.87105.29
标准DenseNet92.05±1.132028.00
标准SVM84.38±12.526.07

表3

各模型诊断结果"

类别方法诊断准确率/%
浅层学习BPNN89.06
PNN96.88
GWO-KELM95.58
深度学习SDAE95.17
本文99.68

表4

CWRU数据集样本类型描述"

故障类型破坏直径/mm样本个数类别号
轴承正常0.0000761
内圈故障0.1778762
0.3556763
0.5334764
0.7112765
滚动体故障0.1778766
0.3556767
0.5334768
0.7112769
外圈故障0.17787610
0.35567611
0.53347612

表5

本文方法和其他方法的诊断准确率"

方法样本类型准确率/%
CNN-HMM原始数据98.125
DAFD频域信号94.730
DGNN频域信号97.810
ResNet-SVM时频图像98.750
本文模型时频图像99.060
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