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

doi:10.13229/j.cnki.jdxbgxb20200702

Abstract

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

CLC Number:

V267

Lao-hu YUAN,Dong-shan LIAN,Liang ZHANG,Yi LIU. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.

Figures/Tables 10

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故障类型	实测转速/ （r·min^-1）	训练/测试样本个数	类别号
正常	880	30/8	1
大齿轮点蚀	880	30/8	2
大齿轮断齿	878	30/8	3
小齿轮磨损	881	30/8	4

模型	准确率/%	训练时间/s
本文	99.68±0.87	105.29
标准DenseNet	92.05±1.13	2028.00
标准SVM	84.38±12.5	26.07

类别	方法	诊断准确率/%
浅层学习	BPNN	89.06
	PNN	96.88
	GWO-KELM	95.58
深度学习	SDAE	95.17
深度学习	本文	99.68

故障类型	破坏直径/mm	样本个数	类别号
轴承正常	0.0000	76	1
内圈故障	0.1778	76	2
	0.3556	76	3
	0.5334	76	4
	0.7112	76	5
滚动体故障	0.1778	76	6
	0.3556	76	7
	0.5334	76	8
	0.7112	76	9
外圈故障	0.1778	76	10
	0.3556	76	11
	0.5334	76	12

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

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

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