一种基于多通道马尔可夫变迁场的故障诊断方法

doi:10.13229/j.cnki.jdxbgxb20210669

Abstract

Abstract:

Deep learning has good diagnostic capabilities and generalization capabilities in fault diagnosis， but most of the work is to directly extract signal feature maps from the convolutional layer so that adjacent signal points are not considered， and different sampling frequencies will also affect feature extraction. Therefore， the M2TF-ResNet algorithm was proposed based on the MTF and ResNet18 algorithm. Many experiments were carried out in Case Western Reserve University （CWRU） bearing dataset. Through the verification， it can adapt to the signal feature extraction under different sampling frequencies and avoid over-fitting training. And compared with other fault diagnosis methods， it has more prominent advantages in the diagnosis rate.

Key words: fault diagnosis, deep learning, Markov transition field, residual network

CLC Number:

TP277

Jie CAO,Jia-lin MA,Dai-lin HUANG,Ping YU. A fault diagnosis method based on multi Markov transition field[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 491-496.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Table 1

Structure of ResNet18"

网络层名称	参数	输出
Conv Block 1	7×7，64， strider 2	112×112
Conv Block 2，x	3×3 max pool， strider 2 $3 × 3, 64 3 × 3, 64 × 2$ dropout 0.2	56×56
Conv Block 3，x	$3 × 3, 128 3 × 3, 128 × 2$ dropout 0.2	28×28
Conv Block 4，x	$3 × 3, 256 3 × 3, 256 × 2$ dropout 0.2	14×14
Conv Block 5，x	$3 × 3, 512 3 × 3, 512 × 2$ dropout 0.2	7×7
Classification	Average pool， fc， SoftMax	1×4

Table 1

Table 2

CWRU bearing data setting"

数据集名称	电机转速/（r $?$ min $- 1$ ）	电机载荷	采样点数目	训练集：测试集
CWRU 1797	1797	0	400	4 $∶$ 1
CWRU 1772	1772	1	400
CWRU 1750	1750	2	400
CWRU 1730	1730	3	400

Table 2

Table 3

Fig.4

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数据集名称	网络结构	诊断率	最大诊断率
CWRU 1797	Conv1D	0.9966（±0.0016）	0.9985
	MTF-ResNet	0.9923 （±0.0035）	0.9984
	2D-ResNet	0.9312（±0.0306）	0.9766
	M2TF-ResNet	0.9990（±0.0009）	1.0000
CWRU 1772	Conv1D	0.9987（±0.0009）	1.0000
	MTF-ResNet	0.9799（±0.0124）	0.9967
	2D-ResNet	0.9317（±0.0314）	0.9790
	M2TF-ResNet	0.9972（±0.0029）	1.0000
CWRU 1750	Conv1D	0.9985（±0.0007）	0.9996
	MTF-ResNet	0.9633（±0.0264）	0.9848
	2D-ResNet	0.9279（±0.0223）	0.9589
	M2TF-ResNet	0.9974（±0.0024）	1.0000
CWRU 1730	Conv1D	0.9981（±0.0005）	0.9988
	MTF-ResNet	0.9755（±0.0229）	0.9944
	2D-ResNet	0.9094（±0.0270）	0.9511
	M2TF-ResNet	0.9931（±0.0061）	1.0000

A fault diagnosis method based on multi Markov transition field

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