基于非平稳信号组合分析的故障诊断方法

doi:10.11959/j.issn.1000-436x.2020099

Abstract

Abstract:

Considering the complementarity between the deep learning,spectrum and time frequency analysis methods,a multi-stream framework was designed by combining the convolutional network,Fourier transform and wavelet package decomposition methods,with the aim to analyze the non-stationary signal.Accordingly,a none-stationary signal combined analysis based fault diagnosis method was proposed to extract features in difference aspects.The fault diagnosis experiments demonstrate that the combined analysis method can efficiently and stably depict the fault and significantly improve the performance of fault diagnosis.

Key words: none-stationary signal, fault diagnosis, signal processing, deep learning, feature fusion

CLC Number:

TP391

Zhe CHEN,Yuqi HU,Shiqing TIAN,Huimin LU,Lizhong XU. Non-stationary signal combined analysis based fault diagnosis method[J]. Journal on Communications, 2020, 41(5): 187-195.

Figures/Tables 18

References 28

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模块	层	卷积核
	输入层	—
CB-CNN	第一层卷积层	64
	第一级级联块	32,64]
	第二级级联块	128,256]
SCE	全连接层1	17
	全连接层2	272
分类器	全连接层3	100
	全连接层4	10

分解层数	准确率
3	99.89%
4	99.85%
5	99.81%
6	99.67%
7	99.80%

方法	平均分类准确率
SVM	99.38%
Bayes	98.95%
SAE	99.56%
SDAE	99.25%
组合分析法	99.85%

方法	平均分类准确率
WDCNN	99.78%
PCNN	99.21%
MSCNN	99.53%
MC-CNN	99.71%
MDI-CNN	99.75%
本文组合分析法	99.85%

方法				分类准确率
方法	SNR= -4 dB	SNR=-2 dB	SNR=0 dB	SNR=2 dB	SNR=4 dB	SNR=6 dB	SNR=8 dB	SNR=10 dB
WDCNN	92.5%	97.04%	98.79%	99.37%	99.53%	99.61%	99.63%	99.79%
PCNN	81.34%	89.41%	91.34%	93.18%	95.77%	96.11%	98.75%	99.14%
MSCNN	90.21%	92.44%	95.37%	96.12%	98.63%	99.27%	99.51%	99.61%
MC-CNN	86.53%	90.13%	93.43%	96.85%	97.99%	98.15%	99.21%	99.73%
MDI-CNN	95.16%	96.65%	98.59%	99.21%	99.87%	99.71%	99.83%	99.89%
组合分析法	96.67%	97.15%	99.12%	99.46%	99.71%	99.82%	99.88%	99.93%

Non-stationary signal combined analysis based fault diagnosis method

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Figures/Tables 18

References 28

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模式	训练集负载	测试集负载
模式1	1hp	2hp
模式2	1hp	3hp
模式3	2hp	1hp
模式4	2hp	3hp
模式5	3hp	1hp
模式6	3hp	2hp

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方法	模式1	模式2	模式3	模式4	模式5	模式6	平均值
WDCNN	99.2%	91.0%	95.1%	91.5%	78.1%	85.1%	90.0%
PCNN	81.5%	88.9%	93.2%	89.6%	75.0%	76.0%	84.0%
MSCNN	99.3%	95.6%	91.1%	84.0%	85.0%	86.0%	90.2%
MC-CNN	93.3%	90.4%	88.3%	84.1%	85.7%	96.2%	91.3%
MDI-CNN	99.4%	93.4%	87.1%	93.7%	95.0%	88.9%	93.1%
组合分析法	99.7%	95.7%	98.5%	96.1%	89.9%	99.3%	96.5%