Sensor Fault Diagnosis of Control System Based on CMKPCA and BP Neural Network

被引：0

作者：

Wang, Yinsong ^{[1
]}

Sun, Tianshu ^{[1
]}

Cai, Bo ^{[1
]}

Lu, Lu ^{[2
]}

Jiang, Xiongjie ^{[3
]}

Hu, Xiang ^{[3
]}

机构：

[1] North China Elect Power Univ, Baoding 071000, Peoples R China

[2] Zhejiang Energy Technol Res Inst Co Ltd, Jiaxing 311100, Peoples R China

[3] Zhejiang Energy Jiahua Power Generat Co Ltd, Jiaxing 314000, Peoples R China

来源：

2018 37TH CHINESE CONTROL CONFERENCE (CCC) | 2018年

关键词：

CMKPCA; BP neural network; fault diagnosis;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Higher levels of automation and intelligence put higher demands on fault diagnosis. BP neural network has great advantages in fault identification and classification, but its efficiency and accuracy are restricted in practical application. In this paper, Class Mean Kernel Principal Component Analysis (CMKPCA) is applied to the optimization of BP neural network. By extracting the characteristic information of the fault samples, this method highlights the main components that cause faults and at the same time achieves lossless dimensionality reduction. The processed data will reduce the complexity of the neural network and further optimize the network parameters. The training result will be compared with that only using BP neural network. Simulation shows that this method can improve the recognition rate of fault diagnosis.

引用

页码：6061 / 6066

页数：6

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