Research on fault diagnosis of diesel engine based on PCA-RBF neural network

被引：3

作者：

Zhang, Meifeng ^{[1
,2
]}

Li, Yongxin ^{[1
]}

Cai, Jianwen ^{[2
]}

Chen, Fuhao ^{[2
]}

Miao, Xin ^{[2
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Mech Engn, 200 Xiaolingwei St, Nanjing, Jiangsu, Peoples R China

[2] Changzhou Inst Technol, Sch Elect & Optoelect Engn, 666 Liaohe St, Changzhou, Jiangsu, Peoples R China

来源：

MODERN PHYSICS LETTERS B | 2018年 / 32卷 / 34-36期

关键词：

Principal component analysis; diesel engine; fault diagnosis; RBF neural network;

D O I：

10.1142/S0217984918400997

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

In order to improve the fault diagnosis rate and efficiency of diesel engine, the PCA-RBF neural network as a new algorithm was constructed by combing the character extraction ability of PCA with the nonlinear approximation ability of RBF neural network. Firstly, eight factors which affected the fault types of diesel engine were analyzed and three principal components were extracted by PCA. Secondly, the data obtained from the three principal components were taken as the input of RBF neural network which was trained and tested. Finally, the PCA-RBF neural network was verified through simulation. The simulation results show that the network has fewer training steps, less training and higher training accuracy.

引用

页数：7