Fault Diagnosis of Gas Turbine Based on Support Vector Machine

被引：0
|
作者：
Hu, Weihong [1 ]
Liu, Jiyuan [1 ]
Cui, Jianguo [1 ]
Gao, Yang [1 ]
Cui, Bo [1 ]
Jiang, Liying [1 ]
机构：
[1] Shenyang Aerosp Univ, Sch Elect & Informat Engn, Shenyang 110136, Peoples R China
来源：
26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC) | 2014年
关键词：
Support Vector Machine; Fault Diagnosis; Vibration Signal; EEMD Singular Value Decomposition; Gas Turbine Bearing; DECOMPOSITION;
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：
In this paper, a fault diagnosis method based on support vector machine (SVM) is proposed for gas turbine bearing. Firstly, through analysis and processing of vibration signals, the singular value decomposition related EEMD technique is applied to extract feature vectors of the signals. The results are used as the input of SVM classifier model. Then, by using the SVM network intelligence, the turbine bearing operating status and fault type are determined. Experimental results show that the proposed SVM classification method with small sample can accurately and efficiently classify the working status and fault type of the gas turbine bearing, and has some engineering applications values.
引用
收藏
页码：2853 / 2856
页数：4
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