Study of Data Fusion Method for Fault Diagnosis Based On FDR Feature Selection Algorithm and HMM/SVM Model

被引：0

作者：

Li, Sheng ^{[1
]}

Zhang, Chun-liang ^{[2
]}

Hu, Liangbin ^{[1
]}

机构：

[1] Univ South China, Sch Mech Engn, Hengyang 421001, Peoples R China

[2] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou, Guangdong 510006, Peoples R China

来源：

MANUFACTURING ENGINEERING AND AUTOMATION II, PTS 1-3 | 2012年 / 591-593卷

关键词：

Fault diagnosis; Support Vector Machine; Hidden Markov model; Data Fusion; Fisher's Discriminant Ratio;

D O I：

10.4028/www.scientific.net/AMR.591-593.2046

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To effectively avoid the loss of useful infomation, in this paper, we extract feature information from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then for the multi-dimensional feature extraction is prone to the problem of "dime nsion disaster", introduce the principles of FDR in data mining to determine the classification ability of each individual feature, and introduce the cross correlation' coefficient to solve the problem that dealing with individual feature negleots the interrelationship between the features, and construct a new feature level data fusion algorithm. Finally, According to the characteristics of the HMM (Hidden Markov model), SVM (Support Vector Machine) and its hybrid model, we construct a new decision-level data fusion model.

引用

页码：2046 / +

页数：3