Feature Extraction for Fault Diagnosis Utilizing Supervised Nonnegative Matrix Factorization Combined Statistical Model

被引：0
|
作者：
Chen, Yuanming [1 ]
Li, Maolin [2 ]
Liang, Lin [1 ,3 ]
Xu, Guanghua [3 ,4 ]
Gao, Huizhong [1 ]
机构：
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, Engn Workshop, Xian, Peoples R China
[3] Xi An Jiao Tong Univ, Key Lab, Educ Minist Modern Design & Rotor Bearing Syst, Xian, Peoples R China
[4] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Peoples R China
来源：
2016 9TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2016) | 2016年
基金：
中国国家自然科学基金;
关键词：
Nonnegative matrix factorization; Fault diagnosis; Source seperation; Supervised learning; SOURCE SEPARATION; PRIORS;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
A new method for separating the feature automatically with supervised nonnegative matrix factorization (NMF) is proposed for fault diagnosis. Because of the shortage of lacking prior knowledge in existed NMF, a supervised NMF combined statistical model for fault diagnosis is proposed. The basis matrix achieved in training stage is treated as the sources' features. Besides, Gaussian Mixture Model is introduced to estimate the distributions of the base vectors and then keep them as the prior knowledge. The rotation machinery with faults was used to evaluate the performance of the proposed method. The results show that the proposed method has a good source separation capability. Its performance is better than NMF.
引用
收藏
页码：1188 / 1193
页数：6
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