Non-negative Matrix Factorization Based on Clustering and Its Application

被引：0

作者：

Li M. ^{[1
]}

Liang L. ^{[2
]}

Chen Y. ^{[2
]}

Xu G. ^{[2
]}

He K. ^{[2
]}

机构：

[1] Engineering Workshop, Xi'an Jiaotong University, Xi'an

[2] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2018年 / 29卷 / 06期

关键词：

Clustering; Feature selection; Iterative algorithm; Non-negative matrix factorization(NMF);

D O I：

10.3969/j.issn.1004-132X.2018.06.013

中图分类号：

学科分类号：

摘要：

With the increasing complexity of electromechanical system state informations, traditional feature extraction and selection methods were unable to meet the needs. According to the characteristics of conventional non-negative matrix factorization(NMF) algorithm, a NMF method for monitoring and fault diagnosis was proposed based on the clustering property of NMF. By comparing classification accuracy and iteration efficiency, an improved alternating least square iterative algorithm with sparsity and correlation constraints was selected, and the low-dimensional embedded dimension and iterative initialization method were also determined. Experimental results to UCI test datasets and fault diagnosis of Tennessee-Eastman process(TEP) systems show that this approach is more effective to extract the fault features, and enhance the failure pattern capabilities. © 2018, China Mechanical Engineering Magazine Office. All right reserved.

引用

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页码：720 / 725

页数：5

共 11 条

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