Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization

Chengpeng Ma; Lin Liang; Yuanming Chen; Qing Zhang

doi:10.1109/ITNEC48623.2020.9084774

Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization

Chengpeng Ma
, Lin Liang
, Yuanming Chen
, Qing Zhang

School of Instrument Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

To accurately extract the feature, a new feature extraction method based on kernel nonnegative matrix factorization is proposed to extract the features of nonlinear data in feature space. The method can extract the feature by using the nonlinear high-dimensional mapping of the kernel function, and can deal with the nonlinear data. The method of feature extraction and fault diagnosis based on kernel nonnegative matrix factorization is given. The results of nonnegative matrix factorization, principal component analysis, kernel principal component analysis and kernel nonnegative matrix factorization are compared by UCI data. The application of rolling bearing in practice shows that the method is suitable for the extraction of fault features for equipment, and effectively overcomes the shortcomings of nonnegative matrix factorization and principal component analysis, and can improve the fault diagnosis performance.

Original language	English
Title of host publication	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1412-1416
Number of pages	5
ISBN (Electronic)	9781728143903
DOIs	https://doi.org/10.1109/ITNEC48623.2020.9084774
State	Published - Jun 2020
Event	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 - Chongqing, China Duration: 12 Jun 2020 → 14 Jun 2020

Publication series

Name	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Conference

Conference	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Country/Territory	China
City	Chongqing
Period	12/06/20 → 14/06/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

fault diagnosis
feature extraction
kernel function
kernel nonnegative matrix factorization

Access to Document

10.1109/ITNEC48623.2020.9084774

Cite this

Ma, C., Liang, L., Chen, Y., & Zhang, Q. (2020). Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization. In B. Xu, & K. Mou (Eds.), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 (pp. 1412-1416). Article 9084774 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC48623.2020.9084774

Ma, Chengpeng ; Liang, Lin ; Chen, Yuanming et al. / Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization. Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1412-1416 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

@inproceedings{3e89f9f660714040aa60f615f09496ce,

title = "Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization",

abstract = "To accurately extract the feature, a new feature extraction method based on kernel nonnegative matrix factorization is proposed to extract the features of nonlinear data in feature space. The method can extract the feature by using the nonlinear high-dimensional mapping of the kernel function, and can deal with the nonlinear data. The method of feature extraction and fault diagnosis based on kernel nonnegative matrix factorization is given. The results of nonnegative matrix factorization, principal component analysis, kernel principal component analysis and kernel nonnegative matrix factorization are compared by UCI data. The application of rolling bearing in practice shows that the method is suitable for the extraction of fault features for equipment, and effectively overcomes the shortcomings of nonnegative matrix factorization and principal component analysis, and can improve the fault diagnosis performance.",

keywords = "fault diagnosis, feature extraction, kernel function, kernel nonnegative matrix factorization",

author = "Chengpeng Ma and Lin Liang and Yuanming Chen and Qing Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 ; Conference date: 12-06-2020 Through 14-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ITNEC48623.2020.9084774",

language = "英语",

series = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1412--1416",

editor = "Bing Xu and Kefen Mou",

booktitle = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",

}

Ma, C, Liang, L, Chen, Y & Zhang, Q 2020, Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization. in B Xu & K Mou (eds), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020., 9084774, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1412-1416, 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Chongqing, China, 12/06/20. https://doi.org/10.1109/ITNEC48623.2020.9084774

Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization. / Ma, Chengpeng; Liang, Lin; Chen, Yuanming et al.
Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1412-1416 9084774 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization

AU - Ma, Chengpeng

AU - Liang, Lin

AU - Chen, Yuanming

AU - Zhang, Qing

PY - 2020/6

Y1 - 2020/6

N2 - To accurately extract the feature, a new feature extraction method based on kernel nonnegative matrix factorization is proposed to extract the features of nonlinear data in feature space. The method can extract the feature by using the nonlinear high-dimensional mapping of the kernel function, and can deal with the nonlinear data. The method of feature extraction and fault diagnosis based on kernel nonnegative matrix factorization is given. The results of nonnegative matrix factorization, principal component analysis, kernel principal component analysis and kernel nonnegative matrix factorization are compared by UCI data. The application of rolling bearing in practice shows that the method is suitable for the extraction of fault features for equipment, and effectively overcomes the shortcomings of nonnegative matrix factorization and principal component analysis, and can improve the fault diagnosis performance.

AB - To accurately extract the feature, a new feature extraction method based on kernel nonnegative matrix factorization is proposed to extract the features of nonlinear data in feature space. The method can extract the feature by using the nonlinear high-dimensional mapping of the kernel function, and can deal with the nonlinear data. The method of feature extraction and fault diagnosis based on kernel nonnegative matrix factorization is given. The results of nonnegative matrix factorization, principal component analysis, kernel principal component analysis and kernel nonnegative matrix factorization are compared by UCI data. The application of rolling bearing in practice shows that the method is suitable for the extraction of fault features for equipment, and effectively overcomes the shortcomings of nonnegative matrix factorization and principal component analysis, and can improve the fault diagnosis performance.

KW - fault diagnosis

KW - feature extraction

KW - kernel function

KW - kernel nonnegative matrix factorization

UR - https://www.scopus.com/pages/publications/85086277955

U2 - 10.1109/ITNEC48623.2020.9084774

DO - 10.1109/ITNEC48623.2020.9084774

M3 - 会议稿件

AN - SCOPUS:85086277955

T3 - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

SP - 1412

EP - 1416

BT - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Y2 - 12 June 2020 through 14 June 2020

ER -

Ma C, Liang L, Chen Y, Zhang Q. Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization. In Xu B, Mou K, editors, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1412-1416. 9084774. (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). doi: 10.1109/ITNEC48623.2020.9084774

Feature Extraction for Fault Diagnosis of Machine based on Kernel Nonnegative Matrix Factorization

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this