Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM

Hongmei Yan; Huina Mu; Xiaojian Yi; Yuanyuan Yang; Guangliang Chen

doi:10.1109/PHM-Paris.2019.00011

Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM

Hongmei Yan, Huina Mu, Xiaojian Yi^*, Yuanyuan Yang, Guangliang Chen

^*Corresponding author for this work

School of Mechatronical Engineering

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

2 Citations (Scopus)

Abstract

A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.

Original language	English
Title of host publication	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Editors	Chuan Li, Jose Valente de Oliveira, Ping Ding, Ping Ding, Diego Cabrera
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	18-23
Number of pages	6
ISBN (Electronic)	9781728103297
DOIs	https://doi.org/10.1109/PHM-Paris.2019.00011
Publication status	Published - May 2019
Event	2019 Prognostics and System Health Management Conference, PHM-Paris 2019 - Paris, France Duration: 2 May 2019 → 5 May 2019

Publication series

Name	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

Conference

Conference	2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Country/Territory	France
City	Paris
Period	2/05/19 → 5/05/19

Keywords

fault diagnosis
least squares support vector machine
particle swarm optimization
rolling bearing
wavelet packet decomposition

Access to Document

10.1109/PHM-Paris.2019.00011

Cite this

Yan, H., Mu, H., Yi, X., Yang, Y., & Chen, G. (2019). Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM. In C. Li, J. V. de Oliveira, P. Ding, P. Ding, & D. Cabrera (Eds.), Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 (pp. 18-23). Article 8756408 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM-Paris.2019.00011

Yan, Hongmei ; Mu, Huina ; Yi, Xiaojian et al. / Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM. Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. editor / Chuan Li ; Jose Valente de Oliveira ; Ping Ding ; Ping Ding ; Diego Cabrera. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 18-23 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019).

@inproceedings{699c72360af647e68f5ebc75ddb19b51,

title = "Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM",

abstract = "A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.",

keywords = "fault diagnosis, least squares support vector machine, particle swarm optimization, rolling bearing, wavelet packet decomposition",

author = "Hongmei Yan and Huina Mu and Xiaojian Yi and Yuanyuan Yang and Guangliang Chen",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 ; Conference date: 02-05-2019 Through 05-05-2019",

year = "2019",

month = may,

doi = "10.1109/PHM-Paris.2019.00011",

language = "English",

series = "Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019",

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

pages = "18--23",

editor = "Chuan Li and {de Oliveira}, {Jose Valente} and Ping Ding and Ping Ding and Diego Cabrera",

booktitle = "Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019",

address = "United States",

}

Yan, H, Mu, H , Yi, X, Yang, Y & Chen, G 2019, Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM. in C Li, JV de Oliveira, P Ding, P Ding & D Cabrera (eds), Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019., 8756408, Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019, Institute of Electrical and Electronics Engineers Inc., pp. 18-23, 2019 Prognostics and System Health Management Conference, PHM-Paris 2019, Paris, France, 2/05/19. https://doi.org/10.1109/PHM-Paris.2019.00011

Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM. / Yan, Hongmei; Mu, Huina ; Yi, Xiaojian et al.
Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. ed. / Chuan Li; Jose Valente de Oliveira; Ping Ding; Ping Ding; Diego Cabrera. Institute of Electrical and Electronics Engineers Inc., 2019. p. 18-23 8756408 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019).

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

TY - GEN

T1 - Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM

AU - Yan, Hongmei

AU - Mu, Huina

AU - Yi, Xiaojian

AU - Yang, Yuanyuan

AU - Chen, Guangliang

PY - 2019/5

Y1 - 2019/5

N2 - A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.

AB - A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.

KW - fault diagnosis

KW - least squares support vector machine

KW - particle swarm optimization

KW - rolling bearing

KW - wavelet packet decomposition

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U2 - 10.1109/PHM-Paris.2019.00011

DO - 10.1109/PHM-Paris.2019.00011

M3 - Conference contribution

AN - SCOPUS:85070521511

T3 - Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

SP - 18

EP - 23

BT - Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

A2 - Li, Chuan

A2 - de Oliveira, Jose Valente

A2 - Ding, Ping

A2 - Cabrera, Diego

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

Y2 - 2 May 2019 through 5 May 2019

ER -

Yan H, Mu H , Yi X, Yang Y, Chen G. Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM. In Li C, de Oliveira JV, Ding P, Ding P, Cabrera D, editors, Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 18-23. 8756408. (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019). doi: 10.1109/PHM-Paris.2019.00011

Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM

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