Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis

Chen Zhang; Junchao Guo; Dong Zhen; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball

doi:10.1007/978-3-030-57745-2_98

Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis

Chen Zhang, Junchao Guo, Dong Zhen, Hao Zhang, Zhanqun Shi, Fengshou Gu, Andrew Ball

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

1 Citation (Scopus)

Abstract

Considering fault impulse signals of rolling element bearings have the features of periodicity and easily to be immerged by background noise, a novel fault feature extraction method based on the wavelet packet transform (WPT) and the time-delay correlation demodulation analysis is proposed in this paper. Firstly, the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) are used as the criterion to select the optimal wavelet packet parameters to enhance the SNR of the vibration signal. Then, the denoised signal is reconstructed for further analysis. Finally, the fault features of the reconstructed signal are extracted using time-delay correlation demodulation analysis. The results show that the fault characteristic frequencies can be extracted with higher accuracy based on the simulation and experimental studies, respectively. It can be concluded that the proposed method has more effectiveness and feasibility for fault diagnosis of rolling element bearing with higher accuracy.

Original language	English
Title of host publication	Advances in Asset Management and Condition Monitoring, COMADEM 2019
Editors	Andrew Ball, Len Gelman, B.K.N. Rao
Publisher	Springer, Cham
Pages	1195-1203
Number of pages	9
Volume	166
ISBN (Electronic)	9783030577452
ISBN (Print)	9783030577445
DOIs	https://doi.org/10.1007/978-3-030-57745-2_98
Publication status	Published - 28 Aug 2020
Event	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom Duration: 3 Sep 2019 → 5 Sep 2019 Conference number: 32 http://www.comadem2019.com/ (Link to Conference Website)

Publication series

Name	Smart Innovation, Systems and Technologies
Publisher	Springer
Volume	166
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated title	COMADEM 2019
Country/Territory	United Kingdom
City	Huddersfield
Period	3/09/19 → 5/09/19
Internet address	http://www.comadem2019.com/ (Link to Conference Website)

UN SDGs

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

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Cite this

Zhang, C., Guo, J., Zhen, D., Zhang, H., Shi, Z., Gu, F., & Ball, A. (2020). Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. In A. Ball, L. Gelman, & B. K. N. Rao (Eds.), Advances in Asset Management and Condition Monitoring, COMADEM 2019 (Vol. 166, pp. 1195-1203). (Smart Innovation, Systems and Technologies; Vol. 166). Springer, Cham. https://doi.org/10.1007/978-3-030-57745-2_98

Zhang, Chen ; Guo, Junchao ; Zhen, Dong et al. / Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. Advances in Asset Management and Condition Monitoring, COMADEM 2019. editor / Andrew Ball ; Len Gelman ; B.K.N. Rao. Vol. 166 Springer, Cham, 2020. pp. 1195-1203 (Smart Innovation, Systems and Technologies).

@inproceedings{6592064fd3754214930c3b13eed3c581,

title = "Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis",

abstract = "Considering fault impulse signals of rolling element bearings have the features of periodicity and easily to be immerged by background noise, a novel fault feature extraction method based on the wavelet packet transform (WPT) and the time-delay correlation demodulation analysis is proposed in this paper. Firstly, the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) are used as the criterion to select the optimal wavelet packet parameters to enhance the SNR of the vibration signal. Then, the denoised signal is reconstructed for further analysis. Finally, the fault features of the reconstructed signal are extracted using time-delay correlation demodulation analysis. The results show that the fault characteristic frequencies can be extracted with higher accuracy based on the simulation and experimental studies, respectively. It can be concluded that the proposed method has more effectiveness and feasibility for fault diagnosis of rolling element bearing with higher accuracy.",

keywords = "Delayed correlation demodulation, Envelope analysis, Rolling element bearing, Wavelet packet",

author = "Chen Zhang and Junchao Guo and Dong Zhen and Hao Zhang and Zhanqun Shi and Fengshou Gu and Andrew Ball",

year = "2020",

month = aug,

day = "28",

doi = "10.1007/978-3-030-57745-2_98",

language = "English",

isbn = "9783030577445",

volume = "166",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer, Cham",

pages = "1195--1203",

editor = "Andrew Ball and Len Gelman and B.K.N. Rao",

booktitle = "Advances in Asset Management and Condition Monitoring, COMADEM 2019",

address = "Switzerland",

note = "32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, COMADEM 2019 ; Conference date: 03-09-2019 Through 05-09-2019",

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Zhang, C, Guo, J, Zhen, D, Zhang, H, Shi, Z, Gu, F & Ball, A 2020, Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. in A Ball, L Gelman & BKN Rao (eds), Advances in Asset Management and Condition Monitoring, COMADEM 2019. vol. 166, Smart Innovation, Systems and Technologies, vol. 166, Springer, Cham, pp. 1195-1203, 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, Huddersfield, United Kingdom, 3/09/19. https://doi.org/10.1007/978-3-030-57745-2_98

Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. / Zhang, Chen; Guo, Junchao; Zhen, Dong et al.

Advances in Asset Management and Condition Monitoring, COMADEM 2019. ed. / Andrew Ball; Len Gelman; B.K.N. Rao. Vol. 166 Springer, Cham, 2020. p. 1195-1203 (Smart Innovation, Systems and Technologies; Vol. 166).

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

TY - GEN

T1 - Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis

AU - Zhang, Chen

AU - Guo, Junchao

AU - Zhen, Dong

AU - Zhang, Hao

AU - Shi, Zhanqun

AU - Gu, Fengshou

AU - Ball, Andrew

N1 - Conference code: 32

PY - 2020/8/28

Y1 - 2020/8/28

N2 - Considering fault impulse signals of rolling element bearings have the features of periodicity and easily to be immerged by background noise, a novel fault feature extraction method based on the wavelet packet transform (WPT) and the time-delay correlation demodulation analysis is proposed in this paper. Firstly, the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) are used as the criterion to select the optimal wavelet packet parameters to enhance the SNR of the vibration signal. Then, the denoised signal is reconstructed for further analysis. Finally, the fault features of the reconstructed signal are extracted using time-delay correlation demodulation analysis. The results show that the fault characteristic frequencies can be extracted with higher accuracy based on the simulation and experimental studies, respectively. It can be concluded that the proposed method has more effectiveness and feasibility for fault diagnosis of rolling element bearing with higher accuracy.

AB - Considering fault impulse signals of rolling element bearings have the features of periodicity and easily to be immerged by background noise, a novel fault feature extraction method based on the wavelet packet transform (WPT) and the time-delay correlation demodulation analysis is proposed in this paper. Firstly, the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) are used as the criterion to select the optimal wavelet packet parameters to enhance the SNR of the vibration signal. Then, the denoised signal is reconstructed for further analysis. Finally, the fault features of the reconstructed signal are extracted using time-delay correlation demodulation analysis. The results show that the fault characteristic frequencies can be extracted with higher accuracy based on the simulation and experimental studies, respectively. It can be concluded that the proposed method has more effectiveness and feasibility for fault diagnosis of rolling element bearing with higher accuracy.

KW - Delayed correlation demodulation

KW - Envelope analysis

KW - Rolling element bearing

KW - Wavelet packet

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UR - https://www.springer.com/gb/book/9783030577445

U2 - 10.1007/978-3-030-57745-2_98

DO - 10.1007/978-3-030-57745-2_98

M3 - Conference contribution

AN - SCOPUS:85091278583

SN - 9783030577445

VL - 166

T3 - Smart Innovation, Systems and Technologies

SP - 1195

EP - 1203

BT - Advances in Asset Management and Condition Monitoring, COMADEM 2019

A2 - Ball, Andrew

A2 - Gelman, Len

A2 - Rao, B.K.N.

PB - Springer, Cham

T2 - 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference

Y2 - 3 September 2019 through 5 September 2019

ER -

Zhang C, Guo J, Zhen D, Zhang H, Shi Z, Gu F et al. Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. In Ball A, Gelman L, Rao BKN, editors, Advances in Asset Management and Condition Monitoring, COMADEM 2019. Vol. 166. Springer, Cham. 2020. p. 1195-1203. (Smart Innovation, Systems and Technologies). Epub 2020 Aug 28. doi: 10.1007/978-3-030-57745-2_98

Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis

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