Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance

Chao Zhang; Haoran Duan; Jianguo Wang; Fengshou Gu; Biao Zhang

doi:10.1007/978-3-030-75793-9_51

Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance

Chao Zhang, Haoran Duan, Jianguo Wang, Fengshou Gu, Biao Zhang

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

Abstract

Large mechanical equipment is subject to periodic vibrations in harsh operating environments for a long time. Bearing failure produces violent changes in the behaviors of large rotating machinery as the safety and reliability of the scene. Therefore, it is especially significant to effectively identify the early failure of the bearing. Since the signal of bearing fault belongs to a low-frequency weak fault, it is hard to classify the characteristic frequency. To solve this shortcoming, a new stochastic resonance model MWS, which is established on the joint monostable function and Woods-Saxon, is presented and used to strengthen the characteristics frequency, also the bearing fault characteristic frequency is gained. The simulation analysis shows that the proposed method can effectively extract the weak fault characteristics which are submerged in the noisy environment, and the bearing test proves that the MWS effectively extract the weak feature information in the bearing fault signal.

Original language	English
Title of host publication	Proceedings of IncoME-V & CEPE Net-2020
Subtitle of host publication	Condition Monitoring, Plant Maintenance and Reliability
Editors	Dong Zhen, Dong Wang, Tianyang Wang, Hongjun Wang, Baoshan Huang, Jyoti K. Sinha, Andrew David Ball
Place of Publication	Cham
Publisher	Springer Nature Switzerland AG
Pages	541-549
Number of pages	9
Volume	105
Edition	1st
ISBN (Electronic)	9783030757939
ISBN (Print)	9783030757922
DOIs	https://doi.org/10.1007/978-3-030-75793-9_51
Publication status	Published - 16 May 2021
Event	5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network - Zhuhai, China Duration: 23 Oct 2020 → 25 Oct 2020 Conference number: 5 https://link.springer.com/book/10.1007/978-3-030-75793-9#about

Publication series

Name	Mechanisms and Machine Science
Volume	105
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network
Abbreviated title	IncoME-V and CEPE Net-2020
Country/Territory	China
City	Zhuhai
Period	23/10/20 → 25/10/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-75793-9#about

UN SDGs

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

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Zhang, C., Duan, H., Wang, J., Gu, F., & Zhang, B. (2021). Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance. In D. Zhen, D. Wang, T. Wang, H. Wang, B. Huang, J. K. Sinha, & A. D. Ball (Eds.), Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability (1st ed., Vol. 105, pp. 541-549). (Mechanisms and Machine Science; Vol. 105). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-75793-9_51

Zhang, Chao ; Duan, Haoran ; Wang, Jianguo et al. / Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance. Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. editor / Dong Zhen ; Dong Wang ; Tianyang Wang ; Hongjun Wang ; Baoshan Huang ; Jyoti K. Sinha ; Andrew David Ball. Vol. 105 1st. ed. Cham : Springer Nature Switzerland AG, 2021. pp. 541-549 (Mechanisms and Machine Science).

@inproceedings{5334e6530be245a9ae32a542aff3a07a,

title = "Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance",

abstract = "Large mechanical equipment is subject to periodic vibrations in harsh operating environments for a long time. Bearing failure produces violent changes in the behaviors of large rotating machinery as the safety and reliability of the scene. Therefore, it is especially significant to effectively identify the early failure of the bearing. Since the signal of bearing fault belongs to a low-frequency weak fault, it is hard to classify the characteristic frequency. To solve this shortcoming, a new stochastic resonance model MWS, which is established on the joint monostable function and Woods-Saxon, is presented and used to strengthen the characteristics frequency, also the bearing fault characteristic frequency is gained. The simulation analysis shows that the proposed method can effectively extract the weak fault characteristics which are submerged in the noisy environment, and the bearing test proves that the MWS effectively extract the weak feature information in the bearing fault signal.",

keywords = "Bearing failure, MWS, Signal extraction, Stochastic resonance",

author = "Chao Zhang and Haoran Duan and Jianguo Wang and Fengshou Gu and Biao Zhang",

note = "Funding Information: Acknowledgments. This research is supported by the National Science Foundation of China (NSFC) (No. 51565046), Natural Science Foundation of Inner Mongolia Autonomous Region, China (No. 2018KG007), Inner Mongolia Higher Education Youth Science and Technology Leadership Talent Project (NJYT-18-A10). Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network, IncoME-V and CEPE Net-2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2021",

month = may,

day = "16",

doi = "10.1007/978-3-030-75793-9_51",

language = "English",

isbn = "9783030757922",

volume = "105",

series = "Mechanisms and Machine Science",

publisher = "Springer Nature Switzerland AG",

pages = "541--549",

editor = "Dong Zhen and Dong Wang and Tianyang Wang and Hongjun Wang and Baoshan Huang and Sinha, {Jyoti K.} and Ball, {Andrew David}",

booktitle = "Proceedings of IncoME-V & CEPE Net-2020",

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Zhang, C, Duan, H, Wang, J, Gu, F & Zhang, B 2021, Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance. in D Zhen, D Wang, T Wang, H Wang, B Huang, JK Sinha & AD Ball (eds), Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. 1st edn, vol. 105, Mechanisms and Machine Science, vol. 105, Springer Nature Switzerland AG, Cham, pp. 541-549, 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network, Zhuhai, China, 23/10/20. https://doi.org/10.1007/978-3-030-75793-9_51

Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance. / Zhang, Chao; Duan, Haoran; Wang, Jianguo et al.

Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. ed. / Dong Zhen; Dong Wang; Tianyang Wang; Hongjun Wang; Baoshan Huang; Jyoti K. Sinha; Andrew David Ball. Vol. 105 1st. ed. Cham : Springer Nature Switzerland AG, 2021. p. 541-549 (Mechanisms and Machine Science; Vol. 105).

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

TY - GEN

T1 - Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance

AU - Zhang, Chao

AU - Duan, Haoran

AU - Wang, Jianguo

AU - Gu, Fengshou

AU - Zhang, Biao

N1 - Conference code: 5

PY - 2021/5/16

Y1 - 2021/5/16

N2 - Large mechanical equipment is subject to periodic vibrations in harsh operating environments for a long time. Bearing failure produces violent changes in the behaviors of large rotating machinery as the safety and reliability of the scene. Therefore, it is especially significant to effectively identify the early failure of the bearing. Since the signal of bearing fault belongs to a low-frequency weak fault, it is hard to classify the characteristic frequency. To solve this shortcoming, a new stochastic resonance model MWS, which is established on the joint monostable function and Woods-Saxon, is presented and used to strengthen the characteristics frequency, also the bearing fault characteristic frequency is gained. The simulation analysis shows that the proposed method can effectively extract the weak fault characteristics which are submerged in the noisy environment, and the bearing test proves that the MWS effectively extract the weak feature information in the bearing fault signal.

AB - Large mechanical equipment is subject to periodic vibrations in harsh operating environments for a long time. Bearing failure produces violent changes in the behaviors of large rotating machinery as the safety and reliability of the scene. Therefore, it is especially significant to effectively identify the early failure of the bearing. Since the signal of bearing fault belongs to a low-frequency weak fault, it is hard to classify the characteristic frequency. To solve this shortcoming, a new stochastic resonance model MWS, which is established on the joint monostable function and Woods-Saxon, is presented and used to strengthen the characteristics frequency, also the bearing fault characteristic frequency is gained. The simulation analysis shows that the proposed method can effectively extract the weak fault characteristics which are submerged in the noisy environment, and the bearing test proves that the MWS effectively extract the weak feature information in the bearing fault signal.

KW - Bearing failure

KW - MWS

KW - Signal extraction

KW - Stochastic resonance

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UR - https://www.springer.com/gb/book/9783030757922

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DO - 10.1007/978-3-030-75793-9_51

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AN - SCOPUS:85106004890

SN - 9783030757922

VL - 105

T3 - Mechanisms and Machine Science

SP - 541

EP - 549

BT - Proceedings of IncoME-V & CEPE Net-2020

A2 - Zhen, Dong

A2 - Wang, Dong

A2 - Wang, Tianyang

A2 - Wang, Hongjun

A2 - Huang, Baoshan

A2 - Sinha, Jyoti K.

A2 - Ball, Andrew David

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network

Y2 - 23 October 2020 through 25 October 2020

ER -

Zhang C, Duan H, Wang J, Gu F, Zhang B. Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance. In Zhen D, Wang D, Wang T, Wang H, Huang B, Sinha JK, Ball AD, editors, Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. 1st ed. Vol. 105. Cham: Springer Nature Switzerland AG. 2021. p. 541-549. (Mechanisms and Machine Science). Epub 2021 May 16. doi: 10.1007/978-3-030-75793-9_51

Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance

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