Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance

Gang Yu; Mang Gao; Lulu Zhao; Yingying Zhu

doi:10.1145/3351917.3351957

Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance

Gang Yu^*
, Mang Gao
, Lulu Zhao
, Yingying Zhu

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

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

Abstract

Aiming at the problem of extracting the fault features of the rotating machinery under the situation of the mixed faults and low signal-to-noise ratio (SNR), a method of fault feature extraction based onindependent component analysis with reference (ICA-R) and stochastic resonance (SR) algorithm is proposed. Firstly, the improved fault signal pre-processing is carried out by using the improved ICA-R, and the expected fault signal is extracted. Then, combining the time-domain analysis method with the artificial bee colony algorithm, the scale adaptive SR algorithm is employed to further extract fault features. The experimental results show that the proposed method is effective in diagnosing the mixed faults of rotating machinery.

Original language	English
Title of host publication	Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019
Editors	Fumin Zhang
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450371865
DOIs	https://doi.org/10.1145/3351917.3351957
State	Published - 19 Jul 2019
Externally published	Yes
Event	4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019 - Shenzhen, China Duration: 19 Jul 2019 → 21 Jul 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019
Country/Territory	China
City	Shenzhen
Period	19/07/19 → 21/07/19

Keywords

Fault feature extraction
ICA-R
Mixed faults
Stochastic resonance

Access to Document

10.1145/3351917.3351957

Cite this

Yu, G., Gao, M., Zhao, L., & Zhu, Y. (2019). Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance. In F. Zhang (Ed.), Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019 Article a6 (ACM International Conference Proceeding Series). Association for Computing Machinery . https://doi.org/10.1145/3351917.3351957

@inproceedings{b96cef9df69a48ebacf75a3aafd6185c,

title = "Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance",

abstract = "Aiming at the problem of extracting the fault features of the rotating machinery under the situation of the mixed faults and low signal-to-noise ratio (SNR), a method of fault feature extraction based onindependent component analysis with reference (ICA-R) and stochastic resonance (SR) algorithm is proposed. Firstly, the improved fault signal pre-processing is carried out by using the improved ICA-R, and the expected fault signal is extracted. Then, combining the time-domain analysis method with the artificial bee colony algorithm, the scale adaptive SR algorithm is employed to further extract fault features. The experimental results show that the proposed method is effective in diagnosing the mixed faults of rotating machinery.",

keywords = "Fault feature extraction, ICA-R, Mixed faults, Stochastic resonance",

author = "Gang Yu and Mang Gao and Lulu Zhao and Yingying Zhu",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019 ; Conference date: 19-07-2019 Through 21-07-2019",

year = "2019",

month = jul,

day = "19",

doi = "10.1145/3351917.3351957",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

editor = "Fumin Zhang",

booktitle = "Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019",

address = "美国",

}

Yu, G, Gao, M, Zhao, L & Zhu, Y 2019, Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance. in F Zhang (ed.), Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019., a6, ACM International Conference Proceeding Series, Association for Computing Machinery , 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019, Shenzhen, China, 19/07/19. https://doi.org/10.1145/3351917.3351957

Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance. / Yu, Gang; Gao, Mang; Zhao, Lulu et al.
Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019. ed. / Fumin Zhang. Association for Computing Machinery , 2019. a6 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance

AU - Yu, Gang

AU - Gao, Mang

AU - Zhao, Lulu

AU - Zhu, Yingying

PY - 2019/7/19

Y1 - 2019/7/19

N2 - Aiming at the problem of extracting the fault features of the rotating machinery under the situation of the mixed faults and low signal-to-noise ratio (SNR), a method of fault feature extraction based onindependent component analysis with reference (ICA-R) and stochastic resonance (SR) algorithm is proposed. Firstly, the improved fault signal pre-processing is carried out by using the improved ICA-R, and the expected fault signal is extracted. Then, combining the time-domain analysis method with the artificial bee colony algorithm, the scale adaptive SR algorithm is employed to further extract fault features. The experimental results show that the proposed method is effective in diagnosing the mixed faults of rotating machinery.

AB - Aiming at the problem of extracting the fault features of the rotating machinery under the situation of the mixed faults and low signal-to-noise ratio (SNR), a method of fault feature extraction based onindependent component analysis with reference (ICA-R) and stochastic resonance (SR) algorithm is proposed. Firstly, the improved fault signal pre-processing is carried out by using the improved ICA-R, and the expected fault signal is extracted. Then, combining the time-domain analysis method with the artificial bee colony algorithm, the scale adaptive SR algorithm is employed to further extract fault features. The experimental results show that the proposed method is effective in diagnosing the mixed faults of rotating machinery.

KW - Fault feature extraction

KW - ICA-R

KW - Mixed faults

KW - Stochastic resonance

UR - https://www.scopus.com/pages/publications/85073202908

U2 - 10.1145/3351917.3351957

DO - 10.1145/3351917.3351957

M3 - 会议稿件

AN - SCOPUS:85073202908

T3 - ACM International Conference Proceeding Series

BT - Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019

A2 - Zhang, Fumin

PB - Association for Computing Machinery

T2 - 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019

Y2 - 19 July 2019 through 21 July 2019

ER -

Yu G, Gao M, Zhao L, Zhu Y. Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance. In Zhang F, editor, Proceedings - 2019 4th International Conference on Automation, Control and Robotics Engineering, CACRE 2019. Association for Computing Machinery . 2019. a6. (ACM International Conference Proceeding Series). doi: 10.1145/3351917.3351957

Feature extraction of mixed faults of rotating machinery based on ICA-R and stochastic resonance

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