Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD

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Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD. / Gong, Xiaoyun; Du, Wenliao; Georgiadis, Anthimos et al.
in: Journal of Vibroengineering, Jahrgang 19, Nr. 7, 15.11.2017, S. 5036-5046.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{c1c80808462749baa73b5165ec561579,
title = "Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD",
abstract = "Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery.",
keywords = "Engineering, Fault detection, Condition monitoring, Higher order statistics, Machinery, Signal processing, Vibration analysis, Fault diagnosis, rotating machinery, multi-fault, EEMD, spectral kurtosis",
author = "Xiaoyun Gong and Wenliao Du and Anthimos Georgiadis and Baowei Zhao",
note = "This paper is supported by National Natural Science Foundation of China, (No. 51405453), by Program for Science & Technology Innovation Talents in Universities of Henan Province (No. 17HASTIT028) and by Key Scientific Research Projects of Henan Province(No. 16A460012).; 1st World Congress on Condition Monitoring 2017 - WCCM 2017, WCCM2017 ; Conference date: 13-06-2017 Through 16-06-2017",
year = "2017",
month = nov,
day = "15",
doi = "10.21595/jve.2017.18671",
language = "English",
volume = "19",
pages = "5036--5046",
journal = "Journal of Vibroengineering",
issn = "1392-8716",
publisher = "Public Institution Vibromechanika",
number = "7",
url = "https://intiscm.org/events.php?udpview=show-conference&src=events&sid=15",

}

RIS

TY - JOUR

T1 - Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD

AU - Gong, Xiaoyun

AU - Du, Wenliao

AU - Georgiadis, Anthimos

AU - Zhao, Baowei

N1 - Conference code: 1

PY - 2017/11/15

Y1 - 2017/11/15

N2 - Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery.

AB - Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery.

KW - Engineering

KW - Fault detection

KW - Condition monitoring

KW - Higher order statistics

KW - Machinery

KW - Signal processing

KW - Vibration analysis

KW - Fault diagnosis

KW - rotating machinery

KW - multi-fault

KW - EEMD

KW - spectral kurtosis

UR - http://www.scopus.com/inward/record.url?scp=85029444199&partnerID=8YFLogxK

UR - http://www.jvejournals.com/Vibro/journal/JVE-19-7.html

U2 - 10.21595/jve.2017.18671

DO - 10.21595/jve.2017.18671

M3 - Journal articles

AN - SCOPUS:85029444199

VL - 19

SP - 5036

EP - 5046

JO - Journal of Vibroengineering

JF - Journal of Vibroengineering

SN - 1392-8716

IS - 7

T2 - 1st World Congress on Condition Monitoring 2017 - WCCM 2017

Y2 - 13 June 2017 through 16 June 2017

ER -

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