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 Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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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 -