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

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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.

Original languageEnglish
JournalJournal of Vibroengineering
Issue number7
Pages (from-to)5036-5046
Number of pages11
Publication statusPublished - 15.11.2017
Event1st World Congress on Condition Monitoring 2017 - WCCM 2017 - ILEC Conference Centre, London, United Kingdom
Duration: 13.06.201716.06.2017
Conference number: 1

Bibliographical 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).

    Research areas

  • Engineering - Fault detection, Condition monitoring, Higher order statistics, Machinery, Signal processing, Vibration analysis, Fault diagnosis, rotating machinery, multi-fault, EEMD, spectral kurtosis