Bearing fault detection based on L-kurtosis

LIU Shaopeng; HOU Shumin

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Bearing fault detection based on L-kurtosis

LIU Shaopeng 1,HOU Shumin 2 * #

1.College of Mechanical Engineering and Automation, Wuhan University of Science and Technology, WuHan 430081

2.College of Mechanical Engineering and Automation, Wuhan University of Science and Technology, Wuhan, 430081

*Correspondence author

#Submitted by

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Funding: the Doctoral Foundation for Young Teachers in the Higher Education Institutions of Ministry of Education （No.Grant NO: 20104219120002）

Opened online:21 May 2013

Accepted by: none

Citation: LIU Shaopeng,HOU Shumin.Bearing fault detection based on L-kurtosis[OL]. [21 May 2013] http://en.paper.edu.cn/en_releasepaper/content/4543183

Kurtosis has been widely used to measure peakedness and to detect machine faults featuring impulsive signals. However, the kurtosis metric is susceptible to outliers caused by random events. One or a few outliers in the signal can lead to large increase in the kurtosis value. Such increase in the kurtosis value will propagate and further escalate afterwards, which can mislead the diagnosis process. This paper reports an L-kurtosis approach to impulsive fault feature detection for rotating machines. The L-kurtosis can effectively capture impulses but is robust to outliers. The L-kurtosis values of the Gaussian noise and the sinusoidal interfering signal are unique and can be used to discern the target fault signature, noise and unwanted interfering signal components. Both simulation and experimental analyses have demonstrated that the L-kurtosis measure can identify the effective frequency band to obtain the preferred envelope spectrum for bearing fault detection and is more robust than the conventional kurtosis metric in handling data with outliers.

Keywords:bearing fault diagnosis; kurtosis; L-kurtosis

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