Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2015, 15(11), 29363-29377; https://doi.org/10.3390/s151129363
Received: 8 September 2015
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Revised: 10 November 2015
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Accepted: 17 November 2015
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Published: 20 November 2015
(This article belongs to the Section Physical Sensors)
The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.
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Keywords:
maximum correlated kurtosis deconvolution; spectral kurtosis; rolling element bearing; early fault diagnosis
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MDPI and ACS Style
Jia, F.; Lei, Y.; Shan, H.; Lin, J. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution. Sensors 2015, 15, 29363-29377. https://doi.org/10.3390/s151129363
AMA Style
Jia F, Lei Y, Shan H, Lin J. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution. Sensors. 2015; 15(11):29363-29377. https://doi.org/10.3390/s151129363
Chicago/Turabian StyleJia, Feng, Yaguo Lei, Hongkai Shan, and Jing Lin. 2015. "Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution" Sensors 15, no. 11: 29363-29377. https://doi.org/10.3390/s151129363
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