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Sensors 2016, 16(9), 1482; doi:10.3390/s16091482

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

1
Department of Mechanical Engineering, Academy of Armored Forces Engineering, Beijing 100072, China
2
Beijing Special Vehicle Research Institute, Beijing 100072, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gangbing Song
Received: 24 May 2016 / Revised: 1 September 2016 / Accepted: 3 September 2016 / Published: 13 September 2016
(This article belongs to the Section Physical Sensors)

Abstract

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. View Full-Text
Keywords: correlated kurtosis; redundant second generation wavelet package transform; kurtogram; weak fault diagnosis correlated kurtosis; redundant second generation wavelet package transform; kurtogram; weak fault diagnosis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, X.; Feng, F.; Zhang, B. Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram. Sensors 2016, 16, 1482.

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