An Adaptive Spectral Kurtosis Method and its Application to Fault Detection of Rolling Element Bearings

The rolling element bearing is easy to be malfunctioning due to the harsh operation. When a fault exists in the bearing, it can generate the periodical or quasi-periodical impulses, which are important features for the bearing fault detection. These impulses may be submerged in the background noise...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2020-03, Vol.69 (3), p.739-750
Main Authors: Hu, Yue, Bao, Wenjie, Tu, Xiaotong, Li, Fucai, Li, Ke
Format: Article
Language:English
Subjects:
Background noise
Decomposition
Fault detection
Fault diagnosis
Feature extraction
Frequency measurement
Impulses
Kurtosis
morphological filter
Resonant frequency
Roller bearings
Rolling bearings
rolling element bearing
Spectra
spectral kurtosis
Wavelet transforms
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Summary:The rolling element bearing is easy to be malfunctioning due to the harsh operation. When a fault exists in the bearing, it can generate the periodical or quasi-periodical impulses, which are important features for the bearing fault detection. These impulses may be submerged in the background noise and interferences of other unrelated components. The spectral kurtosis, and its fast realization, fast kurtogram, have been widely used for the bearing fault diagnosis by extracting the impulsive feature. However, the performance is weakened due to its fixed decomposition scheme and prior information of the bearing faults. A new and adaptive spectral kurtosis method is proposed in this paper. This method is free from parameter selection. Different from the fast kurtogram, the decomposition scheme of the proposed method is flexible and adaptive. The effectiveness of the proposed method is verified by the simulation and the experiment. Both results show that the proposed method can effectively extract the bearing fault features.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2019.2905022