Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
Volume 236, Issue 4, Pages 1994-2008Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954406220906245
Keywords
Impulsive wavelet; correlation filtering; adaptive filter; Shannon wavelet; fault diagnosis
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In rotation machinery fault diagnosis, useful fault features are often overshadowed by noise and other interference factors. This study proposes a fast algorithm based on an adaptive impulsive wavelet to denoise vibration signals and extract fault characteristic frequency by identifying modeling parameters.
The useful fault features applied for the fault diagnosis are usually overwhelmed by noise and other interference factors in rotation machinery. The impulses masked in vibration signals can represent the faults of gears or bearings in a gearbox. The key to finding impulsive components is to identify the modeling parameters (such as damping ratio, central frequency) of a transient (Morlet wavelet, Laplace wavelet), which can be used as an adaptive filter to denoise the vibration signal. However, its engineering application is limited by the time-consuming computation. In order to tackle this issue, a fast algorithm based on an adaptive impulsive wavelet is proposed to filter the fault signal so that the fault characteristic frequency can be identified. Firstly, a correlation coefficient maximum criterion is employed to find one of the optimal parameters of the impulsive wavelet. Then, the other parameter is optimized by the minimum Shannon wavelet entropy criterion. Finally, the impulsive wavelet filter with optimal parameters is applied to extract the fault characteristic frequency. Simulation signals are applied to verify the efficiency of the proposed approach, and comparison analysis is conducted as well. Further, the proposed method is applied to detect the gear fault of a gearbox. The experimental results show that the proposed method is effective with high efficiency.
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