Bearing Fault Diagnosis Based on Laplace Wavelet Transform

Hui Li, Yingjie Yin

Abstract


The roller bearing characteristic frequencies contain very little energy, and are usually overwhelmed by noise and higher levels of structural vibrations. Therefore, envelope spectrum analysis is widely used to detection bearing localized fault. In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the Laplace wavelet transform and envelope spectrum is proposed for detection and diagnosis defects in roller element bearings. The basic principle is introduced in detail. Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the Laplace wavelet transform. Then the envelope spectrum is applied to the selected daughter wavelet that stands for the bearing faults. The experimental results show that Laplace wavelet can extract the impulse response from strong noise signals and can effectively diagnose the faults of bearing.

 

DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1653


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