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The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in mechanical system. The Fourier spectrum of complex Morlet wavelet is real, which the Fourier spectrum has no complex phase, the complex Morlet wavelet does not affect the phase of...
The training procedures of RBF neural network are faster than BP neural network and it has the global optimal ability. However, a key problem by using the RBF neural network approach is about how to choose the optimal the parameters of RBF neural network. Particle swarm optimization is introduced to select the parameters of RBF neural network. In the paper, particle swarm optimization and RBF neural...
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 rolling element bearings. This approach...
Rolling element bearings vibrations are random cyclostationary signals which are a combination of periodic and random processes due to the machine's rotation cycle and interaction with the real world. The combinations of such components are best considered as cyclostationary. This paper discusses which second order cyclostationary statistics should be used for fault diagnosis of bearing. The second...
In order to overcome the shortcomings of the traditional envelope analysis in which manually specifying a resonant frequency band is required, a novel approach based on the ensemble empirical mode decomposition (EEMD) and envelope spectrum is proposed for detecting Localized defects in rolling bearings. This approach can extract the characteristic frequencies related to the defect from the resonant...
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