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A novel approach to fault feature extraction using Weibull distribution parameters is proposed. After the original signal of bearing vibration is modeled as the Weibull distribution, its scale parameter is extracted as a new feature vector for the bearing running state. The tests results of fault diagnosis of the rolling bearing verify that this new feature can catch the regularity of changes in the...
A feature extraction method using joint empirical mode decomposition (EMD) and correlation analysis is proposed. The vibration signal of a rolling bearing is decomposed into a number of IMF (Intrinsic Mode Function) components by using EMD method, after adopting large numbers of correlation analysis of the IMF components and vibration signal is decomposed, we found that the correlation coefficients...
In this paper, fault diagnosis approach to rolling bearing based on wavelet packet transform and support vector machine is proposed. At first, feature vectors are extracted from the non-stationary vibration signals by means of wavelet packet transform. Then support vector machine algorithm is used to fault identification and classification of rolling bearing. The experiments show that, as for limited...
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