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In order to overcome the shortcomings in the traditional envelope analysis, the methods based on the wavelet transform and morphological filters are introduced for detecting defects in rolling element bearings. The method based on the analytic wavelet can be applied to non-linear and non-stationary bearing vibration signals. The method based on the morphological filters has the advantage of less computation,...
De-noising and extraction of the weak signal are very important to mechanical fault detection in which case signals often have very low signal-to-noise ratio (SNR). In this paper, a denoising method based on the optimal Morlet wavelet is applied to feature extraction for mechanical vibration signals. The wavelet shape parameters are optimized based on kurtosis maximization criteria. The effectiveness...
A new method of bearings fault diagnosis based on the optimal impulse response wavelet is presented. The construction and optimization for mother wavelet are introduced. Theoretical background of the analytic wavelet transform is discussed in this paper. Experiment has confirmed that the proposed method is effective in detecting.
A new method of roller bearings fault diagnosis based on least squares support vector machines (LS-SVM) was presented. Feature selection method based on simulated annealing (SA) algorithm was discussed in this paper. LS-SVM classifier was constructed for bearing faults. Compared with the Artificial Neural Network based method, the LS-SVM based method possessed desirable advantages. Experiment shows...
A new method of fault diagnosis based on support vector machine (SVM) and feature evaluation is presented. Feature evaluation based on class separability criterion is discussed in this paper. A multi-fault SVM classifier based on binary classifier is constructed for bearing faults. Compared with the artificial neural network based method, the SVM based method has desirable advantages. Experiment shows...
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