A fault feature extraction method based on sample entropy(SE)and kurtosis standard deviation(KSD)optimization of fractional variational modal decomposition(FRFT-VMD)is proposed,and a random forest(RF)classifier is combined to perform fault detection.Automatic recognition of classification.Aiming at the problem that the choice of order in the fractional Fourier transform has a greater impact on the separability of data,it is proposed to search for the minimum entropy of the sample to obtain the optimal order of the fractional order.Makes the overlapped part of the data better separated in the score domain.At the same time,the kurtosis standard deviation criterion is used to find the optimal parameters of the variational modal decomposition to make the effect of the variational modal decomposition better.The research results based on database data and measured data show that the signals extracted by this method contain more and more obvious fault characteristic frequencies,which greatly improves the fault diagnosis accuracy of rolling bearings in different states.
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