Mechanical impact feature extraction method based on nonlinear manifold learning of continuous wavelet coefficients

To acquire an impact component aroused by mechanical fault,a novel feature extraction method based on nonlinear manifold learning of continuous wavelet coefficients was put forward.Firstly,the wavelet entropy method was adopted to optimize the Morlet wavelet shape factor in order to match with the impact components to obtain the optimal continuous wavelet coefficients.Secondly,the nonlinear manifold learning algorithm named local tangent space alignment was used to reduce the optimal wavelet coefficients matrix,and according to the principle of the maximum kurtosis index,the low-dimensional embedded vectors introduced to reflect impact failures were extracted from the global coordinate feature matrix.Finally,simulations and industrial applications showed that compared with the singular value decomposition,this approach is effective to extract not only the weak impacts with the greater kurtosis in time waveform,but also the fault feature frequencies in frequency spectrum.