Impulse Feature Extraction of Bearing Faults Based on Convolutive Nonnegative Matrix Factorization

The detection of bearing faults is of great significance for the stable operation of rotating machinery. Apart from detection by analysing the vibration response, another powerful strategy is to extract the periodic impulse excitation directly induced by faults, which efficiently eliminates the influence of the transmission path and noise. Typical methods of this strategy include maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution (MOMEDA). However, these deconvolution methods based on maximizing a certain measurement index are still insufficient at finding the correct fault period directly because of the interference of noise components. To effectively extract the periodic impulse excitation from the vibration response, a new impulse feature extraction method from the vibration spectrogram based on convex hull convolutive nonnegative matrix factorization (CH-CNMF) is proposed. As the spectrogram intuitively reveals the time and frequency information of the impulse response generated by the fault excitation, according to the decomposition characteristics of CH-CNMF, the time-frequency structure of the impulse response is represented by the basis tensor, while the weight matrix corresponds to the impulse excitation. Meanwhile, autocorrelation is adopted to enhance the periodic impulse excitation. Finally, based on power spectral entropy and first-order correlated kurtosis, the optimal periodic pulse can be selected from the autocorrelation curves of the weight matrix. Both numerical simulation and experimental verifications on bearings indicate that the proposed method can eliminate the influence of random shock excitations and directly attain the periodic impulse for the source of the bearing fault, and that its extraction effectiveness outperforms MOMEDA.