Time-frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery. Therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals is conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number and second penalty factor is proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) is established. A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search (HS) method. A multi-source shock simulation signal is established, and actual data on the valve fault are obtained through diesel engine fault experiments. The fault recognition rate of the intake and exhaust valve clearance is above 90% and the extraction accuracy of the shock start position is improved by 10 degrees.