Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis

The presence of periodic impulses in vibration signals is a typical symptom of localized faults of rotating machinery. It is of great significance to study how to effectively extract the periodic impulses in vibration signals for realizing the fault diagnosis of rotating machinery. Variational mode decomposition (VMD) provides a feasible tool for non-stationary signal analysis. However, the reasonable selection of algorithm parameters and under- or over-decomposition problem in VMD hinder its application in engineering signals processing to some extent. Therefore, a new periodic impulses extraction method based on improved adaptive VMD and adaptive sparse code shrinkage denoising is proposed for the fault diagnosis of rotating machinery. The method can adaptively determine the mode number and the penalty factor depending on different signals. Meanwhile, the decomposition results are clustered and combined by using the spectrum overlap coefficient and kurtosis index to eliminate the over decomposition phenomenon and realize the effective extraction of the periodic impulses. The adaptive sparse code shrinkage algorithm is developed to denoise the mode component containing the periodic impulses, further highlighting the impulses and improving the accuracy of fault identification. Simulation data and real data acquired from rolling bearing and gearbox are adopted to verify the effectiveness and superiority of the proposed method compared with other methods.