Optimization of Ramanujan Subspace Periodic and Its Application in Identifying Industrial Bearing Fault Features

Abstract

Due to the complex working conditions, it is difficult to accurately extract the periodic pulse component of the rolling bearing vibration signal. The Ramanujan subspace (RS) theory has a good ability to extract periodic components (PCs). However, the theory divides signals by fixed period length, which means that the effect of extracting quasi-periodic signals is poor. Therefore, this article proposes an optimized periodic mode decomposition (OPMD) method to improve the defect and apply it to bearing fault diagnosis. First, the window length of the intercepted data segment is determined by periodicity measurement. Then, the pulse phase corresponding to each data segment is detected by correlation to divide the data segment, and cubic spline interpolation is used for phase compensation. Finally, the periodic pulse is extracted from the reconstructed signal. Simulation and experimental results show that compared with the existing RS methods, OPMD can extract better periodic pulses, reduce the impact of quasi-periodic signals on the extraction effect, and improve the adaptability of the algorithm.