Periodic Detection Mode Decomposition and Its Application in Bearing Fault Diagnosis

In the complex background noise, it is difficult to extract the periodic pulse of the rolling bearing fault signal. In this article, a new modal decomposition method supported by the singular value ratio (SVR) spectrum is proposed to find the optimal period of bearing fault data, which can be named periodic detection mode decomposition (PDMD). To detect the intervals of periods and eliminate the interference of useless periods, the initial measurement intervals in this method are divided according to the theoretical fault periods of different fault types of bearings. In each interval, the SVR spectrum is used to detect the appropriate period and suppress the influence of noise on the recognition process. This period is used to construct the optimal Ramanujan subspace (RS). Finally, harmonic spectral kurtosis (HSK) is used to identify the extracted period as false information, interference, or fault. Simulation and experimental data verify the effectiveness of the proposed method. This method can effectively extract and screen periodic pulses and can successfully identify the outer and inner ring faults of bearings.