Robust Separation-Enhanced NRC Method for Multiple Periodicity Detection: Applications in Bearing Compound Fault Diagnosis

Identification of compound faults in rotating machinery bearings is crucial for ensuring reliability and safety. Traditional methods face challenges in detecting weak multiperiodic components amidst strong noise during bearing malfunctions. While the noise-resistant correlation (NRC) method excels in single hidden period detection, it struggles with unclear peaks under strong noise and complex fault diagnoses. This article introduces a novel approach, the separation-enhanced NRC (SE-NRC) method, which addresses these challenges. First, we approach the NRC method from a different perspective and propose an enhanced version, referred to as the enhanced NRC (E-NRC) method, which amplifies the magnitude of peaks at periodic locations. Second, we integrate maximum overlap discrete wavelet packet transform (MODWPT) and construct the Anti-Gini index as a metric to evaluate the magnitude of periodic components in the decomposed signal, facilitating multiperiod detection. In addition, a thresholding method is proposed to filter components containing periodic information. The effectiveness of the proposed method in detecting compound faults has also been tested by simulation and experiment studies.