Fault Diagnosis for Rolling Bearings Based on RCMDE and GG

Given the nonlinear and nonstationary nature of fault signals in rotating machinery, this paper proposes a fault feature extraction and identification method based on refined composite multiscale dispersion entropy (RCMDE) and Gath–Geva (GG) clustering. In the proposed approach, RCMDE is first employed to extract feature sets from the vibration signals. Principal component analysis (PCA) is then applied for dimensionality reduction, and the resulting low-dimensional features are fed into the GG clustering algorithm to achieve fault diagnosis. The effectiveness of the proposed method is validated on a typical bearing fault dataset, with classification performance evaluated using the classification coefficient and partition entropy. Experimental results demonstrate that the proposed RCMDE-GG achieves superior performance in fault feature extraction and exhibits excellent classification capability.