Compound fault diagnosis method for rolling bearings based on enhanced MED and adaptive periodized symplectic geometry mode decomposition

Rolling bearing compound faults (RBCF) always interact and couple with each other, which makes it tremendously challenging to accurately diagnose them by processing the collected vibration signals. For the sake of separating and extracting fault features in RBCF, a novel method based on enhanced minimum entropy deconvolution (EMED) with adaptive periodized symplectic geometry mode decomposition (APSGMD) is proposed. First, weighted unbiased autocorrelation kurtosis is established as the new objective function to determine the optimal inverse filter coefficients of EMED method, which can enhance periodic impulse components of weak fault and eliminate background noise in RBCF signal. Second, for proposed APSGMD method, the termination condition based on cosine difference factor and kurtosis criterion are employed to adaptively select symplectic geometry components (SGCs), and a criterion for selecting singular value pairs is established to enhance the periodic impulse components of each SGC obtained. Finally, hierarchical clustering is leveraged to classify and reconstruct SGCs with different fault periods. A comprehensive simulation model is developed for RBCF to testify this method. The experimental results show that inner ring and outer ring faults, inner ring and ball faults, outer ring and ball faults, and inner–outer ring and ball faults can be accurately diagnosed by the proposed method.