Rolling Bearing Fault Diagnosis Using GOOSE-VMD Method

Abstract

Extracting weak fault-related impulses from rolling bearing vibration signals remains difficult, because the signals are non-stationary and heavily contaminated by noise, and because the parameters of variational mode decomposition (VMD) still have to be set manually. This paper proposes a method that combines GOOSE-optimized VMD with generalized Morse wavelet element analysis. GOOSE determines the mode number K and the penalty factor α adaptively, with permutation entropy as the fitness function, so that neither has to be preset; the two modes with the largest correlation coefficients among those whose excess kurtosis exceeds three are then selected, and element analysis estimates the impact period from them. Among the four criteria, permutation entropy corresponds most directly to the periodic impulse train produced by a defect, and its decomposition separates the fault-related components most clearly. For a self-built test rig, the period estimated from the reconstructed signal, 0.00616 s (162.38 Hz), agreed closely with the theoretical inner-ring fault frequency of 162.46 Hz at 1800 rpm; on the public CWRU bearing dataset, 69 of the 70 signals were diagnosed correctly (98.57%), covering both inner- and outer-ring faults. The method requires no labelled training data, and its decision is interpretable at every stage, as the fault type follows from a comparison between the measured impact period and the theoretical characteristic frequency. Its main limitation is that the self-built test rig covers only a single fault type, rotational speed and load.