Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method

Abstract

Incipient damages of wind turbine rolling bearing are very difficult to be detected because of the interference of multi-frequency components and strong ambient noise. To solve this problem, this paper proposes a new detected method named VMD-AMCKD, combining complementary advantages of variational mode decomposition (VMD) and adaptive maximum correlated kurtosis deconvolution (AMCKD). A novel index is proposed to screen out the most sensitive mode containing fault information after VMD decomposition. The mode also can determine a suspectable range for the fault frequency, based on which the optimized range of devolution period T in MCKD can be pre-determined. The Grasshopper optimization algorithm (GOA) is adapted to adaptively select the key parameters in MCKD. The proposed method can successfully diagnose the simulated signal mixed with strong white Gaussian noise. Its robustness is further proven by the diagnosis for three different types of experimental signal from CWRU bearing data center. Finally, the VMD-AMCKD is applied to detect incipient damages of rolling bearings in a laboratory wind turbine.