No 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.INDEX TERMS Incipient damage detection, rolling bearing, wind turbine, variational mode decomposition, adaptive maximum correlated kurtosis deconvolution, grasshopper optimization algorithm.

Weak faults in planetary gear trains are difficult to detect due to the interference of background noise and the amplitude modulation effect. In order to detect weak faults in planetary gearboxes, this paper proposes a comprehensive diagnostic methodology, referred to as improved maximum correlation kurtosis deconvolution (IMCKD). The proposed diagnostic methodology combines MCKD with variational mode decomposition (VMD) and the grasshopper optimization algorithm (GOA) to solve the difficulty of selecting MCKD parameters. An optimal mode obtained from VMD is selected to determine the deconvolution period of T in MCKD. The filter length L and the number of shifts M are determined by GOA. The effectiveness of the IMCKD methodology is proven by the successful detection of an incipient root crack in the sun gear of a single stage planetary gearbox. The advantages of the proposed IMCKD method are further highlighted by a comparison between two illustrious weak fault diagnosis methods, MED-SK and PSO-SR.

No abstract

No abstract