Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution

Abstract

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.