Fault identification of gearbox degradation with optimized wavelet neural network

TLDR: In this paper, a novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degra- dation in gearbox in which wavelet packet analysis (WPA) was applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction.

Abstract: A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degra- dation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back- propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experi- mental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.