Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network. (2013)

• Record Type: Journal Article
• Authors: Chen, Hanxin; Lu, Yanjun; Tu, Ling
• Is Part Of: Shock and vibration. Volume 2013(2013)
• Page Start: 247
• Page End: 262
• Publication Date: 2013
• Subjects: Fault identification -- wavelet neural network -- back-propagation algorithm -- gearbox degradation; Shock (Mechanics) -- Periodicals; Vibration -- Periodicals; 534.5
• DOI: 10.3233/SAV-2012-00741
• Languages: English
• ISSNs: 1070-9622

Abstract

Abstract : A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation 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 experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.