Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

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...

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Main Authors: Hanxin Chen, Yanjun Lu, Ling Tu
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.3233/SAV-2012-00741
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author Hanxin Chen
Yanjun Lu
Ling Tu
author_facet Hanxin Chen
Yanjun Lu
Ling Tu
author_sort Hanxin Chen
collection DOAJ
description 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.
format Article
id doaj-art-6bd1c23a80bb49d79ce307739d967cbd
institution Kabale University
issn 1070-9622
1875-9203
language English
publishDate 2013-01-01
publisher Wiley
record_format Article
series Shock and Vibration
spelling doaj-art-6bd1c23a80bb49d79ce307739d967cbd2025-08-20T03:33:45ZengWileyShock and Vibration1070-96221875-92032013-01-0120224726210.3233/SAV-2012-00741Fault Identification of Gearbox Degradation with Optimized Wavelet Neural NetworkHanxin Chen0Yanjun Lu1Ling Tu2School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, Hubei, ChinaSchool of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, Hubei, ChinaSchool of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, Hubei, ChinaA 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.http://dx.doi.org/10.3233/SAV-2012-00741
spellingShingle Hanxin Chen
Yanjun Lu
Ling Tu
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Shock and Vibration
title Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
title_full Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
title_fullStr Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
title_full_unstemmed Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
title_short Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
title_sort fault identification of gearbox degradation with optimized wavelet neural network
url http://dx.doi.org/10.3233/SAV-2012-00741
work_keys_str_mv AT hanxinchen faultidentificationofgearboxdegradationwithoptimizedwaveletneuralnetwork
AT yanjunlu faultidentificationofgearboxdegradationwithoptimizedwaveletneuralnetwork
AT lingtu faultidentificationofgearboxdegradationwithoptimizedwaveletneuralnetwork