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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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2012-00741 |
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| _version_ | 1849414707288473600 |
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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 |