Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox
Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/6748469 |
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| _version_ | 1850226521222938624 |
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| author | Rusmir Bajric Ninoslav Zuber Georgios Alexandros Skrimpas Nenad Mijatovic |
| author_facet | Rusmir Bajric Ninoslav Zuber Georgios Alexandros Skrimpas Nenad Mijatovic |
| author_sort | Rusmir Bajric |
| collection | DOAJ |
| description | Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine. |
| format | Article |
| id | doaj-art-54ebd853b0c842199afdfacca64e0b42 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-54ebd853b0c842199afdfacca64e0b422025-08-20T02:05:03ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/67484696748469Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine GearboxRusmir Bajric0Ninoslav Zuber1Georgios Alexandros Skrimpas2Nenad Mijatovic3EPC Elektroprivreda BiH, Kreka Coal Mines, 75000 Tuzla, Bosnia and HerzegovinaFaculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaBruel and Kjær Vibro, 2850 Nærum, DenmarkTechnical University of Denmark, 2800 Lyngby, DenmarkVibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.http://dx.doi.org/10.1155/2016/6748469 |
| spellingShingle | Rusmir Bajric Ninoslav Zuber Georgios Alexandros Skrimpas Nenad Mijatovic Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox Shock and Vibration |
| title | Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox |
| title_full | Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox |
| title_fullStr | Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox |
| title_full_unstemmed | Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox |
| title_short | Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox |
| title_sort | feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox |
| url | http://dx.doi.org/10.1155/2016/6748469 |
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