An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis
Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, cont...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/9359426 |
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| _version_ | 1850210408467529728 |
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| author | Jian-Hua Zhong JieJunYi Liang Zhi-Xin Yang Pak Kin Wong Xian-Bo Wang |
| author_facet | Jian-Hua Zhong JieJunYi Liang Zhi-Xin Yang Pak Kin Wong Xian-Bo Wang |
| author_sort | Jian-Hua Zhong |
| collection | DOAJ |
| description | Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM), to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS. |
| format | Article |
| id | doaj-art-946861fd720e47d182df31b7e854c49e |
| 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-946861fd720e47d182df31b7e854c49e2025-08-20T02:09:47ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/93594269359426An Effective Fault Feature Extraction Method for Gas Turbine Generator System DiagnosisJian-Hua Zhong0JieJunYi Liang1Zhi-Xin Yang2Pak Kin Wong3Xian-Bo Wang4Department of Electromechanical Engineering, University of Macau, MacauSchool of Electrical, Mechanical and Mechatronic Systems, University of Technology Sydney, Sydney, NSW 2007, AustraliaDepartment of Electromechanical Engineering, University of Macau, MacauDepartment of Electromechanical Engineering, University of Macau, MacauDepartment of Electromechanical Engineering, University of Macau, MacauFault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM), to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.http://dx.doi.org/10.1155/2016/9359426 |
| spellingShingle | Jian-Hua Zhong JieJunYi Liang Zhi-Xin Yang Pak Kin Wong Xian-Bo Wang An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis Shock and Vibration |
| title | An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis |
| title_full | An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis |
| title_fullStr | An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis |
| title_full_unstemmed | An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis |
| title_short | An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis |
| title_sort | effective fault feature extraction method for gas turbine generator system diagnosis |
| url | http://dx.doi.org/10.1155/2016/9359426 |
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