A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring
Advances in high performance sensing technologies enable the development of wind turbine condition monitoring system to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two stage fault detection framework for failure diagnosis of rotating componen...
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The Prognostics and Health Management Society
2013-01-01
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doaj-d1d537deec3d40f9b7a816c15a74d1a02021-07-02T02:04:20ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482013-01-014Sp22131A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition MonitoringJanet M. TwomeyShuangwen ShengPingfeng WangPrasanna TamilselvanAdvances in high performance sensing technologies enable the development of wind turbine condition monitoring system to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method and a graphical verification method together to ensure the diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study with the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully picked five faults out of seven in total with accurate severity levels without producing any false alarm in the blind analysis. The case study results indicated that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drive train system based upon system vibration characteristics.http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2013/ijphm_13_010.pdfGearboxCondition monitoringDiagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Janet M. Twomey Shuangwen Sheng Pingfeng Wang Prasanna Tamilselvan |
spellingShingle |
Janet M. Twomey Shuangwen Sheng Pingfeng Wang Prasanna Tamilselvan A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring International Journal of Prognostics and Health Management Gearbox Condition monitoring Diagnosis |
author_facet |
Janet M. Twomey Shuangwen Sheng Pingfeng Wang Prasanna Tamilselvan |
author_sort |
Janet M. Twomey |
title |
A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring |
title_short |
A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring |
title_full |
A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring |
title_fullStr |
A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring |
title_full_unstemmed |
A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring |
title_sort |
two-stage diagnosis framework for wind turbine gearbox condition monitoring |
publisher |
The Prognostics and Health Management Society |
series |
International Journal of Prognostics and Health Management |
issn |
2153-2648 |
publishDate |
2013-01-01 |
description |
Advances in high performance sensing technologies enable the development of wind turbine condition monitoring system to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method and a graphical verification method together to ensure the diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study with the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully picked five faults out of seven in total with accurate severity levels without producing any false alarm in the blind analysis. The case study results indicated that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drive train system based upon system vibration characteristics. |
topic |
Gearbox Condition monitoring Diagnosis |
url |
http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2013/ijphm_13_010.pdf |
work_keys_str_mv |
AT janetmtwomey atwostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT shuangwensheng atwostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT pingfengwang atwostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT prasannatamilselvan atwostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT janetmtwomey twostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT shuangwensheng twostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT pingfengwang twostagediagnosisframeworkforwindturbinegearboxconditionmonitoring AT prasannatamilselvan twostagediagnosisframeworkforwindturbinegearboxconditionmonitoring |
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1721343992112611328 |