Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning

At present, high frequency vibration data of wind turbine gearbox is collected through condition monitoring system and fault diagnosis for gearbox is made by manual analysis. But this method requires vibration analysis engineers with sufficient experience knowledge. And because of the large number o...

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Main Authors: CHEN Yanan, HU Kaikai, CHEN Gang, SHU Hui, LI Ziyuan
Format: Article
Language:zho
Published: Editorial Office of Control and Information Technology 2021-01-01
Series:Kongzhi Yu Xinxi Jishu
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Online Access:http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.018
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author CHEN Yanan
HU Kaikai
CHEN Gang
SHU Hui
LI Ziyuan
author_facet CHEN Yanan
HU Kaikai
CHEN Gang
SHU Hui
LI Ziyuan
author_sort CHEN Yanan
collection DOAJ
description At present, high frequency vibration data of wind turbine gearbox is collected through condition monitoring system and fault diagnosis for gearbox is made by manual analysis. But this method requires vibration analysis engineers with sufficient experience knowledge. And because of the large number of units, manual analysis will be time-consuming and laborious. In this paper, frequency conversion and alignment of the original data collected are carried out to eliminate the influence of changing working conditions. Data samples are amplified by time-long segmentation, and time domain and frequency domain features of a specific frequency doubling section are extracted as the input of machine learning. Through model training, a gearbox fault prediction model is constructed. The accuracy of the model is more than 90%, which effectively realizes the gearbox fault prediction.
format Article
id doaj-art-ca8f1d4d0f0144729aa7659e88f1eb60
institution Kabale University
issn 2096-5427
language zho
publishDate 2021-01-01
publisher Editorial Office of Control and Information Technology
record_format Article
series Kongzhi Yu Xinxi Jishu
spelling doaj-art-ca8f1d4d0f0144729aa7659e88f1eb602025-08-25T06:53:05ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-013810811282319139Fault Early Warning of Wind Turbine Gearbox Based on Machine LearningCHEN YananHU KaikaiCHEN GangSHU HuiLI ZiyuanAt present, high frequency vibration data of wind turbine gearbox is collected through condition monitoring system and fault diagnosis for gearbox is made by manual analysis. But this method requires vibration analysis engineers with sufficient experience knowledge. And because of the large number of units, manual analysis will be time-consuming and laborious. In this paper, frequency conversion and alignment of the original data collected are carried out to eliminate the influence of changing working conditions. Data samples are amplified by time-long segmentation, and time domain and frequency domain features of a specific frequency doubling section are extracted as the input of machine learning. Through model training, a gearbox fault prediction model is constructed. The accuracy of the model is more than 90%, which effectively realizes the gearbox fault prediction.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.018wind turbinegearboxfault diagnosismachine learningfault predictionautomatic diagnosis
spellingShingle CHEN Yanan
HU Kaikai
CHEN Gang
SHU Hui
LI Ziyuan
Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
Kongzhi Yu Xinxi Jishu
wind turbine
gearbox
fault diagnosis
machine learning
fault prediction
automatic diagnosis
title Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
title_full Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
title_fullStr Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
title_full_unstemmed Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
title_short Fault Early Warning of Wind Turbine Gearbox Based on Machine Learning
title_sort fault early warning of wind turbine gearbox based on machine learning
topic wind turbine
gearbox
fault diagnosis
machine learning
fault prediction
automatic diagnosis
url http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.018
work_keys_str_mv AT chenyanan faultearlywarningofwindturbinegearboxbasedonmachinelearning
AT hukaikai faultearlywarningofwindturbinegearboxbasedonmachinelearning
AT chengang faultearlywarningofwindturbinegearboxbasedonmachinelearning
AT shuhui faultearlywarningofwindturbinegearboxbasedonmachinelearning
AT liziyuan faultearlywarningofwindturbinegearboxbasedonmachinelearning