{"title":"Research on Wind Turbine Fault Diagnosis Method Realized by Vibration Monitoring","authors":"Xiuhua Jiang","doi":"10.1007/s40745-023-00497-x","DOIUrl":null,"url":null,"abstract":"<div><p>Wind energy is one of the fast evolving renewable energy sources that has seen widespread application. Therefore, research on its carrier, the wind turbine, is growing, and the majority of them concentrate on the diagnosis of wind turbine faults. In this paper, the vibration signals collected in the time domain by vibration monitoring were analyzed, and the fault characteristic parameters were identified. These parameters were then inputted into a genetic algorithm back-propagation neural network (GA-BPNN) for wind turbine fault diagnosis. It was found that the presence of defects in the wind turbine depended on the effective value, peak value, and kurtosis of the vibration signal. The overall recognition accuracy of the GA-BPNN was 94.89%, which was much higher than that of the support vector machine (88.7%) and random forest (88.35%). Therefore, it is feasible and highly accurate to extract fault characteristic parameters through vibration monitoring and input them into a GA-BPNN for wind turbine fault diagnosis.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-023-00497-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
Wind energy is one of the fast evolving renewable energy sources that has seen widespread application. Therefore, research on its carrier, the wind turbine, is growing, and the majority of them concentrate on the diagnosis of wind turbine faults. In this paper, the vibration signals collected in the time domain by vibration monitoring were analyzed, and the fault characteristic parameters were identified. These parameters were then inputted into a genetic algorithm back-propagation neural network (GA-BPNN) for wind turbine fault diagnosis. It was found that the presence of defects in the wind turbine depended on the effective value, peak value, and kurtosis of the vibration signal. The overall recognition accuracy of the GA-BPNN was 94.89%, which was much higher than that of the support vector machine (88.7%) and random forest (88.35%). Therefore, it is feasible and highly accurate to extract fault characteristic parameters through vibration monitoring and input them into a GA-BPNN for wind turbine fault diagnosis.
期刊介绍：
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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