将机器学习应用于风力涡轮机振动检测

IF 0.8 Q3 ENGINEERING, MULTIDISCIPLINARY Modelling and Simulation in Engineering Pub Date : 2022-02-16 DOI:10.1155/2022/6572298
J. Vives
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引用次数: 2

摘要

利用机器学习技术,可以提前检测和诊断风力涡轮机部件,从而防止退化。自动和自主学习用于预测、检测和诊断风力涡轮机的电气和机械故障。基于适应风力涡轮机不同部件和故障的机器学习算法的实现,本研究评估了用于监测、监督和故障诊断的不同方法。
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Incorporating Machine Learning into Vibration Detection for Wind Turbines
With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine learning algorithms adapted to the different components and faults of wind turbines, this study evaluates different methodologies for monitoring, supervision, and fault diagnosis.
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来源期刊
Modelling and Simulation in Engineering
Modelling and Simulation in Engineering ENGINEERING, MULTIDISCIPLINARY-
CiteScore
2.70
自引率
3.10%
发文量
42
审稿时长
18 weeks
期刊介绍: Modelling and Simulation in Engineering aims at providing a forum for the discussion of formalisms, methodologies and simulation tools that are intended to support the new, broader interpretation of Engineering. Competitive pressures of Global Economy have had a profound effect on the manufacturing in Europe, Japan and the USA with much of the production being outsourced. In this context the traditional interpretation of engineering profession linked to the actual manufacturing needs to be broadened to include the integration of outsourced components and the consideration of logistic, economical and human factors in the design of engineering products and services.
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