基于异常的风机主轴承故障检测

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Wind Energy Science Pub Date : 2023-04-14 DOI:10.5194/wes-8-557-2023
Lorena Campoverde-Vilela, Maria del Cisne Feijóo, Y. Vidal, José Sampietro, C. Tutivén
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引用次数: 4

摘要

摘要可再生能源是一种清洁且取之不尽的能源,因此每年对研究和寻求改善生产的兴趣都在增加。风能是最常用的能源之一,因此需要进行预测性维护管理,以确保每台风力涡轮机的可靠性和可操作性,这已成为一个很好的研究机会。在这项工作中,通过应用基于主成分分析(PCA)的异常检测器开发了一个故障检测系统,以对主轴承可能发生的故障进行预警。为了开发该模型,使用了来自运行中的风电场的SCADA数据。所获得的结果允许在致命故障发生前几个月检测故障。该模型只需要(构建)使用健康的SCADA数据,而不需要获得故障历史记录或安装需要更大投资的额外设备或传感器。总之,该拟议策略为风电场内预测性维护的规划和执行提供了一种工具。
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Anomaly-based fault detection in wind turbine main bearings
Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
期刊最新文献
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