基于多源数据融合的风力发电机叶片缺陷图像识别与分析  

Image Recognition and Analysis of Wind Turbine Blade Defects Based on Multi-source Data Fusion

作　　者：夏汨罗 杨峥 周邵楠 Miluo Xia;Zheng Yang;Shaonan Zhou(State Power Investment Corporation Henan Engineering Operation and Maintenance Co.,Ltd.,Zhengzhou,Henan 450000,China)

机构地区：[1]国电投河南工程运维有限公司,河南郑州450000

出　　处：《产业科技创新》2024年第5期58-61,共4页Industrial Technology Innovation

摘　　要：本文提出了一种基于多源数据融合和深度学习技术的风力发电机叶片缺陷图像识别与分析方法。考虑到风力发电机叶片在运行过程中易受到多种因素的影响,本文首先通过多源数据的获取与预处理,利用来自不同传感器的数据提高了识别系统的输入信息量和质量。随后,采用数据融合技术整合不同来源的信息,以提升叶片缺陷识别的准确性。在深度学习模型方面,本文不仅探讨了模型选择和训练策略,还着重于模型优化与参数调整,通过调整网络结构和优化训练过程来提高模型性能。This paper proposes an image recognition and analysis method for wind turbine blade defects based on multi-source data fusion and deep learning technology.Considering that wind turbine blades are susceptible to various factors during operation,this paper first improves the input information quantity and quality of the recognition system by acquiring and preprocessing multi-source data,utilizing data from different sensors.Subsequently,data fusion technology is employed to integrate information from different sources,enhancing the accuracy of blade defect recognition.In terms of deep learning models,this paper not only discusses model selection and training strategies but also focuses on model optimization and parameter tuning,improving model performance by adjusting network structures and optimizing the training process.

关 键 词：多源数据融合 风力发电机叶片 缺陷图像识别 深度学习 数据预处理 

分 类 号：TM315[电气工程—电机]