分布式能源2026，Vol.11Issue(1)：20-26,7.DOI:10.16513/j.2096-2185.DE.25100291
基于磁场信号的风电机组螺栓监测传感器的设计和实验研究
Design and Experimental Investigation of a Bolt Monitoring Sensor for Wind Turbine Using Magnetic Field Signals
摘要
Abstract
The development of wind power generation is a critical measure for achieving the"dual carbon"goals.The safe and reliable operation of wind turbines is the foundation for ensuring their sustainable operation.As key fastening components of wind turbines,connection bolts are subjected to alternating loads and environmental corrosion over long periods,making them prone to loosening,fatigue fractures,and other safety risks that threaten the operational safety of the turbines.To address this issue,this study proposes a stress state detection sensor device for tower and blade root bolts based on magnetic field signals.On this basis,a dedicated experimental testing system is established to quantitatively investigate the correlation between magnetic memory signals and bolt stress.Experimental results demonstrate that stress-induced magnetic signals can be effectively detected on the bolt surface.Under tensile loading,the magnetic memory signals exhibit a clear linear response to stress variations,enabling reliable stress state monitoring through magnetic measurements.Furthermore,the influence of bolt material on the relationship between stress and magnetic signal variation is relatively minor.In contrast,significant differences are observed in the slopes of the magnetic signal-stress curves for bolts of different strength grades,indicating the necessity of prior calibration.The test analysis in this paper can provide the necessary experimental foundation and reference data for engineering applications such as remote monitoring and early diagnosis of wind turbine bolt conditions.关键词
风电机组/螺栓安全/状态监测/磁记忆信号Key words
wind turbine/bolt safety/condition monitoring/magnetic memory signal分类
能源科技引用本文复制引用
王海军,蔡伟,纪贤瑞,邱虎,刘嵩,孙岩,熊毅..基于磁场信号的风电机组螺栓监测传感器的设计和实验研究[J].分布式能源,2026,11(1):20-26,7.基金项目
国家重点研发计划项目(2023YFC3009200) This work is supported by National Key Research and Development Program of China(2023YFC3009200) （2023YFC3009200）
