海上风机无人机巡检螺旋桨振力异常监测研究
Research on Abnormal Monitoring of Propeller Vibration Force During Inspec-tion of Offshore Wind Turbine Unmanned Aerial Vehicle
刘艳贵 1傅望安 1王海明 1曾崇济2
作者信息
- 1. 华能(浙江)能源开发有限公司清洁能源分公司,杭州 310014
- 2. 中国华能集团清洁能源技术研究院有限公司,北京 102209
- 折叠
摘要
在对海上风机巡检过程中,由于无人机螺旋桨振力振动信号数据量较大,且海上环境的复杂性和不稳定性增加了异常信号的干扰,使得异常信号的检测和识别更加困难.为此,该文提出一种海上风机无人机巡检螺旋桨振力异常监测方法.利用集成经验模态(EEMD)分解,处理无人机螺旋桨振力信号,提取固有模态分量(IMF);利用稀疏傅里叶变换,处理具有周期性成分的IMF组分,提取振动强度异常特征;引入多传感器信息决策融合网络结构,并利用神经网络对其展开融合和综合决策,最终实现无人机螺旋桨振力异常监测.实验结果表明,所提方法可以获取高准确率和高效率的螺旋桨振力异常监测结果.
Abstract
During the inspection process of offshore wind turbines,due to the large amount of data on the vibration signals of unmanned aerial vehicle propellers and the complexity and instability of the marine environment,the inter-ference of abnormal signals is increased,making the detection and identification of abnormal signals more difficult.To this end,a monitoring method for abnormal propeller vibration force during offshore wind turbine unmanned aerial ve-hicle inspection is proposed.Utilizing integrated empirical mode decomposition(EEMD)decomposition to process the vibration signal of unmanned aerial vehicle propeller and extract the intrinsic mode function(IMF)components.Using sparse Fourier transform to process IMF components with periodic components and extract abnormal vibration intensity features.Introducing a multi-sensor information fusion network structure and utilizing neural networks to fuse and make comprehensive decisions,ultimately achieving abnormal monitoring of unmanned aerial vehicle propeller vibration force.The experimental results show that the proposed method can obtain high accuracy and efficiency monitoring re-sults of abnormal propeller vibration force.
关键词
海上风机无人机/巡检螺旋桨/振力异常/监测Key words
offshore wind turbine unmanned aerial vehicle/patrol inspection of propellers/abnormal vibration force/monitor引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据