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作 者:朱一凯 余哲帆 陈安妮 万华平 ZHU Yikai;YU Zhefan;CHEN Anni;WAN Huaping(Key Laboratory of Concrete and Pre-stressed Concrete Structures of Ministry of Education,Southeast University Nanjing,211189,China;College of Civil Engineering and Architecture,Zhejiang University Hangzhou,310058,China)
机构地区:[1]东南大学混凝土及预应力混凝土结构教育部重点实验室,南京211189 [2]浙江大学建筑工程学院,杭州310058
出 处:《振动.测试与诊断》2024年第4期684-689,824,共7页Journal of Vibration,Measurement & Diagnosis
基 金:国家重点研发计划资助项目(2021YFF0501001);国家自然科学基金资助项目(51878235);浙江省重点研发计划资助项目(2021C03154);混凝土及预应力混凝土结构教育部重点实验室开放课题资助项目(CPCSME2020-05)。
摘 要:由于风速具有明显的非平稳性,常用字典的压缩感知(compressed sensing,简称CS)方法对于风速信号重构效果不佳,故引入基于小波字典的压缩感知方法,用于重构风速缺失数据,有效提升了风速信号的重构精度。通过风速仿真数据和广州塔的监测风速数据验证了本研究方法的有效性,并研究了数据缺失工况、正则化参数、小波字典层数和小波类型对风速信号重构效果的影响。结果表明,基于小波字典的压缩感知方法可有效重构缺失的风速信号。Wind speed is usually non-stationary and not naturally sparse.The commonly used dictionary for the compressed sensing(CS)method is not effective for the reconstruction of the wind speed signals.In this paper,the wavelet dictionary is introduced to improve the sparsity of wind speed signals and effectively enhance the accuracy of the reconstructed signal.The effectiveness of this method is verified using both wind speed simulation data and monitored wind speed data of Canton Tower.The effects of data missing scenarios,regularization parameters,wavelet dictionary layers,and wavelet types on the reconstruction performance of the CS method are explored in detail.The results show that the wavelet dictionary-based CS method has high accuracy in reconstructing the missing wind speed signals.
分 类 号:TU317[建筑科学—结构工程] TH7[机械工程—仪器科学与技术]
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