Wavelet‑based vibration denoising for structural health monitoring  

作　　者：Ahmed Silik Mohammad Noori Zhishen Wu Wael A.Altabey Ji Dang Nabeel S.D.Farhan 

机构地区：[1]Key Laboratory of C&PC Structures Ministry of Education,National and Local Unified Engineering Research Center for Basalt Fiber Production and Application Technology,Southeast University,Nanjing 211189,China [2]Mechanical Engineering Department,California Polytechnic State University,One Grand Avenue,San Luis Obispo,CA 93407,USA [3]School of Civil Engineering,University of Leeds,Leeds LS29JT,UK [4]Department of Mechanical Engineering,Faculty of Engineering,Alexandria University,Alexandria(21544),Egypt [5]Civil and Environmental Engineering,Saitama University,255 Shimo‑Okubo,Asakura‑Ku,Saitama City,Japan [6]Department of Civil Engineering,Faculty of Engineering Sciences,Nyala University,Nyala,Sudan [7]School of Civil Engineering,Henan University of Technology,Zhengzhou,China

出　　处：《Urban Lifeline》2024年第1期193-206,共14页城市生命线(英文)

基　　金：support from National Natural Science Foundation of China(Grant No.52178115);the support provided by the International Institeu for Urban Systems Engineering at Southeast University.

摘　　要：In the context of civil engineering applications,vibration responses are complex,exhibiting variations in time and space and often containing nonlinearity and uncertainties not considered during data collection.These responses can also be contaminated by various sources,impacting damage identification processes.A significant challenge is how to effectively remove noise from these data to obtain reliable damage indicators that are unresponsive to noise and environmental factors.This study proposes a new denoising algorithm based on discrete wavelet transform(DWT)that addresses this issue.The suggested method offers a strategy for denoising using distinct thresholds for positive and negative coefficient values at each band and applying denoising process to both detail and trend components.The results prove the effectiveness of the technique and show that Bayes thresholding performs better than the other techniques in terms of the evaluated metrics.This suggests that Bayes thresholding is a more accurate and robust technique for thresholding compared to other common techniques.

关 键 词：Discrete wavelet transform DENOISING THRESHOLDING Structural responses 

分 类 号：O17[理学—数学]