Noise robustness of an operational modal-based structural damage-detection scheme using impact-synchronous modal analysis  

作　　者：Pei Yi SIOW Zhi Chao ONG Shin Yee KHOO Kok-Sing LIM 

机构地区：[1]Department of Mechanical Engineering,Faculty of Engineering,Universiti Malaya,Kuala Lumpur 50603,Malaysia [2]Photonics Research Centre,Deputy Vice Chancellor(Research and Innovation)Office,Universiti Malaya,Kuala Lumpur 50603,Malaysia

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2023年第9期782-800,共19页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the Ministry of Higher Education for the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UM/02/29);the SD Advance Engineering Sdn.Bhd.(No.PV032-2018);the SATU Joint Research University Grant(No.ST020-2020);the Impact-Oriented Interdisciplinary Research Grant(No.IIRG007B-2019)awarded to Zhi Chao ONG;the Advanced Shock and Vibration Research(ASVR)Group of University of Malaya.

摘　　要：Data-driven damage-detection schemes are usually unsupervised machine-learning models in practice,as these do not require any training.Vibration-based features are commonly used in these schemes but often require several other parameters to accurately correlate with damage,as they may not globally represent the model,making them less sensitive to damage.Modal data,such as frequency response functions(FRFs)and principal component analysis(PCA)reduced FRFs(PCA-FRFs),inherits the dynamic characteristics of the structure,and it changes when damage occurs,thus showing sensitivity to damage.However,noise from the environment or external sources such as wind,operating machines,or the in-service system itself,can reduce the modal data's sensitivity to damage if not handled properly,which affects damage-detection accuracy.This study proposes a noise-robust operational modal-based structural damage-detection scheme that uses impact-synchronous modal analysis(ISMA)to generate clean,static-like FRFs for damage diagnosis.ISMA allows modal data collection without requiring shutdown conditions,and its denoising feature aids in generating clean,static-like FRFs for damage diagnosis.Our results showed that the FRFs obtained through ISMA under noise conditions have frequency response assurance criterion(FRAC)and cross signature assurance criterion(CSAC)scores greater than 0.9 when compared with FRFs obtained through experimental modal analysis(EMA)under static conditions;this validates the denoising feature of ISMA.When the denoised FRFs are reduced to PCA-FRFs and used in an unsupervised learning-based damage-detection scheme,zero false alarms occur.

关 键 词：Impact-synchronous modal analysis(ISMA) Frequency response function(FRF) Principal component analysis(PCA) Unsupervised learning Damage detection 

分 类 号：TB53[理学—物理] U270.16[理学—声学]