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

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.

相位同步对工况下模态测试的影响评估分析（英文）

目的:通过研究证明激励信号的相位信息对同步激励模态分析的重要性:当各激励信号的相位信息非一致时,采用较少次数的时间平均即可实现对非激励-响应信号的滤除。创新点:通过对比采用一致相位激励信号和非一致相位激励信号下的频率响应函数,证明了当各激励信号的相位信息非一致时,采用较少次数的时间平均足以实现对非激励-响应信号的滤除。方法:基于非一致相位信号的同步激励模态分析法。结论:基于同步激励时间平均技术,通过对比采用一致相位激励信号和非一致相位激励信号下的频率响应函数,证明了当各激励信号的相位信息非一致时,采用较少次数的时间平均足以实现对非激励-响应信号的滤除。在仿真试验中,非同步信号成分的滤除效果提升了98.48%;模态分析试验中,滤除效果提升了95.22%。