CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-09-20
Cited: 0
Clicked: 1249
Pei Yi SIOW, Zhi Chao ONG, Shin Yee KHOO, Kok-Sing LIM. Noise robustness of an operational modal-based structural damage-detection scheme using impact-synchronous modal analysis[J]. Journal of Zhejiang University Science A, 2023, 24(9): 782-800.
@article{title="Noise robustness of an operational modal-based structural damage-detection scheme using impact-synchronous modal analysis",
author="Pei Yi SIOW, Zhi Chao ONG, Shin Yee KHOO, Kok-Sing LIM",
journal="Journal of Zhejiang University Science A",
volume="24",
number="9",
pages="782-800",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200620"
}
%0 Journal Article
%T Noise robustness of an operational modal-based structural damage-detection scheme using impact-synchronous modal analysis
%A Pei Yi SIOW
%A Zhi Chao ONG
%A Shin Yee KHOO
%A Kok-Sing LIM
%J Journal of Zhejiang University SCIENCE A
%V 24
%N 9
%P 782-800
%@ 1673-565X
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200620
TY - JOUR
T1 - Noise robustness of an operational modal-based structural damage-detection scheme using impact-synchronous modal analysis
A1 - Pei Yi SIOW
A1 - Zhi Chao ONG
A1 - Shin Yee KHOO
A1 - Kok-Sing LIM
J0 - Journal of Zhejiang University Science A
VL - 24
IS - 9
SP - 782
EP - 800
%@ 1673-565X
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2200620
Abstract: 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.
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