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On-line Access: 2023-06-20

Received: 2022-12-28

Revision Accepted: 2022-01-21

Crosschecked: 2023-09-20

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhi Chao Ong

https://orcid.org/0000-0002-1686-3551

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Journal of Zhejiang University SCIENCE A 2023 Vol.24 No.9 P.782-800

http://doi.org/10.1631/jzus.A2200620


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


Author(s):  Pei Yi SIOW, Zhi Chao ONG, Shin Yee KHOO, Kok-Sing LIM

Affiliation(s):  Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia; more

Corresponding email(s):   zhichao83@gmail.com, alexongzc@um.edu.my

Key Words:  Impact-synchronous modal analysis (ISMA), Frequency response function (FRF), Principal component analysis (PCA), Unsupervised learning, Damage detection


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.

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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.

使用冲击同步模态分析的基于运行模态的结构损伤检测方案的噪声鲁棒性

作者:萧珮怡1,翁志超1,邱峰义1,林国生2
机构:1马来亚大学,工程学院机械工程系,马来西亚吉隆坡,50603;2马来亚大学,副校长(研究与创新)办公室,光子学研究中心,马来西亚吉隆坡,50603
目的:来自环境或外部来源的噪声,如风、运行中的机器或来自在役系统本身的噪声,如果不加以注意或处理,会降低模态数据对损伤的敏感性,从而影响损伤检测的准确性。为了生成用于损伤诊断的干净、类静态频率响应函数(FRF),本研究提出了一种使用冲击同步模态分析(ISMA)的基于噪声鲁棒操作模态的结构损伤检测方案。
创新点:结合ISMA、主成分分析(PCA)-FRF和基于无监督学习的方法对运行结构的损伤检测进行去噪和提取对损伤敏感的主要特征。
方法:所提出的损伤检测方案的总体框架包括三个阶段:1.首先使用ISMA对信号进行去噪,以生成仅包含结构动态信息的干净FRF。2.应用PCA清理FRF,生成的前两个主成分用于构造单线PCA-FRF,提取PCA-FRF的峰作为无监督的特征基于机器学习的损伤检测。3.采用无监督机器学习k-means算法进行损伤检测。邓恩指数用作损坏指标,当邓恩指数超过基线/未损坏的邓恩指数时,就会检测到损坏。
结论:结果表明,与在静态条件下通过实验模态分析(EMA)获得的FRF相比,通过ISMA在噪声条件下获得的FRF具有大于0.9的频率响应保证准则(FRAC)和交叉签名保证准则(CSAC)分数,这验证了ISMA的去噪功能。当去噪的FRF减少到PCA-FRF并用于无监督的基于学习的损伤检测方案时,显示零误报。

关键词:冲击同步模态分析;频率响应函数;主成分分析;无监督学习;损伤检测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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