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

[1]BandaraRP, ChanTHT, ThambiratnamDP, 2014. Frequency response function based damage identification using principal component analysis and pattern recognition technique. Engineering Structures, 66:116-128.

[2]BokaeianV, KhoshnoudianF, FallahianM, 2021. Structural damage detection in plates using a deep neural network-couple sparse coding classification ensemble method. Journal of Vibration and Control, 27(3-4):437-450.

[3]BouzenadAE, El MountassirM, YaacoubiS, et al., 2019. A semi-supervised based k-means algorithm for optimal guided waves structural health monitoring: a case study. Inventions, 4(1):17.

[4]BrownjohnJMW, XiaPQ, HaoH, et al., 2001. Civil structure condition assessment by FE model updating: methodology and case studies. Finite Elements in Analysis and Design, 37(10):761-775.

[5]ChenSL, OngZC, LamWH, et al., 2020. Unsupervised damage identification scheme using PCA-reduced frequency response function and waveform chain code analysis. International Journal of Structural Stability and Dynamics, 20(8):2050091.

[6]ChenW, JinMS, HuangJS, et al., 2021. A method to distinguish harmonic frequencies and remove the harmonic effect in operational modal analysis of rotating structures. Mechanical Systems and Signal Processing, 161:107928.

[7]DaneshvarMH, SarmadiH, 2022. Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring. Engineering Structures, 256:114059.

[8]DasS, RoyK, 2022. A state-of-the-art review on FRF-based structural damage detection: development in last two decades and way forward. International Journal of Structural Stability and Dynamics, 22(2):2230001.

[9]ElyasiN, KhoshnoudianF, KhoshnoudianY, 2019. A new damage index for isolated structures. International Journal of Structural Stability and Dynamics, 19(6):1950055.

[10]EntezamiA, ShariatmadarH, 2018. An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification. Structural Health Monitoring, 17(2):325-345.

[11]FavarelliE, TestiE, GiorgettiA, 2022. The impact of sensing parameters on data management and anomaly detection in structural health monitoring. Journal of Civil Structural Health Monitoring, 12(6):1413-1425.

[12]FleetT, KameiK, HeFY, et al., 2020. A machine learning approach to model interdependencies between dynamic response and crack propagation. Sensors, 20(23):6847.

[13]FotiD, GiannoccaroNI, VaccaV, et al., 2020. Structural operativity evaluation of strategic buildings through finite element (FE) models validated by operational modal analysis (OMA). Sensors, 20(11):3252.

[14]Garcia-PerezA, Amezquita-SanchezJP, Dominguez-GonzalezA, et al., 2013. Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(9):615-630.

[15]GillichN, TufisiC, SacareaC, et al., 2022. Beam damage assessment using natural frequency shift and machine learning. Sensors, 22(3):1118.

[16]GordanM, IsmailZ, RazakHA, et al., 2020a. Data mining-based damage identification of a slab-on-girder bridge using inverse analysis. Measurement, 151:107175.

[17]GordanM, RazakHA, IsmailZ, et al., 2020b. A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining. Applied Soft Computing, 88:106013.

[18]JayasundaraN, ThambiratnamDP, ChanTHT, et al., 2020. Locating and quantifying damage in deck type arch bridges using frequency response functions and artificial neural networks. International Journal of Structural Stability and Dynamics, 20(10):2042010.

[19]KangJ, LiuL, ShaoYP, et al., 2021. Non-stationary signal decomposition approach for harmonic responses detection in operational modal analysis. Computers & Structures, 242:106377.

[20]KhoshnoudianF, TalaeiS, 2017. A new damage index using FRF data, 2D-PCA method and pattern recognition techniques. International Journal of Structural Stability and Dynamics, 17(8):1750090.

[21]KimJJ, KimAR, LeeSW, 2020. Artificial neural network-based automated crack detection and analysis for the inspection of concrete structures. Applied Sciences, 10(22):8105.

[22]KouadriA, HajjiM, HarkatMF, et al., 2020. Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems. Renewable Energy, 150:598-606.

[23]KumarK, BiswasPK, DhangN, 2020. Time series-based SHM using PCA with application to ASCE benchmark structure. Journal of Civil Structural Health Monitoring, 10(5):899-911.

[24]LimHC, OngZC, BrandtA, 2018. Implementation of phase controlled impact device for enhancement of frequency response function in operational modal testing. Journal of the Franklin Institute, 355(1):291-313.

[25]LimHC, OngZC, IsmailZ, et al., 2019. A performance study of controlled impact timing on harmonics reduction in operational modal testing. Journal of Dynamic Systems, Measurement, and Control, 141(3):034501.

[26]LiuJH, KizakiT, RenZW, et al., 2022. Mode shape database-based estimation for machine tool dynamics. International Journal of Mechanical Sciences, 236:107739.

[27]LydonD, KromanisR, LydonM, et al., 2022. Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges. Journal of Civil Structural Health Monitoring, 12(6):1299-1316.

[28]MaZS, DingQ, TangY, 2020. Operational modal analysis of a liquid-filled cylindrical structure with decreasing filling mass by multivariate stochastic parameter evolution methods. International Journal of Mechanical Sciences, 172:105420

[29]ManochandarS, PunniyamoorthyM, JeyachitraRK, 2020. Development of new seed with modified validity measures for k-means clustering. Computers & Industrial Engineering, 141:106290

[30]MaoJX, YangCY, WangH, et al., 2022. Bayesian operational modal analysis with genetic optimization for structural health monitoring of the long-span bridge. International Journal of Structural Stability and Dynamics, 22(5):2250051.

[31]NickH, AziminejadA, 2021. Vibration-based damage identification in steel girder bridges using artificial neural network under noisy conditions. Journal of Nondestructive Evaluation, 40(1):15.

[32]OngZC, LimHC, KhooSY, et al., 2017. Assessment of the phase synchronization effect in modal testing during operation. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18(2):92-105.

[33]OngZC, LimHC, BrandtA, 2018. Automated impact device with non-synchronous impacts: a practical solution for modal testing during operation. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 19(6):452-460.

[34]OngZC, LimHC, BrandtA, et al., 2019. An inconsistent phase selection assessment for harmonic peaks elimination in operational modal testing. Archive of Applied Mechanics, 89(12):2415-2430.

[35]OzdagliAI, KoutsoukosX, 2019. Machine learning based novelty detection using modal analysis. Computer-Aided Civil and Infrastructure Engineering, 34(12):1119-1140.

[36]PadilKH, BakharyN, AbdulkareemM, et al., 2020. Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using artificial neural network. Journal of Sound and Vibration, 467:115069.

[37]RahmanAGA, ChaoOZ, IsmailZ, 2011a. Effectiveness of impact-synchronous time averaging in determination of dynamic characteristics of a rotor dynamic system. Measurement, 44(1):34-45.

[38]RahmanAGA, OngZC, IsmailZ, 2011b. Enhancement of coherence functions using time signals in modal analysis. Measurement, 44(10):2112-2123.

[39]SantosJP, CrémonaC, CaladoL, et al., 2016. On-line unsupervised detection of early damage. Structural Control and Health Monitoring, 23(7):1047-1069.

[40]SarmadiH, EntezamiA, SalarM, et al., 2021. Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. Journal of Civil Structural Health Monitoring, 11(3):629-644.

[41]ShinK, 2016. An alternative approach to measure similarity between two deterministic transient signals. Journal of Sound and Vibration, 371:434-445.

[42]SiowPY, OngZC, KhooSY, et al., 2021. Damage sensitive PCA-FRF feature in unsupervised machine learning for damage detection of plate-like structures. International Journal of Structural Stability and Dynamics, 21(2):2150028.

[43]SolimineJ, NiezreckiC, InalpolatM, 2020. An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades. Structural Health Monitoring, 19(6):1711-1725.

[44]SvendsenBT, FrøsethGT, ØisethO, et al., 2022. A data-based structural health monitoring approach for damage detection in steel bridges using experimental data. Journal of Civil Structural Health Monitoring, 12(1):101-115.

[45]WanHP, NiYQ, 2018. Bayesian modeling approach for forecast of structural stress response using structural health monitoring data. Journal of Structural Engineering, 144(9):04018130.

[46]XieYL, LiBB, GuoJ, 2020. Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 21(7):553-564.

[47]XinY, HaoH, LiJ, et al., 2019. Bayesian based nonlinear model updating using instantaneous characteristics of structural dynamic responses. Engineering Structures, 183:459-474.

[48]ZahidFB, OngZC, KhooSY, 2020. A review of operational modal analysis techniques for in-service modal identification. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(8):398.

[49]ZahidFB, OngZC, KhooSY, et al., 2021. Inertial sensor based human behavior recognition in modal testing using machine learning approach. Measurement Science and Technology, 32(11):115905.

[50]ZhouK, TangJ, 2021. Structural model updating using adaptive multi-response gaussian process meta-modeling. Mechanical Systems and Signal Processing, 147:107121.