Vibration data often carries important information about the dynamic characteristics of structures, but continuous collection of high-frequency vibration data can create practical challenges (e.g., transmission and storage of large amounts of data). Compressive sensing (CS), which integrates data acquisition and compression utilizing the sparse nature of signals, shows promise in mitigating the practical challenges of data measurement, transmission, and storage. The presence of side lobes in the discrete Fourier transform (DFT) of vibration signals, known as the spectral leakage effect, adversely impacts the sparsity of these signals. This, in turn, diminishes the effectiveness of CS algorithms. Built with three key modifications, this study proposes a spectral compressive sampling matching pursuit (CoSaMP) algorithm to alleviate the effect of spectral leakage. First, the redundant DFT dictionary is incorporated to consider the spectral leakage effect. Second, a coherence-inhibiting large component identification step replaces the original one, addressing the coherence issues due to the redundant DFT dictionary. Third, Ridge regression supersedes the least squares in the estimation step to improve the reconstruction accuracy. The proposed spectral CoSaMP algorithm is validated by the simulated data from a frame structure and the field data collected from a long-span cable-stayed bridge. The influence of related parameters (i.e., frequency redundancy indicator, coherence threshold, sparsity level, and regularization parameter of Ridge regression) is extensively investigated, and recommended parameter settings are provided. Moreover, the superiority of spectral CoSaMP over classic CS algorithms (including classic CoSaMP, -norm regularized optimization, and Bayesian CS) on the reconstruction accuracy of vibration signals is demonstrated through comparative studies.
KopsaftopoulosFPFassoisSD.Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods. Mech Syst Signal Process2010; 24(7): 1977–1997.
2.
ZhangCWMousaviAAMasriSF, et al. Vibration feature extraction using signal processing techniques for structural health monitoring: a review. Mech Syst Signal Process2022; 177: 109175.
3.
GoyalDPablaBS.The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch Comput Methods Eng2016; 23(4): 585–594.
4.
HouRXiaY.Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. J Sound Vibr2021; 491: 115741.
5.
NiYQXiaYLinW, et al. SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data. Smart Struct Syst2012; 10: 411–426.
6.
PengHLiFKanZ.A novel distributed model predictive control method based on a substructuring technique for smart tensegrity structure vibrations. J Sound Vibr2020; 471: 115171.
7.
LiYLuoYZWanHP, et al. Identification of earthquake ground motion based on limited acceleration measurements of structure using kalman filtering technique. Struct Control Health Monit2019; 24(4): 507–524.
8.
NoelABAbdaouiAElfoulyT, et al. Structural health monitoring using wireless sensor networks: A comprehensive survey. IEEE Commun Surv Tutorials2017; 19(3): 1403–1423.
9.
WangYCLuoYZSunB, et al. Field measurement system based on a wireless sensor network for the wind load on spatial structures: design, experimental, and field validation. Struct Control Health Monit2018; 25(9): 18.
10.
ShenYBYangPCLuoYZ.Development of a customized wireless sensor system for large-scale spatial structures and its applications in two cases. Int J Struct Stability Dynamics2016; 16(4): 25.
11.
CandesEJWakinMB.An introduction to compressive sampling. IEEE Signal Process Magazine2008; 25(2): 21–30.
12.
CandesEJTaoT.Decoding by linear programming. IEEE Trans Inform Theory2005; 51(12): 4203–4215.
13.
MiddyaRChakravartyNNaskarMK.Compressive sensing in wireless sensor networks - a survey. IETE Tech Rev2017; 34(6): 642–654.
14.
GaoZDaiLHanS, et al. Compressive sensing techniques for next-generation wireless communications. IEEE Wireless Commun2018; 25(3): 144–153.
15.
PanZYuJHuangH, et al. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images. IEEE Trans Geosc Remote Sens2013; 51(9): 4864–4876.
16.
ZhangYZhangLYZhouJ, et al. A review of compressive sensing in information security field. IEEE Access2016; 4: 2507–2519.
17.
JayawardhanaMZhuXQLiyanapathiranaR, et al. Compressive sensing for efficient health monitoring and effective damage detection of structures. Mech Syst Signal Process2017; 84: 414–430.
18.
HuangYBeckJLWuS, et al. Robust bayesian compressive sensing for signals in structural health monitoring. Comput Aid Civ Infrastruct Eng2014; 29(3): 160–179.
19.
BaoYQShiZQWangXY, et al. Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring. Struct Health Monit2018; 17(4): 823–836.
20.
BaoYQTangZYLiH.Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach. Struct Health Monit2020; 19(1): 293–304.
21.
ChenSXNiYQZhouL.A deep learning framework for adaptive compressive sensing of high-speed train vibration responses. Struct Control Health Monit2022; 29(8): e2979.
22.
WanHPDongGSLuoYZ.Compressive sensing of wind speed data of large-scale spatial structures with dedicated dictionary using time-shift strategy. Mech Syst Signal Process2021; 157: 107685.
23.
WanHPDongGSLuoYZ, et al. An improved complex multi-task Bayesian compressive sensing approach for compression and reconstruction of SHM data. Mech Syst Signal Process2021; 167: 108531.
24.
DongGSWanHPLuoY, et al. A fast sparsity-free compressive sensing approach for vibration data reconstruction using deep convolutional GAN. Mech Syst Signal Process2023; 188: 109937.
25.
ZouZLBaoYQLiH, et al. Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring. IEEE Sens J2015; 15(2): 797–808.
26.
NgeljaratanLMoustafaMAPekcanG.A compressive sensing method for processing and improving vision-based target-tracking signals for structural health monitoring. Comput Aid Civ Infrastruct Eng2021; 36(9): 1203–1223.
27.
HuangYBeckJLLiH.Multitask sparse bayesian learning with applications in structural health monitoring. Comput Aid Civ Infrastruct Eng2019; 34(9): 732–754.
28.
YangYNagarajaiahS.Output-only modal identification by compressed sensing: non-uniform low-rate random sampling. Mech Syst Signal Process2015; 56–57: 15–34.
29.
YangYNagarajaiahS.Robust data transmission and recovery of images by compressed sensing for structural health diagnosis. Struct Control Health Monit2017; 24(1): e1856.
30.
YaoRGPakzadSNVenkitasubramaniamP.Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics. Struct Control Health Monit2017; 24(4): 14.
31.
ZhaoMZhouWHuangY, et al. Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves. Struct Health Monit2021; 20(1): 3–24.
32.
LinJFWuWLHuangJL, et al. An adaptive sparse regularization method for response covariance-based structural damage detection. Struct Control Health Monit2023; 2023: 1–40.
33.
BaoYQLiHChenZC, et al. Sparse l(1) optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges. Struct Control Health Monit2016; 23(1): 144–155.
34.
BaoYQBeckJLLiH.Compressive sampling for accelerometer signals in structural health monitoring. Struct Health Monit2011; 10(3): 235–246.
35.
BaoYQYuYLiH, et al. Compressive sensing-based lost data recovery of fast-moving wireless sensing for structural health monitoring. Struct Control Health Monit2015; 22(3): 433–448.
36.
GanesanVDasTRahnavardN, et al. Vibration-based monitoring and diagnostics using compressive sensing. J Sound Vibr2017; 394: 612–630.
37.
ChenYChiY. Spectral compressed sensing via structured matrix completion. In DasguptaSMcAllesterD (eds.) Proceedings of the 30th international conference on machine learning, proceedings of machine learning research, Atlanta, Georgia, USA: PMLR, vol. 28, pp. 414–422.
38.
LyuFLiFFangX, et al. An enhanced photonic-assisted sampling approach for spectrum-sparse signal by compressed sensing. IEEE Access2022; 10: 55350–55359.
