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modal parameter identification of a rotating large umbrella truss structure using digital image correlation

JOURNAL OF SOUND AND VIBRATION 2025年 594卷

作者： Guo, Jiaojiao Wei, Guo Li, Xinyuan Sun, Jialiang Jin, Dongping He, Xiaoyuan Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Southeast Univ Sch Civil Engn Dept Engn Mech Nanjing 210096 Peoples R China

An effective experimental identification of modal parameters of a rotating large umbrella truss structure has been developed which uses Digital Image Correlation (DIC) technique in a framework that integrates with Improved Empirical Wavelet Transform (IEWT) and Random Decrement Technique. In order to offset gravity effect in the ground experiment, an air-bearing-based supporting device is designed to allow a large umbrella truss structure to have a frictionless rotation about its mass center. Burg's algorithm is used to calculate the Standardized Autoregressive power spectrums, according to the full-field displacement of the rotating umbrella truss structure captured by DIC. Using the Standardized Autoregressive power spectrums, the natural frequencies of rotating umbrella truss structure are identified, while the corresponding damping ratios and mode shapes are determined via the nonlinear curve fitting method. Finally, vibratory modal analysis of the rotating large umbrella truss structure was performed at different rotating speeds. The presented technique is anticipated to provide an effective approach for identification of modal parameters of large space structures in practical applications.

关键词： Rotating large umbrella truss structure Digital image correlation Improved empirical wavelet transform modal parameter identification Gravity effect

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modal parameter identification of the Improved Random Decrement Technique-Stochastic Subspace identification Method Under Non-Stationary Excitation

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1398-1398页

作者： Wu, Jinzhi Hu, Jie Ma, Ming Zhang, Chengfei Ma, Zenan Zhou, Chunjuan Sun, Guojun State Key Lab Bldg Safety & Built Environm Beijing 100013 Peoples R China Natl Engn Res Ctr Bldg Technol Beijing 100013 Peoples R China Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China China Acad Bldg Res Co Ltd Beijing 100013 Peoples R China Shaanxi Acad Bldg Res Co Ltd Xian 710082 Peoples R China

Commonly used methods for identifying modal parameters under environmental excitations assume that the unknown environmental input is a stationary white noise sequence. For large-scale civil structures, actual environmental excitations, such as wind gusts and impact loads, cannot usually meet this condition, and exhibit obvious non-stationary and non-white-noise characteristics. The theoretical basis of the stochastic subspace method is the state-space equation in the time domain, while the state-space equation of the system is only applicable to linear systems. Therefore, under non-smooth excitation, this paper proposes a stochastic subspace method based on RDT. Firstly, this paper uses the random decrement technique of non-stationary excitation to obtain the free attenuation response of the response signal, and then uses the stochastic subspace identification (SSI) method to identify the modal parameters. This not only improves the signal-to-noise ratio of the signal, but also improves the computational efficiency significantly. A non-stationary excitation is applied to the spatial grid structure model, and the RDT-SSI method is used to identify the modal parameters. The identification results show that the proposed method can solve the problem of identifying structural modal parameters under non-stationary excitation. This method is applied to the actual health monitoring of stadium grids, and can also obtain better identification results in frequency, damping ratio, and vibration mode, while also significantly improving computational efficiency.

关键词： modal parameter identification non-stationary excitation random decrement technique stochastic subspace identification method RDT-SSI

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Compressive stochastic subspace identification: A modal parameter identification method suitable for compressive sampling

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 223卷

作者： Cao, Jiahui Yang, Zhibo Lu, Minyue Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430064 Peoples R China

Stochastic subspace identification (SSI) and its variants are widely used in conventional structural health monitoring (SHM) owing to their high computation robustness and estimation accuracy. Conventional SHM is based on a Nyquist (high-rate and uniform) sampling dictated by Shannon-Nyquist sampling theorem. As data quantity of SHM is increasing with duration and equipment at an ever-growing rate and new applications of SHM with strict constricts on sampling are emerging one by one, sub-Nyquist sampling, also known as compressive sampling has increasingly been used in SHM in recent years. Therefore, despite the success of SSI method and its algorithmic variants, a significant drawback lies in their handling of sub-Nyquist data. To overcome this drawback, we set out to extend the applicable scenarios of SSI from Nyquist (high-rate/uniform) sampling to sub-Nyquist (low-rate/nonuniform) sampling. In this paper, we propose a compressive stochastic subspace identification (CSSI) which can identify modal parameters from sub-Nyquist/compressed data. Specifically, CSSI involves two aspects: covariance matrix reconstruction and system parameter identification. We find the complete covariance matrix in classic SSI can also be estimated/reconstructed by sub-Nyquist samples. More importantly, this reconstruction can be physically guaranteed through special sampling patterns without sparsity prior. Based on the above findings, we develop a compressed covariance sensing-based covariance matrix reconstruction technique and derive the optimal sub-Nyquist sampling pattern such that the reconstruction is hassle-free and efficient. Then, we perform modal parameter identification on the basis of the reconstructed covariance matrix. Finally, the effectiveness of CSSI is validated by simulations and experiments. In short, CSSI, as a new extension of SSI, can extract modal parameters at lower sampling rates than what is prescribed by the classic SSI. The implication of this is the possib

关键词： Structural health monitoring Operational modal analysis Stochastic subspace identification modal parameter identification Compressive measurement Covariance matrix completion

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Efficient modal parameter identification using DMD-DBSCAN and rank stabilization diagrams

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 161卷

作者： Wu, Chengyuan Yang, Zhichun He, Shun Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Strength & Struct Integr Xian 710072 Peoples R China

modal parameter identification of aerospace structures, particularly improving the efficient using time-domain vibration responses, is currently a topic of great interest in structural dynamics. Data-driven Dynamic Mode Decomposition (DMD) technique in fluid dynamics has shown good potential for identifying the flow mode. In this respect, there have been a few studies on the application of DMD technique in structural dynamics. This paper proposes an efficient structural modal parameter identification method that combines the DMD technique and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, called the DMD-DBSCAN method. Combined with the proposed rank stabilization diagram, the spurious modes produced by the DMD technique can be effectively cleaned by using DBSCAN algorithm to obtain the physical modal parameter. A numerical case of a composite wing model is presented to validate the proposed method, particularly when identifying the closely spaced modes. It has been shown that the rank stabilization diagram is more appropriate for DMD technique when compared with the solely existing sampling frequency stabilization diagram. An experiment on an aircraft model with response from three-dimensional optical technique is used to demonstrate the method's ability to deal with real complex structures. Compared with traditional methods, results show that the proposed DMD-DBSCAN method can accurately identify modal parameters of complex structures and has excellent operational efficiency because it is free from high order Hankel matrix construction. The proposed datadriven modal parameter identification method is efficient and promising for analysis of complex structures with large datasets.

关键词： Dynamic mode decomposition Data-driven Density-based spatial clustering of applications with noise modal parameter identification stabilization diagram

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modal Complexity Factors as indexes for modal parameter identification in Operational modal Analysis of coupled dynamic systems

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JOURNAL OF SOUND AND VIBRATION 2025年 600卷

作者： Ibarrola-Chamizo, J. Agirre-Olabide, I. Merino, M. Aginaga, J. Publ Univ Navarre UPNA Engn Dept Arrosadia Campus Pamplona 31006 Spain Inst Smart Cities ISC Arrosadia Campus Pamplona 31006 Spain

Vibration analysis seeks to extract the modal parameters of a mechanical system by means of experimental measurements. Natural frequencies, damping ratios and mode shapes are identified from the measurements data from experimental or operational modal analysis. modal shapes can show real or complex values. The degree of complexity of a modal shape can be measured by the modal Complexity Factors (MCF). Among others, modal complexity can be due to non-uniformly distributed damping. In complex mechanical systems like a robot, complex modes are expected due to its active and non distributed damping. In turn, in a metallic workpiece real modes are expected. In the robotic machining of thin workpieces, both the robot and the workpiece constitute a coupled dynamic system, operating within the same frequency range. This work proposes the use of MCFs as indexes to determine if each mode corresponds to the workpiece or the robot. Experimental results of an operational modal analysis show a lower mode complexity for the workpiece modes and a higher complexity for the robot frequencies. MCFs show a good performance in separating modes of such coupled systems due to the different damping nature of the robot and the workpiece.

关键词： modal Complexity Factor modal parameter identification Operational modal Analysis Stochastic Subspace identification Robotic machining

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Displacement monitoring and modal parameter identification of cable net structure based on feature optical flow and binocular stereo vision

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STRUCTURES 2025年 76卷

作者： Wu, Jinzhi Ma, Zenan Xue, Yu Qin, Jie You, Deqing Sun, Guojun Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China North China Inst Sci & Technol Architectural Engn Coll Langfang 065201 Peoples R China Beijing Bldg Construct Res Inst Co Ltd Beijing 100039 Peoples R China

Dynamic displacement measurement and modal parameter identification are crucial for structural health monitoring (SHM) and condition assessment. Non-contact monitoring methods based on computer vision have gained attention due to their low cost, ease of installation, and non-invasive nature. However, traditional techniques face challenges in complex dynamic environments, including sensitivity to lighting variations, inconsistent perspectives, and background interference, as well as difficulties in obtaining depth information, limiting their ability to perform synchronous multi-point displacement monitoring and modal identification in large-span spatial structures. This study proposes a feature optical flow method combining ORB(Oriented FAST and Rotated BRIEF) feature matching and optical flow tracking, integrated with stereo vision-based 3D reconstruction technology. By leveraging the rotational and scale invariance of ORB features and the subpixel-level accuracy of the multi-scale Lucas-Kanade (LK) optical flow method, the approach significantly enhances robustness and accuracy. Binocular stereo vision provides spatial displacement data through parallax calculations and 3D reconstruction, enabling high-precision, multi-point synchronous monitoring and modal identification for large-span structures. Applied to scaled model tests of the Xiatian Stadium's cable net structure under environmental, artificial, and swept-frequency excitations, the method demonstrated superior displacement identification accuracy and anti-interference capability compared to traditional methods. The binocular stereo vision system accurately captured modal frequencies and mode shapes across various cable tension conditions, aligning closely with accelerometer data, thereby validating the reliability and precision of the proposed approach.

关键词： Computer vision Feature optical flow method Binocular stereo vision Cable net structure Displacement monitoring modal parameter identification

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modal parameter identification of short span bridges under a moving vehicle by means of multivariate AR model

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2012年第5期8卷 459-472页

作者： Kim, C. W. Kawatani, M. Hao, J. Kyoto Univ Dept Civil & Earth Resources Engn Kyoto 6158540 Japan Kobe Univ Dept Civil Engn Kobe Hyogo 6578501 Japan Construct Project Consultants Inc Gen Consulting Dept Tokyo Japan

This study is intended to investigate the feasibility of health monitoring of short span bridges based on modal parameter identification using the vibration data of the bridge induced by a moving vehicle. The idea of this study using the traffic-induced vibration of short span bridges which is a kind of non-stationary process is that the modal parameters identified repeatedly under a given moving vehicle provide a pattern which may provide useful information to decide current health condition of bridges. The autoregressive (AR) model is adopted for identifying modal parameters such as frequency and damping characteristics of the bridges. Mode shapes of bridges are obtained using multivariate AR model. Feasibility of the modal parameter identification for health monitoring of short span bridges is observed through a moving vehicle laboratory experiment. Pattern changes of identified parameters are observable by comparing the identification results between intact and damaged girders, which encourages the use of the method for long term health monitoring even for short span bridges.

关键词： bridge health monitoring moving vehicle laboratory experiment modal parameter identification autoregressive model short span bridge traffic-induced vibration

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modal parameter identification with compressed samples by sparse decomposition using the free vibration function as dictionary

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SMART STRUCTURES AND SYSTEMS 2020年第2期25卷 123-133页

作者： Kang, Jie Duan, Zhongdong Harbin Inst Technol Shenzhen Sch Civil & Environm Engn Shenzhen Peoples R China

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

关键词： compressive sensing sparse decomposition redundant dictionary orthogonal matching pursuit modal parameter identification

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modal parameter identification of tall buildings based on variational mode decomposition and energy separation

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WIND AND STRUCTURES 2023年第6期37卷 445-460页

作者： Cai, Kang Huang, Mingfeng Li, Xiao Xu, Haiwei Li, Binbin Yang, Chen Zhejiang Univ Coll Civil Engn & Architecture Inst Struct Engn Hangzhou 310058 Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou 310058 Peoples R China Univ Genoa Polytech Sch Dept Civil Chem & Environm Engn Via Montallegro 1 I-16145 Genoa Italy Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ ZJU UIUC Inst Haining 314400 Peoples R China

Accurate estimation of modal parameters (i.e., natural frequency, damping ratio) of tall buildings is of great importance to their structural design, structural health monitoring, vibration control, and state assessment. Based on the combination of variational mode decomposition, smoothed discrete energy separation algorithm -1, and Half -cycle energy operator (VMD-SH), this paper presents a method for structural modal parameter estimation. The variational mode decomposition is proved to be effective and reliable for decomposing the mixed -signal with low frequencies and damping ratios, and the validity of both smoothed discrete energy separation algorithm -1 and Half -cycle energy operator in the modal identification of a single modal system is verified. By incorporating these techniques, the VMD-SH method is able to accurately identify and extract the various modes present in a signal, providing improved insights into its underlying structure and behavior. Subsequently, a numerical study of a four-story frame structure is conducted using the Newmark-beta method, and it is found that the relative errors of natural frequency and damping ratio estimated by the presented method are much smaller than those by traditional methods, validating the effectiveness and accuracy of the combined method for the modal identification of the multimodal system. Furthermore, the presented method is employed to estimate modal parameters of a full-scale tall building utilizing acceleration responses. The identified results verify the applicability and accuracy of the presented VMD-SH method in field measurements. The study demonstrates the effectiveness and robustness of the proposed VMD-SH method in accurately estimating modal parameters of tall buildings from acceleration response data.

关键词： damping ratio energy separation algorithm modal parameter identification natural frequency variational mode decomposition

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modal parameter identification based on combining transmissibility functions and blind source separation techniques

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 105卷 276-293页

作者： Araujo, Ivan Gomez Garcia Sanchez, Jesus Antonio Andersen, Palle Fed Univ Latin Amer Integrat Dept Civil & Infrastruct Engn Ave Silvio Amer Sasdelli BR-1842 Foz Do Iguazu Brazil Univ Fed Itajuba Inst Mech Engn Itajuba Brazil Struct Vibrat Solut AS Aalborg Denmark

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios. (C) 2017 El

关键词： modal parameter identification Blind source separation Transmissibility function modal analysis

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