Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements.

Near-to-node modal identification using multiple related response models

The paper deals with an experimental and a numerical investigation of dynamic properties of the structure. Object of interest is a steel rod with circle section. The modal parameters, such as mode shapes and natural frequencies, are obtained by two different ways: experimentally by using experimental modal analysis and numerically by finite element analysis which were computed in NX Nastran solver. The results from both methods are compared in NX through the correlation function. A relation between measured mode shapes and computed mode shapes is expressed by Modal Assurance Criterion. This process of comparing is appropriate for verification of experimental approaches.

As all experimental procedures, Experimental Modal Analysis (EMA) is subject to a wide range of potential testing and processing errors. The modal identification methods are sensitive to these errors, yielding modal results which are uncertain up to certain error bounds. The question hence is what these error bounds on test data and modal parameters are. In this paper, the studied source of uncertainty is related to the variance (noise) on the Frequency Response Function (FRF) measurements. Under the H1 assumptions and in single-input cases, the FRF variances can be computed from the coherences and the FRFs. In multiple-input cases, some more measurement functions are required. Advanced system identification methods like the Maximum Likelihood Estimator (MLE) and PolyMAX Plus have the possibility to take the uncertainty on the measurement data into account and to propagate the data uncertainty to (modal) parameter uncertainty. This paper will review FRF variance estimation techniques, including some pragmatic approaches. The basic concepts of Maximum Likelihood Estimation and the calculation of confidence bounds will be discussed. Some typical structural testing and modal analysis cases will be used as illustration of the discussed concepts.

Modal parameter identification is an important content for the research on dynamic behavior of civil engineering structures. For the conventional modal parameter identification techniques, excitation signal and response signal are necessary. But excitations can not be measured for structures under natural loading conditions, or excited by random forces. So, the operational modal parameter identification approaches are developing to identify structural modal parameters by using vibration response signal directly. In this paper, a new method for modal parameter identification depending on ambient excitation response signal is proposed based on independent component analysis (ICA). For modal coordinates independent of each other, ICA can extract modal coordinates of structure and estimate mode shape matrix using vibration response signal directly. The free vibration responses can be obtained by random decrement technique (RDT) from modal coordinates which are random processes, and then the natural frequencies and damping ratios are calculated by Hilbert transform. Numerical simulation results indicate that this method is effective and suitable for output-only structural modal parameter identification.

Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified.

Experimental method for modal parameter identification of civil engineering structures based on improved wavelet transform

<p>This report consists of experimental and Finite element-based numerical modal analysis of a 3D printed PLA cantilever beam with a nonuniform delamination of 0.5mm, carried out as part of an MEng project. Experimental modal analysis is performed on four different samples of different delamination thicknesses, including the one with a delamination of 0.5mm, to obtain the system's bending fundamental frequency. Numerical analysis, however, is focused and conducted on only the defective specimen with a non-uniform delamination of 0.5mm. In this case, the delamination is sketched with the help of an electronic microscope which takes the images of the delamination zone. These images are then used to plot the non-uniform delamination on the 3D model, focusing on obtaining the model that is as close as possible to the manufactured specimen. The experimental frequencies are subsequently compared with the numerical values obtained from the Finite Element Analysis (FEA) of the 3D model. In numerical analysis, a progressive approach was used to create two sophisticated models referred to as refined and further refined models. The numerical fundamental frequency obtained from these last models are compared with that of the PLA beam with uniform delamination of 0.5mm. It is observed that by applying progressive refinement in the FEM model, the numerical fundamental bending frequency increasingly converges to the experimental one. Appendix A consists of the images taken by the microscope, which show the non-uniform geometry of the delamination zone.</p>

The instrumented Canton Tower is a 610 m high-rise structure, which has been considered as a benchmark problem for structural health monitoring (SHM) research. In this paper, an improved automatic modal identification method is presented based on a natural excitation technique in conjunction with the eigensystem realization algorithm (NExT/ERA). In the proposed modal identification method, damping ratio, consistent mode indicator from observability matrices (CMI_O) and modal amplitude coherence (MAC) are used as criteria to distinguish the physically true modes from spurious modes. Enhanced frequency domain decomposition (EFDD), the data-driven stochastic subspace identification method (SSI-DATA) and the proposed method are respectively applied to extract the modal parameters of the Canton Tower under different environmental conditions. Results of modal parameter identification based on output-only measurements are presented and discussed. User-selected parameters used in those methods are suggested and discussed. Furthermore, the effect of environmental conditions on the dynamic characteristics of Canton tower is investigated.

Model updating is concerned about the correction of finite element models by processing the record of dynamic response from test structures in order to have an accurate model for any simulated analysis. Finite element model updating had emerged years ago as an important subject in structural dynamics. It has been used frequently and has been successfully applied to many fields especially in detecting the dynamic stiffness of a structure. The purpose of this study is to perform model updating of a go-kart chassis structure in order to reduce the percentage of error between the experimental modal analysis (EMA) and finite element analysis (FEA). Modal properties (natural frequency, mode shapes, and damping ratio) of the go-kart chassis structure were determined using both EMA and FEA. Correlation of the modal parameters gathered in FEA and EMA was carried out before optimizing the data from finite element. By adjusting the selective parameters, incongruities between those two analyses are generally reduced. The sensitivity of selected parameters is also obtained. The significant reduction in percentage of error before and after model updating procedure was carried out in this study clearly shows that model updating technique is a reliable method in reducing the discrepancies between EMA and FEA. Therefore, in cases of high discrepancies between analytical and actual test data, model updating can be considered as an option in order to obtain better correlation between those two sets of data.

HighlightsThe frequency domain modal parameters identification method was applied to a ginkgo tree.Dynamic characteristics of the ginkgo tree were tested during five phenological periods.Almost all resonance frequencies were near the peaks of the frequency response curves.Leaves caused the number of natural frequencies of the ginkgo tree to be greatly reduced.Abstract. Understanding the dynamic characteristics of fruit trees is the premise of effective mechanized harvesting. This study performed a tracking test on a ginkgo tree in five phenological periods from the dormant period to the leaf-unfolding period. The frequency domain modal parameters identification method was applied to the ginkgo tree, and the relationship between the natural frequencies and resonance frequencies of the ginkgo tree was obtained. The main factors affecting the fundamental frequency and damping ratio of the ginkgo tree were not the elastic modulus and moisture content but rather the growth of the leaves. The growth of leaves caused the number of natural frequencies in the low-frequency band to be greatly reduced, and the value of the natural frequencies exhibited a slightly decreasing tendency. The damping caused by leaves had a significant weakening effect on the transmission of vibrational energy on the lateral branches. The resonance frequencies that caused strong response of the ginkgo tree were mostly near the peak frequencies of the frequency response curves (natural frequencies), but eccentric motor excitation could not effectively stimulate all the natural frequencies of the ginkgo tree to reach resonance. In the frequency response curves of the ginkgo tree, the main natural frequency with the maximum energy might not cause the maximum vibration response of the ginkgo tree, even if this excitation frequency could induce resonance. Resonance could be used to maximize the transfer of excitation energy, but each position of the tree had its own independent frequency spectrum characteristics. A single excitation frequency could not cause all positions of the ginkgo tree to resonate simultaneously. Changing the excitation frequency of harvesting equipment within a small frequency range could achieve the maximum resonance response of most positions on fruit trees. Keywords: Dynamic characteristics, Growth periods, Leaves, Natural frequencies, Resonance.

Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.

Structural modal parameter identification with the Power-Exponential window function

Modal parameter identification via ambient vibration is popular but faces challenges from uncertainties due to unknown inputs and low signal‐to‐noise ratio. Bayesian methods are gaining increasing attention for operational modal identification due to their ability to quantify uncertainties. However, improvements in computational efficiency are needed, particularly when addressing numerous modes and degrees of freedom. To address this challenge, this study proposes an innovative approach, termed the “Bayesian spectral decomposition” method (BSD), employing the decompose‐and‐conquer strategy. This novel method, operating within the frequency domain, identifies each mode individually by exploiting their inherent separated modal characteristics. For each mode, the response spectrum matrix undergoes an eigenvalue decomposition, yielding crucial eigenvalues (incorporating frequency and damping information) and eigenvectors (containing mode shape information). Subsequently, statistical properties of the eigenvalues and eigenvectors are utilized to establish likelihood functions for Bayesian parameter identification. By combining prior information, the posterior probability distribution functions of modal parameters are derived. The optimal solution is then obtained by resolving the maximum posterior probability distribution function problem. To further quantify the uncertainty of modal parameters, Gaussian distributions are employed to approximate the posterior probability distribution functions. The adoption of the decomposition approach circumvents the joint identification of all modal parameters, substantially reducing the parameter dimensions for optimization. Consequently, this strategy leads to decreased computational complexity and significantly improved computational stability. The effectiveness of the BSD is confirmed through simulated data generated from an 8‐story shear building as well as measured data collected from both an experimental shear frame and the Canton Tower. The results demonstrate that the proposed method achieves high accuracy in identifying modal parameters, greatly improves computational efficiency, and effectively quantifies the uncertainties in modal parameters.

Viscoelastic materials are used in vibration damping applications as they have the ability to dissipate energy, they are used in machine basis, bridges and many other applications. In this paper four plates with the same thickness but different material lamination are investigated for their modal properties. Namely,natural frequencies, mode shapes and damping loss factors. Experimental modal analysis is carried out using impact hammer for excitation and accelerometer for measurement. Data is processed using computer package to obtain frequency response function (FRF) for each plate and modal parameters are extracted from the plot. Results are compared to finite element model that was created using ANSYS package. It showed very well agreement between experimental and finite element. Damping effect on the shift in natural frequencies was obvious due to the viscoelastic material thickness in the laminated plates. It is concluded that the finite element analysis using ANSYS software is a powerful tool in predicting natural frequencies and mode shapes even in laminated plates with viscoelastic core, it is also confirmed the effect of damping increase due to the use of viscelastic cores on the plate natural modes and vibration behaviour.

Vision-based modal parameter identification for bridges using a novel holographic visual sensor