Identification Vibration Characteristics of Structures by Operational Modal Analysis (OMA) Technique

It is a very difficult engineering issue to assess the earthquake performance levels of existing masonry structures and determine whether they require strengthening or retrofitting. The materials used in the production of masonry structures vary from region to region, even within the same structure, and the lack of standard mechanical properties of these materials makes it difficult to define the material model in the numerical models of such structures. It is known that these problems are eliminated by the Operational Modal Analysis (OMA) technique because it does not damage the structure and accelerates the process. In the framework of this research, experimental measurement of the 5-story masonry structure was taken with OMA, and numerical modeling was made by SAP2000v14 software. The damping ratio, natural frequencies, and mode shapes of the structure were determined. Frequency-domain-based methods were used in the analysis and evaluation of OMA responses from the Enhanced Frequency Domain Decomposition Method (EFDDM), while the Stochastic Subspace Identification Method (SSIM) was used for time-domain-based methods.

An Investigation of the Seismic Behaviour of an Ancient Masonry Bastion Using Non-Destructive and Numerical Methods

Scaling-factor estimation using an optimized mass-change strategy

Operational Modal Analysis (OMA) is a methodology to obtain the modal properties of a structure using the dynamical responses of the system only. That methodology is very useful in aeroelastic in-fligh testing where there are difficulties in measuring directly the aerodynamic loads on aircraft. For risk reduction in the future flight operations of the EOLO at ITA, wind-tunnel tests were performed. EOLO is an Unmanned Aerial Vehicle (UAV) with high aspect ratio and structural flexibility, designed to study aeroelastic phenomena and to evaluate the interaction of flexible effects with the aircraft flight dynamics. In this work The Frequency Domain OMA techniques: Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD), Spatial and Frequency Domain Decomposition (SFDD) are applied to the vibrational data from wing tunnel test campaign in different operation conditions, using acceleration and strain measurements. The results between the OMA techniques are compared with previous modal characterization from Ground Vibration Test (GVT) and numerical aeroelastic analysis. The different generations of the frequency domain decomposition methods proved their suitability for use in aircraft aeroelastic characterization using accelerations and strain measurements.

\nThis work presents a summary of the research study of operational modal analysis (OMA) application for condition monitoring of operating pipelines. Special focus is on the topicality of OMA for definition of the dynamic features of the pipeline (frequencies and mode shapes) in operation. The research was conducted using two operating laboratory models imitated a part of the operating pipeline. The results of finite-element modeling, identification of pipe natural modes and its modification under the influence of virtual failure are discussed. The work considers the results of experimental research of dynamic behavior of the operating pipe models using one of OMA techniques and comparing dynamic properties with the modeled data. The study results demonstrate sensitivity of modal shape parameters to modification of operating pipeline technical state. It is resumed about ability of operating pipeline condition monitoring by measuring dynamic deformations of the operating pipe and OMA techniques application for dynamic properties extraction.\n

Motivated by a need to reduce energy consumption in wireless sensors for vibration-based structural health monitoring (SHM) associated with data acquisition and transmission, this paper puts forth a novel approach for undertaking operational modal analysis (OMA) and damage localization relying on compressed vibrations measurements sampled at rates well below the Nyquist rate. Specifically, non-uniform deterministic sub-Nyquist multi-coset sampling of response acceleration signals in white noise excited linear structures is considered in conjunction with a power spectrum blind sampling/estimation technique which retrieves/samples the power spectral density matrix from arrays of sensors directly from the sub-Nyquist measurements (i.e., in the compressed domain) without signal reconstruction in the time-domain and without posing any signal sparsity conditions. The frequency domain decomposition algorithm is then applied to the power spectral density matrix to extract natural frequencies and mode shapes as a standard OMA step. Further, the modal strain energy index (MSEI) is considered for damage localization based on the mode shapes extracted directly from the compressed measurements. The effectiveness and accuracy of the proposed approach is numerically assessed by considering simulated vibration data pertaining to a white-noise excited simply supported beam in healthy and in 3 damaged states, contaminated with Gaussian white noise. Good accuracy is achieved in estimating mode shapes (quantified in terms of the modal assurance criterion) and natural frequencies from an array of 15 multi-coset devices sampling at a 70% slower than the Nyquist frequency rate for SNRs as low as 10db. Damage localization of equal level/quality is also achieved by the MSEI applied to mode shapes derived from noisy sub-Nyquist (70% compression) and Nyquist measurements for all damaged states considered. Overall, the furnished numerical results demonstrate that the herein considered sub-Nyquist sampling and multi-sensor power spectral density estimation techniques coupled with standard OMA and damage detection approaches can achieve effective SHM from significantly fewer noisy acceleration measurements.

Cyclostationarity and the cepstrum for operational modal analysis of MIMO systems—Part II: Obtaining scaled mode shapes through finite element model updating

This paper will evaluate different automated operational modal analysis techniques for the continuous monitoring of offshore wind turbines. The experimental data has been obtained during a long-term monitoring campaign on an offshore wind turbine in the Belgian North Sea. State-of-the art operational modal analysis techniques and the use of appropriate vibration measurement equipment can provide accurate estimates of natural frequencies, damping ratios and mode shapes of offshore wind turbines. To allow a proper continuous monitoring the methods have been automated and their reliability improved. The advanced modal analysis tools, which will be used, include the poly-reference Least Squares Complex Frequency-domain estimator (pLSCF), commercially known as PolyMAX, the polyreference maximum likelihood estimator (pMLE), and the frequency-domain subspace identification (FSSI) technique. The robustness of these estimators with respect to a possible change in the implementation options that could be defined by the user (e.g. type of polynomial coefficients used, parameter constraint used…) will be investigated. In order to improve the automation of the techniques, an alternative representation for the stabilization charts as well as robust cluster algorithms will be presented.

This research investigates the dynamic behavior of a railway bridge using both experimental and numerical methods. Field tests were conducted to capture the bridge response to live loading conditions with acceleration data collected via uniaxial accelerometers placed at critical locations along the structure. The dynamic characteristics, including the natural frequencies and mode shapes, were determined using two analytical techniques: Fast Fourier Transform (FFT) and Operational Modal Analysis (OMA). While FFT provides a frequency domain analysis, OMA enables the estimation of modal parameters, such as natural frequencies, mode shapes, and damping ratios, using the bridge's response to operational forces. The results revealed that the fundamental frequency obtained from the OMA (2.163 Hz) was higher than that obtained from the FFT (1.95 Hz) and the Finite Element Analysis (FEA) model (1.65 Hz). Additionally, the OMA produced mode shapes that were closely aligned with those predicted by the FEA, validating the accuracy of the numerical model. This study highlights the advantages of OMA over FFT, particularly the ability to capture mode shapes, and underscores the importance of integrating OMA with FEA for a comprehensive dynamic assessment of bridge structures. These findings contribute to the growing body of knowledge on structural monitoring and provide practical insights into improving bridge safety and performance.

This article presents a damage identification method based on structural natural frequencies and mode shape points. According to the well-posedness analysis of this method, an algorithm is proposed to find the optimal combination of natural frequencies and mode shape points. Then, an integrated scheme that consists of the optimal point selection, the damage identification and the measurement noise updating is presented. In such a way, the damage identification errors due to measurement noises can be greatly suppressed. Numerical examples demonstrate that although structural damage parameters are very sensitive to the measurement noises, especially the noises in natural frequencies, they can be reliably identified if the natural frequencies and mode shape points are properly selected using the proposed method.

Abstract. In the present study we investigate development of operational modal analysis (OMA), different OMA techniques as well as relevant issues and experimental studies. Furthermore, we classify previously performed studies have been conducted so far and consider the most important one. So, first we review the fundamental concepts. Then, the history and application of the OMA method will be examined comprehensively to observe their role in the development of the technique. Three basic methods of FDD, SSI, and Next will be discussed and the research works associated to them will be analyzed.

The vibrational effect is one of the primary considerations during construction due to the ambient excitation from the surrounding environment. Thus, it is necessary to find the most dependable Finite Element (FE) model for the actual structure to improve its quality and life span on site. In this context, the study included a 3-story building model that was analyzed using finite element analysis to determine its dynamic properties such as natural frequency and mode shape to avoid the resonance effect. It is due to the importance of obtaining a reliable and accurate model by verifying with the results of the Operational Modal Analysis (OMA). In general, the goal of this study is to update the FE model so that it is close to the actual scaled model based on the previous OMA study. As a result, an initial linear FE model of the 3-story building scaled model was created using CAD software based on actual geometry to describe the structure's physical properties. The FE model was then imported into CAE software, where the boundary condition and estimated material properties were assigned to determine the effect of random vibration. Through the pairing of Finite Element Analysis results and previous studies, 9 natural frequencies and 9 mode shapes were extracted. The Modal Assurance Criterion (MAC) was used to compare the mode shape of FE results against the OMA to determine the degree of consistency between paired mode shapes. A model updating process was carried out to reduce the discrepancy between the methods. The uncertainties arising from the initial conditions have been discussed in terms of the stiffness of the material used. The updated model allows for an evaluation of the structure's current actions as well as the development of models for a wide range of potential future research scenarios.

Determination of the natural frequencies and mode shapes for large structures by accelerated Newton-Raphson method

In order to derive mass normalized mode shapes from Operational Modal Analysis (OMA) techniques, additional experiments have to be conducted in the interest of scaling the determined mode shapes. Various investigations have been carried out based on the deterministic perturbed mass matrix, also known as the mass change method. However, the conventional form of this method requires a number of rather costly and sometimes impractical experiments on the structure. In this article, it is intended to use traffic as a stochastic source of mass change to calculate mass scaling factors. Adequate traffic stream on bridges can affect the Eigen properties of the structure efficiently. The vehicles on the bridge are only considered to affect the mass properties, and vehicle-bridge interaction is neglected. A simplified structural model of a bridge is considered and the traffic is modeled as lognormal distribution. Nodal masses induced by the vehicles are converted to time histories to avoid difficulties based on the moving mass problem. Consequently, a method is proposed to produce the modified mass matrix which can be used in the scaling factor equations. Finally, scaling factors of mode shapes are proposed by comparing the unperturbed structure and the perturbed mass matrix structure.

Beam mode characterization by applying operational modal analysis to neutron detectors data