scholarly journals The role of modal parameters uncertainty estimation in automated modal identification, modal tracking and data normalization

2020 ◽  
Vol 224 ◽  
pp. 111208
Author(s):  
Sérgio Pereira ◽  
Edwin Reynders ◽  
Filipe Magalhães ◽  
Álvaro Cunha ◽  
Jorge P. Gomes
Keyword(s):  
Uncertainty Estimation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Data Normalization ◽  
Parameters Uncertainty ◽  
Automated Modal Identification

scholarly journals Automated Modal Identification Based on Improved Clustering Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Gangrou Wu ◽  
Min He ◽  
Peng Liang ◽  
Chunsheng Ye ◽  
Yue Xu
Keyword(s):  
Structural Damage ◽  
Previous Method ◽  
Modal Identification ◽  
Modal Parameters ◽  
Clustering Method ◽  
Modal Data ◽  
Benchmark Experiment ◽  
Stabilization Diagram ◽  
Automated Modal Identification ◽  
Last Stage

The automated modal identification has been playing an important role in online structural damage detection and condition assessment. This paper proposes an improved hierarchical clustering method to identify the precise modal parameters by automatically interpreting the stabilization diagram. Two major improvements are provided in the whole clustering process. The modal uncertainty is first introduced in the first stage to eliminate as many as possible mathematical modal data to produce more precise clustering threshold, which helps to produce more precise clustering results. The boxplot is introduced in the last stage to assess the precision of the clustering results from a statistical perspective. Based on an iterative analysis of boxplot, the outliers of the clustering results are found out and eliminated and the precise modal results are finally produced. The Z24 benchmark experiment data are utilized to validate the feasibility of the proposed method, and comparison between the previous method and the improved method is also provided. From the result, it can be concluded that the modal uncertainty is more effective than the other modal criteria in distinguishing the mathematical modal data. The modal results by clustering process are not precise in statistic and the boxplot can find out the outliers of the clustering results and produce more precise modal results. The improved automated modal identification method can automatically extract the physical modal data and produce more precise modal parameters.

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Dynamic Property Evaluation of a Long-Span Cable-Stayed Bridge (Sutong Bridge) by a Bayesian Method

2018 ◽  
Vol 19 (01) ◽  
pp. 1940010 ◽  
Author(s):  
Yan-Chun Ni ◽  
Qi-Wei Zhang ◽  
Jian-Feng Liu
Keyword(s):  
Natural Frequency ◽  
Dynamic Characteristics ◽  
Structural Damage ◽  
Dynamic Properties ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Sutong Bridge

Modal identification aims at identifying the dynamic properties including natural frequency, damping ratio, and mode shape, which is an important step in further structural damage detection, finite element model updating, and condition assessment. This paper presents the work on the investigation of the dynamic characteristics of a long-span cable-stayed bridge-Sutong Bridge by a Bayesian modal identification method. Sutong Bridge is the second longest cable-stayed bridge in the world, situated on the Yangtze River in Jiangsu Province, China, with a total length of 2 088[Formula: see text]m. A short-term nondestructive on-site vibration test was conducted to collect the structural response and determine the actual dynamic characteristics of the bridge before it was opened to traffic. Due to the limited number of sensors, multiple setups were designed to complete the whole measurement. Based on the data collected in the field tests, modal parameters were identified by a fast Bayesian FFT method. The first three modes in both vertical and transverse directions were identified and studied. In order to obtain modal parameter variation with temperature and vibration levels, long-term tests have also been performed in different seasons. The variation of natural frequency and damping ratios with temperature and vibration level were investigated. The future distribution of the modal parameters was also predicted using these data.

scholarly journals Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Tianxu Zhu ◽  
Chaoping Zang ◽  
Gengbei Zhang
Keyword(s):  
Parameter Identification ◽  
Noise Reduction ◽  
Flat Plate ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Noise Elimination ◽  
Modal Parameter Identification ◽  
Modal Test ◽  
Svd Method

The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach.

scholarly journals A Method of Modal Parameter Identification for Wind Turbine Blade Based on Binocular Dynamic Photogrammetry

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wenyun Wang ◽  
Xuejun Li ◽  
Anhua Chen
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Modal Identification ◽  
Image Feature ◽  
Vibration Measurement ◽  
Modal Parameters ◽  
Target Point ◽  
Identification Algorithm ◽  
Modal Parameter

The identification of operational modal parameters of a wind turbine blade is fundamental for online damage detection. In this paper, we use binocular photogrammetry technology instead of traditional contact sensors to measure the vibration of blade and apply the advanced stochastic system identification technique to identify the blade modal frequencies automatically when only output data are available. Image feature extraction and target point tracking (PT) are carried out to acquire the displacement of labeled targets on the wind turbine blade. The vibration responses of the target points are obtained. The data-driven stochastic subspace identification (SSI-Data) method based on the Kalman filter prediction sequence is explored to extract modal parameters from vibration response under unknown excitation. Hankel matrixes are reconstructed with different dimensions, so different modal parameters are produced. Similarity of these modal parameters is compared and used to cluster modes into groups. Under appropriate tolerance thresholds, spurious modes can be eliminated. Experiment results show that good effects and stable accuracy can also be achieved with the presented photogrammetry vibration measurement and automatic modal identification algorithm.

scholarly journals Vibration-Based Damage Assessment in Gravity-Based Wind Turbine Tower under Various Waves

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Cong-Uy Nguyen ◽  
So-Young Lee ◽  
Heon-Tae Kim ◽  
Jeong-Tae Kim
Keyword(s):  
Wind Turbine ◽  
Damage Assessment ◽  
Natural Frequencies ◽  
Modal Identification ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Damage Scenarios ◽  
Wind Turbine Tower ◽  
Induced Variation ◽  
Wave Induced

In this study, the feasibility of vibration-based damage assessment in a wind turbine tower (WTT) with gravity-based foundation (GBF) under various waves is numerically investigated. Firstly, a finite element model is constructed for the GBF WTT which consists of a tower, caisson, and foundation bed. Eigenvalue analysis is performed to identify a few vibration modes of interest, which represent complex behaviors of a flexible tower, rigid caisson, and deformable foundation. Secondly, wave-induced dynamic pressures are analyzed for a few selected wave conditions and damage scenarios are also designed to simulate the main components of the target GBF WTT. Thirdly, forced vibration responses of the GBF WTT are analyzed for the wave-induced excitation. Then modal parameters (i.e., natural frequencies and mode shapes) are extracted by using a combined use of time-domain and frequency-domain modal identification methods. Finally, the variation of modal parameters is estimated by measuring relative changes in natural frequencies and mode shapes in order to quantify the damage-induced effects. Also, the wave-induced variation of modal parameters is estimated to relatively assess the effect of various wave actions on the damage-induced variation of modal parameters.

The Modal Identification of Structure Using Distributed ERA and EFDD Methods

2010 ◽  
Vol 163-167 ◽  
pp. 2532-2536
Author(s):  
Ying Lei ◽  
Zhi Lu Lai
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Volume ◽  
Modal Identification ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Civil Infrastructure ◽  
Complicated Structure ◽  
Data Set ◽  
Structural Health

Structural health monitoring (SHM) is an emerging field in civil engineering, offering the potential for continuous and periodic assessment of the safety and integrity of civil infrastructure. In this paper, a distributed computing strategy for modal identification of structure is proposed, which is suitable for the problem of solving large volume of data set in structural health monitoring. Numerical example of distribute computing the modal properties of truss illustrates the distributed out-put only modal identification algorithm based on NExT / ERA techniques and EFDD. This strategy can also be applied to other complicated structure to determine modal parameters.

Modal Identification of an Electrical Submersible Pump Installed in a Test Well Using Drop Tests

2018 ◽  
Author(s):  
U. A. Monteiro ◽  
R. S. Minette ◽  
R. H. R. Gutiérrez ◽  
L. A. Vaz
Keyword(s):  
Petroleum Industry ◽  
Modal Identification ◽  
Modal Testing ◽  
Modal Parameters ◽  
Premature Failure ◽  
Flow Rates ◽  
Manufacturing Defects ◽  
Drop Tests ◽  
Frequency Domain Decomposition ◽  
Gas Lift

Electrical submersible pumps (ESPs) are used in the petroleum industry to pump large amount of fluids from subsea deep wells. Currently, ESPs are responsible for approximately 10% of the world’s crude oil production. When high flow rates are required, gas lift and the ESP are the only available technologies that can be applied. ESP systems are much more complex and less reliable than the gas lift method, but they are more efficient and able to yield higher flow rates and pressure. ESPs are installed inside or near production wells, with the entire auxiliary infrastructure for power supply and control system. This means that maintenance is prohibitive because of production losses and the need for expensive and unavailable drill ships. Assembly errors and manufacturing defects must be avoided to make the method feasible and to prevent premature failure and, this goal, can only be achieved through rigorous quality control, operations procedure, qualification and dynamic tests. In this paper, modal parameters of an ESP installed in a test well were experimentally identified using the Enhanced Frequency Domain Decomposition (EFDD) technique. Drop tests were used to excite the ESP and vibration signals were acquired along the equipment’s structure. Results were compared with modal parameters obtained using impact tests and showed that the drop test is an useful method for modal testing of ESP’s in test well.

Modal Identification of Structures from Seismic Response Data via Amplitude-Dependent Time Series Model

2013 ◽  
Vol 479-480 ◽  
pp. 1155-1159
Author(s):  
Wei Chih Su ◽  
Chiung Shiann Huang ◽  
Ching Yu Liu
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Modal Identification ◽  
Shaking Table ◽  
Modal Parameters ◽  
Arx Model ◽  
Response Data ◽  
Input Amplitude ◽  
Undetermined Coefficient ◽  
Physical Phenomena

The present work develops a novel procedure of establishing a amplitude-dependent time series model for a nonlinear system and estimating the instantaneous modal parameters of the system from the dynamical responses. The undetermined coefficient in a amplitude-dependent autoregressive with exogenous input (amplitude-dependent ARX) model are assumed as function s of amplitude and are expanded by shape functions constructing by moving least-squares with polynomial basis functions. The amplitude of dynamical responses could be obtained by Hilbert transform. The instantaneous modal parameters of the system are directly estimated from the coefficient in the amplitude-dependent ARX model. Finally, the proposed approach is applied to process measured data for a frame specimen subjected to a series of base excitations in shaking table tests. The specimen was damaged during testing. The identified modal parameters are consistent with observed physical phenomena.

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