Finite Element Model Updating for Damage Localisation and Quantification Using Experimental Modal Data

2005 ◽  
Vol 293-294 ◽  
pp. 297-304
Author(s):  
A.S. Kompalka ◽  
S. Reese
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Modal Data ◽  
Additional Equipment ◽  
Structure Vibration ◽  
Experimental Structure

In this contribution we present a validation of an identification procedure and a modeling method with regard to detection, localisation and quantification of damage in a structure. Vibration measurements of an excited experimental structure are used as input for a stochastic subspace system identification algorithm. The identified experimental modal data (eigenvalues and mode shapes) serve to update the underlying finite element model. The experimental setup consists of a cantilever beam and an additional equipment to damage the structure locally and progressively. In contrast to earlier contributions the evolution of damage is quantified in order to estimate the lifetime of the structure.

scholarly journals Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge

2020 ◽  
Vol 9 (2) ◽  
pp. 27 ◽  
Author(s):  
Costas Argyris ◽  
Costas Papadimitriou ◽  
Panagiotis Panetsos ◽  
Panos Tsopelas
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Modal Identification ◽  
Monte Carlo Algorithm ◽  
Mode Shapes ◽  
Model Parameters ◽  
Modal Data ◽  
Acceleration Time Histories

A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors.

Dynamic Characteristics and Finite Element Model Updating of Micromachined Torsion Structures

2013 ◽  
Vol 284-287 ◽  
pp. 1831-1835
Author(s):  
Wei Hsin Gau ◽  
Kun Nan Chen ◽  
Yunn Lin Hwang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Speckle Pattern ◽  
Element Model ◽  
Mode Shapes ◽  
Electronic Speckle Pattern Interferometry ◽  
Finite Element Model Updating ◽  
Element Analysis

In this paper, two experimental techniques, Electronic Speckle Pattern Interferometry and Stroboscopic Interferometry, and two different finite element analysis packages are used to measure or to analyze the frequencies and mode shapes of a micromachined, cross-shaped torsion structure. Four sets of modal data are compared and shown having a significant discrepancy in their frequency values, although their mode shapes are quite consistent. Inconsistency in the frequency results due to erroneous inputs of geometrical and material parameters to the finite element analysis can be salvaged by applying the finite element model updating procedure. Two updating cases show that the optimization sequences converge quickly and significant improvements in frequency prediction are achieved. With the inclusion of the thickness parameter, the second case yields a maximum of under 0.4% in frequency difference, and all parameters attain more reliable updated values.

Impact of Accelerometer Placement on Modal Extraction of Offshore Wind Structures

2020 ◽  
Author(s):  
M. Richmond ◽  
S. Siedler ◽  
M. Häckell ◽  
U. Smolka ◽  
A. Kolios
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Sensor Placement ◽  
Element Model ◽  
Offshore Wind ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Optimal Sensor Placement ◽  
Permanent Campaign

Abstract The modal parameters extracted from a structure by accelerometers can be used for damage assessment as well as model updating. To extract modal parameters from a structure, it is important to place accelerometers at locations with high modal displacements. Sensor placement can be restricted by practical considerations, and installation might be conducted more based on engineering judgement rather than analysis. This leads to the question of how important the optimal sensor placement is, and if fewer sensors suffice to extract the modal parameters. In this work, an offshore wind substation (OSS) from the Wikinger offshore wind farm (owned by Iberdrola) is instrumented with 12, 3-axis accelerometers. This sensor setup consists of 6 sensors in a permanent campaign where sensors were placed based purely on engineering judgement, as well as 6 sensors in a temporary campaign, placed based on a placement analysis. An optimal sensor placement study was conducted using a finite element model of the structure in the software package FEMtools, resulting in optimal layouts. The temporary campaign sensors were placed such that they, in combination with the permanent campaign, can be used to complete the proposed layouts. Samples for each setup are processed using the software ARTeMIS modal to extract the mode shapes and natural frequencies through the Stochastic Subspace Identification (SSI) technique. The frequencies found by this approach are then clustered together using a k-means algorithm for a comparison within clusters. The modal assurance criterion (MAC) values are calculated for each result and compared to the finite element model from which the optimal sensor placement study was conducted. This is to match mode shapes between the two and thus determine the importance of off diagonal MAC elements in the sensor optimization process. MAC values are also calculated relative to a cluster-averaged set of eigenvectors to determine how they vary over the 1.5 months. The results show that for all sensor layouts, the three lower frequency modes are consistently identified. The most optimized sensor layout, consisting of only 3 sensors, was able to distinguish an additional, higher frequency mode which was never identified in the 6-sensor permanent layout. However, the reduced sensor layout shows slightly more scatter in the results than the 6-sensor layout. There is a higher signal to noise ratio in the temporary campaign which results in scatter. We conclude that with an optimized placement of accelerometers, more modes can be identified and distinguished. However, off diagonal elements in the original MAC matrix, as well as loss of sensor degrees of freedom, can result in additional scatter in the measurements. Some of these findings can be extended to other offshore jacket structures, such as those of wind turbines, in that it gives a better understanding of the consequence of an optimal sensor placement study.

Finite Element Model Updating for Unsymmetrical Damping System with Genetic Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 2999-3003 ◽  
Author(s):  
Bao Qiang Zhang ◽  
Guo Ping Chen ◽  
Qin Tao Guo
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Maximum Error ◽  
Optimization Method ◽  
Finite Element Model Updating ◽  
Modal Data ◽  
Complex Modal

Finite element model updating using incomplete complex modal data for unsymmetrical damping system with genetic algorithm is presented. The genetic algorithm method and finite element model updating based on optimization method using complex modal eigenvalue are introduced. The updating for simulation example about a flexible rotor system which is a typical unsymmetrical damping system is performed using bearing stiffness, bearing damping and diameter moment of inertia parameters. The results show that the maximum error of updated parameters is 0.15% and the objective function of genetic algorithm is 0.0081. The study demonstrates that the finite element model updating method using incomplete complex modal data with genetic algorithm is feasible and effective for unsymmetrical damping system.

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