STRUCTURAL IDENTIFICATION OF DYNAMIC SYSTEMS USING PARAMETER UPDATING REANALYSIS METHOD: EXPERIMENTAL INVESTIGATION USING THE MEASURED DATA

2016 ◽  
Vol 40 (5) ◽  
pp. 847-857
Author(s):  
Kyoungbong Han ◽  
Dooyong Cho ◽  
Sun Kyu Park
Keyword(s):  
Analytical Model ◽  
Dynamic Systems ◽  
Frequency Response Function ◽  
Dynamic Test ◽  
Structural Identification ◽  
Measured Data ◽  
Actual Structure ◽  
Response Data ◽  
Numerical Tests ◽  
Structural Reanalysis

In this study, the proposed parameter updating reanalysis method was directly used on the measured data of the structures. Structural reanalysis generates a correlated analytical model that defines the structure on applying the initially assumed baseline analytical model and is presented through the structure’s FRF (frequency response function). Two numerical tests were previously conducted in order to demonstrate the effectiveness of the suggested reanalysis method. The suggested method generates the correlated analytical model with higher precision, as compared to the existing method, despite the application of the noise factor to the observed data. The method initially proven by the numerical experiment of an actual structure was applied to a pseudo-dynamic test on the full-scale concrete pier. The results indicate that the proposed reanalysis is useful even for application to response data of the actual structure.

Comparison of Full Field Strain Distributions to Predicted Strain Distributions from Limited Sets of Measured Data for SHM Applications

2013 ◽  
Vol 569-570 ◽  
pp. 1140-1147
Author(s):  
Peter Avitabile ◽  
Eric Harvey ◽  
Justin Ruddock
Keyword(s):  
Analytical Model ◽  
Operating Systems ◽  
Dynamic Stress ◽  
Measured Data ◽  
Analytical Prediction ◽  
Stress Strain ◽  
Full Field ◽  
Response Data ◽  
Field Dynamic ◽  
Field Information

Measured data from operating systems are often acquired to predict structural health of a system. With only a few measurement sensors, very limited understanding of the system can be obtained. Utilizing some recent novel expansion results, full field information can be obtained to identify a more complete strain description for the system. Using these results in conjunction with an analytical prediction of the expected strains, comparisons and differences can be identified using time response data. The expected full field stress-strain from the analytical model is compared to the predicted full field dynamic stress strain from limited sets of measured locations due to either operating or imposed loading on the structure. Differences in strain distributions at many time increments can provide indications of regions of possible damage in the structure. The work presented in this paper identifies the methodology as well as some results to illustrate the usefulness of the approach.

Contactless dynamic tests for analyzing effects of speed and temperature on the natural frequency of a machine tool spindle

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Atsushi Matsubara ◽  
Kohei Asano ◽  
Toshiyuki Muraki
Keyword(s):  
Prediction Model ◽  
Frequency Response Function ◽  
Radial Direction ◽  
Static Load ◽  
Dynamic Stiffness ◽  
Dynamic Test ◽  
Measured Data ◽  
Mode Frequency ◽  
Dynamic Tests ◽  
Outer Race

AbstractThis paper presents a contactless dynamic test for characterizing several effects on the dynamic stiffness, in particular the first mode frequency, in the radial direction of a rigidly preloaded spindle. The effects of the static load, spindle speed, and spindle outer race temperature on the frequency response function (FRF) are investigated independently in different measurement steps. The first mode frequency was estimated from the measured FRF to evaluate the effects. In the test measurement for a spindle with rigid preload, both thermal and speed effects on the first mode frequency were quantitatively evaluated and a simple prediction model based on the measured data is presented.

Use of Measured Mobility to Improve SEA Predictions in the Mid-Frequency Range

1999 ◽  
Author(s):  
Jerome E. Manning
Keyword(s):  
Dynamic Systems ◽  
Broad Band ◽  
Measured Data ◽  
Hybrid Technique ◽  
Complex Dynamic ◽  
Low Frequencies ◽  
Large Variability ◽  
High Frequencies ◽  
Complex Dynamic Systems ◽  
Light Coupling

Abstract Statistical energy analysis provides a technique to predict acoustic and vibration levels in complex dynamic systems. The technique is most useful for broad-band excitation at high frequencies where many modes contribute to the response in any given frequency band. At mid and low frequencies, the number of modes contributing to the response may be quite small. In this case SEA predictions show large variability from measured data and may not be useful for vibroacoustic design. This paper focuses on the use of measured data to improve the accuracy of the predictions. Past work to measure the SEA coupling and damping loss factors has not been successful for a broad range of systems that do not have light coupling. This paper introduces a new hybrid SEA technique that combines measured mobility functions with analytical SEA predictions. The accuracy of the hybrid technique is shown to be greatly improved at mid and low frequencies.

scholarly journals Predictive System of COVID -19 Using Response Based Analytical Model

2021 ◽  
pp. 05-10
Author(s):  
Pallavi Mirajkar ◽  
Rupali Dahake
Keyword(s):  
Analytical Model ◽  
Medical Services ◽  
The Novel ◽  
Response Data ◽  
Proposed Model ◽  
The World

The novel COVID sickness 2019 (COVID-19) pandemic caused by the SARS-CoV-2 keeps on representing a serious and vital threat to worldwide health. This pandemic keeps on testing clinical frameworks around the world in numerous viewpoints, remembering sharp increments in requests for clinic beds and basic deficiencies in clinical equipments, while numerous medical services laborers have themselves been infected. We have proposed analytical model that predicts a positive SARS-CoV-2 infection by considering both common and severe symptoms in patients. The proposed model will work on response data of all individuals if they are suffering from various symptoms of the COVID-19. Consequently, proposed model can be utilized for successful screening and prioritization of testing for the infection in everyone.

S-synchronization Structural Identifiability and Identification of Nonlinear Dynamic Systems

2020 ◽  
Vol 21 (6) ◽  
pp. 323-336
Author(s):  
N. N. Karabutov
Keyword(s):  
Nonlinear System ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Structural Parameters ◽  
Linear Part ◽  
Structural Identification ◽  
Nonlinear Dynamic Systems ◽  
Necessary Condition ◽  
Structural Identifiability ◽  
Nonlinear Part

An approach to the structural identifiability analysis of nonlinear dynamic systems under uncertainty is proposed. We have shown that S-synchronization is the necessary condition for the structural identifiability of a nonlinear system. Conditions are obtained for the design of a model which identifies the nonlinear part of the system. The method is proposed for the obtaining of a set which contains the information on the nonlinear part. A class of geometric frameworks which reflect the state of the system nonlinear part is introduced. Geometrical frameworks are defined on the synthesized set. The conditions are given for the structural indistinguishability of geometric frameworks on the set of S-synchronizing inputs. Local identifiability conditions are obtained for the nonlinear part. We are shown that a non-synchronizing input gives an insignificant geometric framework. This leads to a structural non-identifiability of the system nonlinear part. The method is proposed for the estimation of the structural identifiability the nonlinear part of the system. Conditions for parametric identifiability of the system linear part are obtained. We show that the structural identifiability is the basis for the structural identification of the system. The hierarchical immersion method is proposed for the estimation of nonlinear system structural parameters. The method is used for the structural identification of a system with Bouc-Wen hysteresis.

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