Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

2021 ◽  
pp. 147592172110360
Author(s):  
Erhua Zhang ◽  
Di Wu ◽  
Deshan Shan
Keyword(s):  
System Identification ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Advanced Technique ◽  
Stabilization Diagram ◽  
Bridge Structures ◽  
Tensor Subspace

Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

scholarly journals Automated Harmonic Signal Removal Technique Using Stochastic Subspace-Based Image Feature Extraction

2020 ◽  
Vol 6 (3) ◽  
pp. 10
Author(s):  
Muhammad Danial Bin Abu Hasan ◽  
Zair Asrar Bin Ahmad ◽  
Mohd Salman Leong ◽  
Lim Meng Hee
Keyword(s):  
Feature Extraction ◽  
System Identification ◽  
Output Signal ◽  
Parameters Estimation ◽  
Human Interaction ◽  
Image Feature ◽  
Modal Parameters ◽  
Start Up ◽  
Stabilization Diagram ◽  
Harmonic Components

This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal.

scholarly journals Two-stage Bayesian system identification using Gaussian discrepancy model

2020 ◽  
pp. 147592172093352
Author(s):  
Feng-Liang Zhang ◽  
Siu-Kui Au ◽  
Yan-Chun Ni
Keyword(s):  
System Identification ◽  
Structural Model ◽  
Structural Parameters ◽  
Stage Ii ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Model Parameters ◽  
Modeling Error ◽  
Two Stage ◽  
Classical Statistics

System identification aims at updating the model parameters (e.g. mass and stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass, and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes’ rule without using a heuristic model (often based on classical statistics) that transfers the information from Stages I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this article investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure.

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

On Frequency-Domain System Identification

1997 ◽  
Author(s):  
Eric L. Blades ◽  
Roy R. Craig
Keyword(s):  
System Identification ◽  
Frequency Domain ◽  
Mode Shapes ◽  
Finite Element Models ◽  
Identification Algorithm ◽  
Parameter System ◽  
Modal Damping ◽  
Stiffness Matrices ◽  
Modal Model ◽  
Vibration Tests

Abstract Over the past three decades vibration tests have frequently been conducted on structures and machines for the purpose of validating (updating) finite element models. Such testing is usually referred to as experimental modal analysis. The results of the vibration test are generally presented as a modal model consisting of natural frequencies, mode shapes, and modal damping factors, and the algorithms most frequently used to reduce the test data to modal-model form are time-domain algorithms. Alternatively, direct-parameter system identification may be performed, in which case the direct-parameter model consists of system mass, damping, and stiffness matrices. This paper discusses several features of a new frequency-domain direct-parameter system identification algorithm. Simulations based on a 52-DOF “payload simulator” are used to illustrate the performance of this algorithm.

scholarly journals Delamination Detection of Reinforced Concrete Decks Using Modal Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Shutao Xing ◽  
Marvin W. Halling ◽  
Paul J. Barr
Keyword(s):  
Reinforced Concrete ◽  
Concrete Slab ◽  
Experimental Studies ◽  
Experimental Tests ◽  
Modal Identification ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Delamination Detection ◽  
Global Dynamic

This study addressed delamination detection of concrete slabs by analyzing global dynamic responses of structures. Both numerical and experimental studies are presented. In the numerical examples, delaminations with different sizes and locations were introduced into a concrete slab; the effects of presence, sizes, and locations of delaminations on the modal frequencies and mode shapes of the concrete slab under various support conditions were studied. In the experimental study, four concrete deck specimens with different delamination sizes were constructed, and experimental tests were conducted. Traditional peak-picking, frequency domain decomposition, and stochastic subspace identification methods were applied to the modal identification from dynamic response measurements. The modal parameters identified by these three methods correlated well. The changes in modal frequencies, damping ratios, and mode shapes that were extracted from the dynamic measurements were investigated and correlated to the actual delaminations and can indicate presence and severity of delamination. Finite element (FE) models of reinforced concrete decks with different delamination sizes and locations were established. The modal parameters computed from the FE models were compared to those obtained from the laboratory specimens, and the FE models were validated. The delamination detection approach was proved to be effective for concrete decks on beams.

Effect of Bass Bar Tension on Modal Parameters of a Violin's Top Plate

2015 ◽  
Vol 39 (1) ◽  
pp. 145-149 ◽  
Author(s):  
Ewa B. Skrodzka ◽  
Bogumił B.J. Linde ◽  
Antoni Krupa
Keyword(s):  
Modal Analysis ◽  
Experimental Modal Analysis ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Normal Value ◽  
Modal Damping

Abstract Experimental modal analysis of a violin with three different tensions of a bass bar has been performed. The bass bar tension is the only intentionally introduced modification of the instrument. The aim of the study was to find differences and similarities between top plate modal parameters determined by a bass bar perfectly fitting the shape of the top plate, the bass bar with a tension usually applied by luthiers (normal), and the tension higher than the normal value. In the modal analysis four signature modes are taken into account. Bass bar tension does not change the sequence of mode shapes. Changes in modal damping are insignificant. An increase in bass bar tension causes an increase in modal frequencies A0 and B(1+) and does not change the frequencies of modes CBR and B(1-).

scholarly journals Comparison of Mode Shapes of Carbon-Fiber-Reinforced Plastic Material Considering Carbon Fiber Direction

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 311
Author(s):  
Chan-Jung Kim
Keyword(s):  
Carbon Fiber ◽  
Modal Analysis ◽  
Dynamic Behavior ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Carbon Fiber Reinforced Plastic ◽  
Fiber Direction ◽  
Fiber Reinforced ◽  
Fiber Reinforced Plastic ◽  
Reinforced Plastic

Previous studies have demonstrated the sensitivity of the dynamic behavior of carbon-fiber-reinforced plastic (CFRP) material over the carbon fiber direction by performing uniaxial excitation tests on a simple specimen. However, the variations in modal parameters (damping coefficient and resonance frequency) over the direction of carbon fiber have been partially explained in previous studies because all modal parameters have only been calculated using the representative summed frequency response function without modal analysis. In this study, the dynamic behavior of CFRP specimens was identified from experimental modal analysis and compared five CFRP specimens (carbon fiber direction: 0°, 30°, 45°, 60°, and 90°) and an isotropic SCS13A specimen using the modal assurance criterion. The first four modes were derived from the SCS13A specimen; they were used as reference modes after verifying with the analysis results from a finite element model. Most of the four mode shapes were found in all CFRP specimens, and the similarity increased when the carbon fiber direction was more than 45°. The anisotropic nature was dominant in three cases of carbon fiber, from 0° to 45°, and the most sensitive case was found in Specimen #3.

scholarly journals Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2801
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Three Dimensional ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Material Degradation ◽  
Local Material ◽  
Noisy Input ◽  
Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

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