Adaptive Damage Detection Using Tunable Piezoelectric Admittance Sensor and Intelligent Inference

2014 ◽  
Author(s):  
K. Zhou ◽  
Q. Shuai ◽  
J. Tang
Keyword(s):  
Bayesian Inference ◽  
Damage Detection ◽  
Structural Model ◽  
Experimental Studies ◽  
Frequency Measurement ◽  
Measurement Information ◽  
Accurate Identification ◽  
Measurement Capability ◽  
Uncertainty Measurement ◽  
Inference Network

The piezoelectric impedance/admittance-based damage detection has been recognized to be sensitive to small-sized damage due to its high frequency measurement capability. Recently, a new class of admittance-based damage detection schemes has been proposed, in which the piezoelectric transducer is integrated with a tunable inductive circuitry. The present research focuses on exploiting the tunable nature of the piezoelectric admittance sensor for the effective identification of damage. In particular, we incorporate the Bayesian inference network into the damage detection process which can intelligently guide the accurate identification of damage location and severity by taking full advantage of the baseline model and measurement as well as the online measurement. As the tunable sensor can provide greatly enriched measurement information, the Bayesian inference can adequately utilize such information and furthermore directly and continuously update the structural model until the model prediction matches with the measurement results. This new approach takes into account the model uncertainty, measurement error, and incompleteness of measurements. Extensive numerical analyses and experimental studies are carried out on a panel structure for methodology demonstration and validation.

A Sensitivity-Enhancing Control Approach for Structural Model Updating

2007 ◽  
Author(s):  
L. J. Jiang ◽  
K. W. Wang ◽  
J. Tang
Keyword(s):  
Closed Loop ◽  
Structural Model ◽  
Natural Frequencies ◽  
Model Updating ◽  
Measurement Data ◽  
Frequency Measurement ◽  
Physical Parameters ◽  
Fe Model ◽  
Initial Model ◽  
Closed Loop Systems

Model updating plays an important role in structural design and dynamic analysis. The process of model updating aims to produce an improved mathematical model by correlating the initial model with the experimentally measured data. There are a variety of techniques available for model updating using dynamic and static measurements of the structure’s behavior. This paper focuses on the model updating methods using the measured natural frequencies of the structure. The practice of model updating using only the natural frequencies encounters two well-known limitations: deficiency of frequency measurement data, and low sensitivity of measured natural frequencies with respect to the physical parameters that need to be updated. To overcome these limitations, a novel model updating method is presented in this paper. First, closed-loop control is applied to the structure to enhance the sensitivity of natural frequencies to the updating parameters. Second, by including the natural frequencies based on a series of sensitivity-enhanced closed-loop systems, we can significantly enrich the frequency measurement data available for model updating. Using the natural frequencies of these sensitivity-enhanced closed-loop systems, an iterative process is utilized to update the physical parameters in the initial model. To demonstrate and verify the proposed method, case studies are carried out using a cantilevered beam structure. The natural frequencies of a series of sensitivity-enhanced closed-loop systems are utilized to update the mass and stiffness parameters in the initial FE model. Results show that the modeling errors in the mass and stiffness parameters can be accurately identified by using the proposed model updating method.

Development of an Adjustable Frequency Device for a Test Vehicle Based on Curved Beam Theory

2021 ◽  
Vol 8 ◽  
pp. 5-8
Author(s):  
J. D. Yau ◽  
S. Urushadze
Keyword(s):  
Experimental Studies ◽  
Beam Theory ◽  
Frequency Measurement ◽  
Curved Beam ◽  
Beam Model ◽  
Lumped Mass ◽  
Test Beam ◽  
Test Vehicle ◽  
Instrumented Vehicle ◽  
Thin Beams

In this article, an adjustable frequency device based on curved beam theory is designed to control vertical stiffness of an instrumented vehicle that it can detect dynamic data when moving on a test beam for frequency measurement. The adjustable frequency device consists of a set of two-layer cantilever semi-circular thin-beams to support a lumped mass for vibrations, in which a rotatable U-frame is used to change its subtended angle for adjustment of the supporting stiffness and corresponding vertical frequencies of the vehicle. Based on curved beam theory, an analytical frequency equation of the single-degree-of-freedom test vehicle was derived and applied to mobile frequency measurement of a simple beam. To determine the sectional rigidity of the semi-circular thin-beams, both theoretical and experimental studies were be carried out in the ITAM laboratory of the Academy of Science in Czech. The analytical and experimental results indicated that the present semi-circular beam model with guided ends is applicable to prediction of natural frequencies of the test vehicle considering different supporting stiffness

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

scholarly journals Synthesis and structural characterization of Ca12Ga14O33

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabrina E. A. McCoy ◽  
John R. Salasin ◽  
S. Michelle Everett ◽  
Claudia J. Rawn
Keyword(s):  
Powder Diffraction ◽  
Structural Model ◽  
Electronic Band Structure ◽  
Rietveld Method ◽  
Experimental Studies ◽  
Phase Evolution ◽  
Neutron Powder Diffraction ◽  
Electronic Band ◽  
Wet Chemistry ◽  
X Ray

Abstract Ca12Ga14O33 was successfully synthesized using a wet chemistry technique to promote the homogenous mixing of the Ca and Ga cations. Rietveld refinements on X-ray and neutron powder diffraction data confirm that the compound is isostructural to Ca12Al14O33, however, with a significantly larger lattice parameter allowing for the cages that result from the framework arrangement to expand. In naturally occurring Ca12Al14O33, the mineral mayenite, these cages are occupied by O2− anions, however, experimental studies exchanging the O2− anions with other anions has led to a host of applications, depending on the caged anion. The functional nature of the structure, where framework distortions coupled with cage occupants, are correlated to electronic band structure and modifications to the framework could lead to interesting physical properties. The phase evolution was tracked using thermogravimetric analysis and high temperature X-ray diffraction and showed a lower formation temperature for the Ca12Ga14O33 analogue compared to Ca12Al14O33 synthesized using the same wet chemistry technique. Analyzing both X-ray and neutron powder diffraction using the Rietveld method with two different starting models results in one structural model, with one Ca position and the caged O on a 24d special position, being preferred.

Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

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