Implementation of Parametric Methods for the Treatment of Uncertainties in Online Identification

Chatzi, Eleni; Spiridonakos, Minas D.; Smyth, Andrew W.

doi:10.1007/978-3-319-32077-9_3

Cited by 7 publications

(8 citation statements)

References 52 publications

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“…Several methods for damage identification, 4 including data acquisition (DAQ), signal processing tools, and feature extraction, have been proposed. Damage detection should be based on a non-destructive testing (NDT) method, such as acoustic emission, 5 Lamb waves, 6 and fiber optic sensors, 7 to be minimally invasive to the monitored structure.…”

Section: Introductionmentioning

confidence: 99%

Impedance-based damage detection under noise and vibration effects

Campeiro

Silveira

Baptista

2017

Structural Health Monitoring

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The electro-mechanical impedance technique has been extensively studied in recent decades as a non-destructive method for detecting structural damage in structural health monitoring applications using low-cost piezoelectric transducers. Although many studies have reported the effectiveness of this detection method, numerous practical problems, such as the effects of noise and vibration, need to be addressed to enable this method's effective use in real applications. Therefore, this article presents an experimental analysis of noise and vibration effects on structural damage detection in impedance-based structural health monitoring systems. The experiments were performed on an aluminum bar using two piezoelectric diaphragms, where one diaphragm was used to measure the electrical impedance signatures and the other diaphragm was used as an actuator to generate noise and controlled vibration. The effects of noise and vibration on impedance signatures were evaluated by computing the coherence function and basic damage indices. The results indicate that vibration and noise significantly affect the threshold of the lowest detectable damage, which can be compensated by increasing the excitation signal of the piezoelectric transducer.

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Section: Introductionmentioning

confidence: 99%

Impedance-based damage detection under noise and vibration effects

Campeiro

Silveira

Baptista

2017

Structural Health Monitoring

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“…1,2 Therefore, different vibration signal feature-based methods have been proposed. [3][4][5][6] However, applicability for complex structures, requirement of baseline data, neglect of nonlinear boundaries, and accuracy of localization of local faults, etc., are always challenging issues for limiting real application of those developed methods and thus present increasing demands on new technical innovation in related areas.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Both potential faults and nonlinear boundary constraints are simulated as nonlinear connecting elements in the T-type MDOF model. (3) Extracting outputs of the model under excitations with different amplitudes and phases, novel fault features are defined with nonlinear fault-induced loads and transmissibility functions. (4) Applying fault features from the substructure to be evaluated only, a novel fault index is obtained with Euclidean distance measurement, and its effectiveness is demonstrated by experimental studies on a lab bolted grid-like structure.…”

Section: Introductionmentioning

confidence: 99%

A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures

Liao

et al. 2023

Structural Health Monitoring

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Complex grid-like structures such as steel truss bridges and steel truss roofs commonly exist in civil engineering applications. Since these structures are usually subject to dynamic operational forces, faults like chemical corrosion, fatigue crack, and bolt loosening arise, and seriously affect structural health. To consider some issues in existing frequency domain fault evaluation methods like applicability for complex structures, requirement of baseline data, neglect of nonlinear boundaries, and localization of local faults, a novel method using nonlinear vibration features and Euclidean distance measurement is presented in this paper. Firstly, complex grid-like structures are decomposed into a series of simple T-type substructures according to structural characteristics and load paths. Secondly, related T-type multi-degree-of-freedom model is built by simulating potential faults and nonlinear boundaries as related nonlinear damper-spring connections. Thirdly, exciting the model and extracting output responses only, novel and local fault features, which are functions of structural properties, nonlinear fault-induced loads and transmissibility functions, are defined. Then, utilizing defined features from the local substructure to be evaluated only, a novel fault index is defined with Euclidean distance measurement. Finally, corresponding evaluation method is developed, and its effectiveness and applicability are demonstrated by comparative studies on a lab bolted grid-like structure.

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“…Slight variations in these covariance matrices might adversely affect the estimation results and lead to significant uncertainties in the estimations. Since these predictions form the basis for monitoring-driven decision-making, the quantification of those uncertainties is of high importance [6].…”

Section: Introductionmentioning

confidence: 99%

Tuning of Kalman Filter Noise Parameters for Uncertainty Quantification in Input-State Estimation of Mdof Systems

Panias,

Caglio,

Glavind

et al. 2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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The current research work deals with uncertainty quantification aspects in the problem of joint input-state estimation in structural dynamics. Specifically, it focuses on methodologies that can facilitate the tuning of the noise covariance matrices within the framework of Bayesian filtering techniques. These covariance matrices reflect the uncertainties of the estimation scheme and their proper calibration can reinforce the reliability of the estimated dynamic response. In this work, the performance of two approaches is investigated in the case of linear systems. First, a state-of-the-art methodology from the literature called Bayesian Expectation Maximization is implemented. The purpose of this optimization scheme is to identify the optimal noise covariance matrices based on the available observations of the dynamic response quantities. After evaluating the performance of this methodology, an adaptive time-varying noise Augmented Kalman Filter is proposed for updating the noise characteristics. The proposed scheme is expected to reduce the uncertainty of the input-state estimation. The two methods are applied on a 2D multi-story and multi-bay steel moment resisting frame subjected to earthquake-induced ground excitation. The performance of the different methods is evaluated and discussed.

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Implementation of Parametric Methods for the Treatment of Uncertainties in Online Identification

Cited by 7 publications

References 52 publications

Impedance-based damage detection under noise and vibration effects

Impedance-based damage detection under noise and vibration effects

A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures

Tuning of Kalman Filter Noise Parameters for Uncertainty Quantification in Input-State Estimation of Mdof Systems

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