Structural damage identification with limited modal measurements and ultra‐sparse Bayesian regression

Xu, Mingqiang; Guo, Jian; Wang, Shuqing; Li, Jun; Hao, Hong

doi:10.1002/stc.2729

Cited by 17 publications

(6 citation statements)

References 60 publications

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“…However, the majority of the existing modal-based methods are still subject to incomplete modal information, 49,50 environment noise, 51 and ill-conditioned solutions of overdetermined linear equations, 46 etc. These factors not only increase the computational complexity but also reduce the robustness of calculation methods.…”

Section: Structure Health Monitoring Of Offshore Jacket Structuresmentioning

confidence: 99%

Condition-based structural health monitoring of offshore wind jacket structures: Opportunities, challenges, and perspectives

Leng

Gardoni

Wang

et al. 2023

Structural Health Monitoring

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Structural health monitoring (SHM) has been recognized as a useful tool for safety management and risk reduction of offshore wind farms. In complex offshore environment, jacket structures of offshore wind turbines are prone to damages due to corrosion and fatigue. Effective SHM on jacket structures can substantially reduce their operation risk and costs. This work reviews the latest progress on the SHM of offshore wind jacket structures. The achievements in the structural damage identification, location, quantification, and remaining useful life (RUL) estimation are respectively introduced in detail, and existing challenges are discussed. Possible solutions to the challenges using the Digital Twin (DT) technology are put forward. The DT is able to mirror a real jacket structure into a virtual model, and Bayesian updating can refresh the virtual model parameters in real-time to keep consistency between virtual model and physical structure; then, just-in-time SHM can be carried out for jacket structures by performing damage detection, location, quantification, and RUL estimation using the virtual model. As a result, the DT may provide engineers and researchers a practicable tool for safety monitoring and risk reduction of fixed foundation offshore wind structures.

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Section: Structure Health Monitoring Of Offshore Jacket Structuresmentioning

confidence: 99%

Condition-based structural health monitoring of offshore wind jacket structures: Opportunities, challenges, and perspectives

Leng

Gardoni

Wang

et al. 2023

Structural Health Monitoring

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“…By considering that the gradually varying physical parameters are expected to be sparse in the systems, 47 it is unnecessary to consider that all physical parameters are time‐varying and then expanded into corresponding numerical coefficients. It is more feasible and effective to distinguish the time‐varying parameters qualitatively and then concentrate on identifying those time‐varying parameters.…”

Section: The Proposed Approach I: Two‐step Approach To Identify the G...mentioning

confidence: 99%

Identification of gradually varying physical parameters based on discrete cosine transform using partial measurements

Yang

Lei

et al. 2022

Structural Contr & Hlth

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Structural physical parameters often vary gradually due to the degradation of material properties or effects of environment. In this paper, two novel approaches are proposed to identify the gradually varying physical parameters based on the discrete cosine transform (DCT) using partial measurements of structural responses. Approach I is proposed for the circumstance of known excitations. The gradually varying physical parameters are first located by the fading-factor extended Kalman filter (FEKF) and then identified by the proposed DCT integrated with Kalman filter (KF) method. Approach II is proposed for the identification of gradually varying physical parameters under unknown excitations. The gradually varying physical parameters are first localized by the proposed fading-factor extended Kalman filter under unknown input (FEKF-UI) and then identified by the proposed DCT integrated with Kalman filter under unknown input (KF-UI). Numerical examples demonstrate that the proposed approaches can identify the gradually varying physical parameters accurately with incomplete measurement data. Moreover, the identification of time-varying cable force in cable-stayed bridge is also discussed as a case study of the proposed approach I. Experimental verification shows that it provides a new path to identify the time-varying cable force by only using one acceleration response measurement of the cable.

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“…The family of SBL-based methods witnessed various applications, including system identification in applied mechanics 41,42 and damage detection in civil engineering. 43,44 However, Bayesian time series analysis enabled by the SBL scheme has not been much explored in the field of civil engineering for structural response forecasting.…”

Section: Introductionmentioning

confidence: 99%

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Wang

Yang

et al. 2023

Structural Health Monitoring

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Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

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Structural damage identification with limited modal measurements and ultra‐sparse Bayesian regression

Abstract: This paper proposes a novel approach for structural damage identification with a limited number of modal measurements and ultra-sparse Bayesian regression. An iterative Cross Modal Strain Energy (CMSE) method is proposed for establishing the linear regression model. It can be applied to

Cited by 17 publications

References 60 publications

Condition-based structural health monitoring of offshore wind jacket structures: Opportunities, challenges, and perspectives

Condition-based structural health monitoring of offshore wind jacket structures: Opportunities, challenges, and perspectives

Identification of gradually varying physical parameters based on discrete cosine transform using partial measurements

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

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