Structural damage detection using incomplete modal data and incomplete static response

Kourehli, Seyed Sina; Bagheri, Abdollah; Amiri, Gholamreza Ghodrati; Ghafory‐Ashtiany, Mohsen

doi:10.1007/s12205-012-1864-2

Cited by 30 publications

(23 citation statements)

References 25 publications

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“…() In the presence of noise, a Kalman filter (KF)‐type observer can be used to obtain accurate estimates but with added computational costs. Likewise, methods have been developed to overcome heavy computation such as the uniform robust exact observers,() objective functions formulated from modal data,() and self‐tuning fusion KFs. () Through these efforts, it is undeniable that modern research in state estimation has been geared towards producing faster and more efficient observers for systems with various levels of complexity.…”

Section: Introductionmentioning

confidence: 99%

Study of input space for state estimation of high‐rate dynamics

Hong

Laflamme

Dodson

2018

Struct Control Health Monit

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Summary High‐rate dynamic systems are defined as systems being exposed to highly dynamic environments that comprise high‐rate and high‐amplitude events. Examples of such systems include civil structures exposed to blast, space shuttles prone to debris strikes, and aerial vehicles experiencing in‐flight changes. The high‐rate dynamic characteristics of these systems provides several possibilities for state estimators to improve performance, including a high potential to reduce injuries and save lives. In this paper, opportunities and challenges that are specific to state estimation of high‐rate dynamic systems are presented and discussed. It is argued that a possible path to design of state estimators for high‐rate dynamics is the utilization of adaptive data‐based observers but that further research needs to be conducted to increase their convergence rate. An adaptive neuro‐observer is designed to examine the particular challenges in selecting an appropriate input space in high‐rate state estimation. It is found that the choice of inputs has a significant influence on the observer performance for high‐rate dynamics when compared against a low‐rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision‐making process in a closed‐loop system in making bad judgments.

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Section: Introductionmentioning

confidence: 99%

Study of input space for state estimation of high‐rate dynamics

Hong

Laflamme

Dodson

2018

Struct Control Health Monit

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“…High-rate dynamic systems include added complexities such as large uncertainties in the external loads, high levels of nonstationarities and disturbances, and generation of unmodeled dynamics from rapid changes in mechanical configurations. These complexities have been considered individually in Ref., [9][10][11][12][13][14][15] however, they have yet to be considered altogether.…”

Section: Introductionmentioning

confidence: 99%

Variable input observer for state estimation of high-rate dynamics

et al. 2017

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High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO's potential, a VIObased neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy. KeywordsHigh-rate dynamics, input space, adaptive observer, neural network, structural health monitoring ABSTRACT High-rate systems operating in the 10 µs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO's potential, a VIObased neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.

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“…Li et al 5 developed the cross-model cross-mode (CMCM) method for damage detection that is capable of identifying the damage to individual member of o®shore jacket platforms, when limited, spatially incomplete modal data were available. Also, Kourehli et al [6][7][8] presented the damage detection and estimation methods for structures based on incomplete modal data and incomplete static responses of a damaged structure via pattern search algorithm and simulated annealing algorithm. In other work, Kourehli 9 applied the arti¯cial neural network and incomplete noisy modal data to structural damage detection and estimation.…”

Section: Introductionmentioning

confidence: 99%

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

Kourehli

2016

Int. J. Str. Stab. Dyn.

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A damage detection and estimation method is proposed for structural health monitoring using incomplete modal data and least squares support vector machine (LS-SVM). To accommodate the use of incomplete modal data, the iterated improved reduction system (IIRS) method has been used to condense the mass and sti®ness matrices of the structure. The¯rst two incomplete mode shapes and natural frequencies of a damaged structure are used as input data to the LS-SVM. The coupled simulated annealing (CSA) and standard simplex method using 10-fold cross-validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM model. Three illustrative examples with and without noise in modal data are prepared to evaluate the performance of the proposed method. The results indicated that this method can be reliably used to identify the damages of structures with good accuracy. Int. J. Str. Stab. Dyn. Downloaded from www.worldscientific.com by FUDAN UNIVERSITY on 05/15/15. For personal use only.

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Structural damage detection using incomplete modal data and incomplete static response

Cited by 30 publications

References 25 publications

Study of input space for state estimation of high‐rate dynamics

Study of input space for state estimation of high‐rate dynamics

Variable input observer for state estimation of high-rate dynamics

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

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