Reduction of uncertainty effect on damage identification using feedback control

Lew, Jiann-Shiun

doi:10.1016/j.jsv.2008.04.037

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Recently, the idea of using feedback control to enhance the sensitivity of damage indices with respect to changes in structural sti®ness has been proposed, with some initial success. [1][2][3][4][5][6][7][8][9] Sensitivity-enhancing control (SEC) exploits the relationship between feedback control gains and classic measures of sensitivity in order to enhance shifts in characteristic closed-loop frequencies or shifts in characteristic closed-loop mode shapes when damage occurs. Ray and Tian 1 introduced the concept of SEC for application to smart structures.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Detection Using Sensitivity-Enhanced Autoregressive Coefficients

Wang

Liu

et al. 2016

Int. J. Str. Stab. Dyn.

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This paper presents an innovative technique for structural damage detection based on time series analysis with feedback controllers incorporated into the structure. The sensitivity of autoregressive (AR) coe±cients to element sti®ness is¯rst derived, and it is proposed that the sensitivity of the AR coe±cients can be enhanced by intentionally assigning the poles of the detection system. Finally, an "x control chart is constructed based on the sensitivity-enhanced AR coe±cient. Identi¯cation of the occurrence of damage is achieved by monitoring statistically signi¯cant changes in the control chart. The proposed methodology is validated by examples including a cantilever Euler beam and Phase I of the IASC-ASCE benchmark structure. The simulation results show that by utilizing the sensitivity-enhanced AR coe±cients, the control charts are more sensitive to damage and are capable of detecting small levels of structural damage even in the presence of measurement noise.

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Section: Introductionmentioning

confidence: 99%

Structural Damage Detection Using Sensitivity-Enhanced Autoregressive Coefficients

Wang

Liu

et al. 2016

Int. J. Str. Stab. Dyn.

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“…As most models are derived from first-principle laws, the parameters of these models communicate fundamental properties of the system. In engineering practice, knowledge of these properties is necessary to conduct control, diagnosis, calibration, and many other essential tasks (Lew, 2008; Yoshimura, 2010, Jamali et al., 2015). A number of well-developed methodologies exist to address this problem, the majority of which have roots in the fundamental theory of least squares.…”

Section: Introductionmentioning

confidence: 99%

Identifying parametric variation in second-order system from frequency response measurement

Hegde

Tang

2016

Journal of Vibration and Control

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Fundamentally, second-order model is the foundation of describing the dynamic characteristics of many mechanical and electrical systems. This paper investigates a parametric identification scheme for single degree-of-freedom second-order model in which the model parameters are subject to normal variation. By utilizing frequency response magnitude and phase angle measurements, we construct a linear-in-the-parameters model and build a related maximum likelihood estimator for both parametric means as well as variances. The validity of the approach is demonstrated through a collection of case analyses, and the results show considerable levels of accuracy in the presence of sufficient data.

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“…The damage of a structure can be detected from the variation of structural features, such as structural vibration and static deformation. However, these features may be affected by changing environmental conditions such as temperature, humidity, loading conditions, and boundary conditions [6,7]. This brings a challenging problem to vibration-based structural health monitoring methods [2,3], which use the changes of vibration characteristics due to damage.…”

Section: Introductionmentioning

confidence: 99%

Modeling of an Arch Dam Long-Term Static Deformation for Structural Health Monitoring

Lew

Loh

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper presents a modeling technique to characterize the long-term static deformation of Fei-Tsui dam, which is located in a very active seismic zone area of Taiwan, for structural health monitoring (SHM). The proposed approach characterizes the static deformation as a function of the measured physical parameters, where the water level and temperature are the two most important variables affecting dam deformation. The developed approach integrates a linear-least squares technique and a nonlinear least-squares process to identify a model by minimizing the residual error between the identified model and the measured static deformation. The proposed modeling technique can also be used for the prediction of the long-term deformation based on the measured water level and temperature for SHM.

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Reduction of uncertainty effect on damage identification using feedback control

Cited by 12 publications

References 7 publications

Structural Damage Detection Using Sensitivity-Enhanced Autoregressive Coefficients

Structural Damage Detection Using Sensitivity-Enhanced Autoregressive Coefficients

Identifying parametric variation in second-order system from frequency response measurement

Modeling of an Arch Dam Long-Term Static Deformation for Structural Health Monitoring

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