A Technique Based on Kalman Filter and Least Square Estimation for Time Domain Structural Damage Detection

Ying Lei; Yi Ke Mao

doi:10.4028/www.scientific.net/AMR.163-167.2683

Paper Titles

Application of Neural Network in Displacement Prediction of Dongping Tunnel
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Study of Load Test of Pedestrian Suspension Bridge
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Application Analysis of Structure Deformation Monitoring Prediction Methods
p.2674

Effect of Computational Patterns of PCA on Moving Force Identification
p.2678

A Technique Based on Kalman Filter and Least Square Estimation for Time Domain Structural Damage Detection
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Investigation Concerning Vibration Reduction of Self-Anchored Suspension Bridge under Vehicle Braking Forces
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Study on Parameters for Identification of Wavelet Packet Energy Spectrum for Structural Damage Alarming
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Bidirectional Moving Vehicle Load Identification from Bridge Responses
p.2699

A Metamodel Based Damage Identification Method
p.2704

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 163-167A Technique Based on Kalman Filter and Least...

A Technique Based on Kalman Filter and Least Square Estimation for Time Domain Structural Damage Detection

Abstract:

In this paper, a time domain structural damage detection approach based on Kalman filter and least square estimation is proposed for structures under limited input and output measurements. First of all, the dynamic parameters of the structure, such as stiffness and damping coefficients are identified by sequentially utilizing the extended Kalman estimator and the least squares estimation. Secondly, after the structural dynamic parameters are gained, the normal Kalman filter and least square estimation is used to identify the state vector and unknown input of the structure, and the changes of the structure such as reducing of the stiffness are regarded as ‘additional unknown input’. Then the changed parameters can be calculated by analyses the connection between ‘additional unknown input’ and changing parameters, Local structural damage in the structure can also be detected by tracking the changes in the identified values of structural dynamic parameters in time domain at element level, e.g., the degrading of stiffness parameters. Numerical example of detecting structural local damages in a four story share building illustrates the efficiency of the proposed approach.

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Periodical:

Advanced Materials Research (Volumes 163-167)

Pages:

2683-2688

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.163-167.2683

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Online since:

December 2010

Authors:

Ying Lei, Yi Ke Mao

Keywords:

Kalman Filter (KF), Least Square Estimation, System Identification, Time Domain Damage Detection, Unknown Input

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