Novel Outlier-Resistant Extended Kalman Filter for Robust Online Structural Identification
Publication: Journal of Engineering Mechanics
Volume 141, Issue 1
Abstract
Structural health monitoring (SHM) using dynamic response measurement has received tremendous attention over the last decades. In practical circumstances, outliers may exist in the measurements that lead to undesirable identification results. Therefore, detection and special treatment of outliers are important. Unfortunately, this issue has rarely been taken into systematic consideration in SHM. In this paper, a novel outlier-resistant extended Kalman filter (OR-EKF) is proposed for outlier detection and robust online structural parametric identification using dynamic response data possibly contaminated with outliers. Instead of definite judgment on the outlierness of a data point, the proposed OR-EKF provides the probability of outlier for the measurement at each time step. By excluding the identified outliers, the OR-EKF ensures the stability and reliability of the estimation. In the illustrative examples, the OR-EKF is applied to parametric identification for structural systems with time-varying stiffness in comparison with the plain EKF. The structural response measurements are contaminated with outliers in addition to Gaussian noise. The proposed OR-EKF is capable of outlier detection, and it can capture the degrading stiffness trend with more stable and reliable results than the EKF.
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Acknowledgments
This work was supported by the Research Committee of University of Macau under Research Grant MYRG081(Y1-L2)-FST13-YKV and the Science and Technology Development Fund (FDCT) of the Macau government under research grant FDCT/012/2013/A1. This generous support is gratefully acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 141 • Issue 1 • January 2015
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© 2014 American Society of Civil Engineers.
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Received: Feb 1, 2013
Accepted: Apr 23, 2014
Published online: May 29, 2014
Published in print: Jan 1, 2015
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