Novel Outlier-Resistant Extended Kalman Filter for Robust Online Structural Identification

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.