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Structural Damage Detection Using Extended Kalman Filter Combined with Statistical Process Control in Nonlinear Systems

C. JIN, S. JANG, X. SUN

Abstract


The goal of structural health monitoring is to determine the status of the structure and identify the structural damage. Extended Kalman filter (EKF) has shown effective capability to track the structural parameters for civil structures. When structural damage occurs, the estimations of parameters from EKF will deviate from their constant values, and the changes can be observed visually. However, in view of the environmental and operational effects, structural parameters may fluctuate within a normal range, which may result false alarm problems and cause difficulties to observe the structural damage in real time. In this paper, EKF is combined with Statistical Process Control (SPC) to detect the structural damage in real time. Adaptive SPC control limits are derived based on parameter estimation from EKF and updated dynamically in each time step. When structural damage occurs, the estimation of parameters will deviate outside of the control ranges, thus can be captured by the SPC control limits. This approach is tested on a two-story nonlinear hysteretic structure. The numerical testing results demonstrate that the adaptive SPC-based Kalman filter method is capable to identify and track the general changes of structural parameters and detect damage online with high confidence for nonlinear structural dynamic systems.

doi: 10.12783/SHM2015/303


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