Moving-window extended Kalman filter for structural damage detection with unknown process and measurement noises

Zhilu Lai, Ying Lei, Songye Zhu*, You Lin Xu, Xiao Hua Zhang, Sridhar Krishnaswamy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The extended Kalman filter (EKF), as a popular tool for optimally estimating system state from noisy measurement, has been used successfully in various areas over the past several decades. However, classical EKF has several limitations when applied to structural system identification; thus, researchers have proposed a number of variations for this method. The current study focuses on using EKF for real-time system identification and damage detection in civil structures. An improved EKF approach, called moving-window EKF (MWEKF), is proposed in this paper after a discussion on the problems associated with the application of classical EKF in time-variant systems. The proposed approach uses the moving-window technique to estimate several statistical properties. MWEKF is more robust and adaptive in structural damage detection compared with classical EKF because of the following reasons: (1) it is insensitive to the selection of the initial state vector; (2) it exhibits more accurate system parameter identification; and (3) it is immune to the inaccurate assumption of noise levels because measurement and process noise levels are estimated in this approach. The salient features of MWEKF are illustrated through numerical simulations of time-variant structural systems and an experiment on a three-story steel shear building model. Results demonstrate that MWEKF is a robust and effective tool for system identification and damage detection in civil structures.

Original languageEnglish (US)
Pages (from-to)428-440
Number of pages13
JournalMeasurement: Journal of the International Measurement Confederation
Volume88
DOIs
StatePublished - Jun 1 2016

Keywords

  • Damage detection
  • Extended Kalman filter
  • Measurement noise estimation
  • Moving window
  • Process noise estimation
  • Structural health monitoring
  • Time-variant system

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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