An integrated real-time structural damage detection method based on extended Kalman filter and dynamic statistical process control. Issue 4 (April 2017)

• Record Type: Journal Article
• Authors: Jin, Chenhao; Jang, Shinae; Sun, Xiaorong
• Is Part Of: Advances in structural engineering. Volume 20:Issue 4(2017)
• Page Start: 549
• Page End: 563
• Publication Date: 2017-04
• Subjects: extended Kalman filter -- parameter identification -- state-space model -- statistical process control -- structural damage detection; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433216658484
• Languages: English
• ISSNs: 1369-4332

Abstract

Real-time structural parameter identification and damage detection are of great significance for structural health monitoring systems. The extended Kalman filter has been implemented in many structural damage detection methods due to its capability to estimate structural parameters based on online measurement data. Current research assumes constant structural parameters and uses static statistical process control for damage detection. However, structural parameters are typically slow-changing due to variations such as environmental and operational effects. Hence, false alarms may easily be triggered when the data points falling outside of the static statistical process control range due to the environmental and operational effects. In order to overcome this problem, this article presents a novel real-time structural damage detection method by integrating extended Kalman filter and dynamic statistical process control. Based on historical measurements of damage-sensitive parameters in the state-space model, extended Kalman filter is used to provide real-time estimations of these parameters as well as standard derivations in each time step, which are then used to update the control limits for dynamic statistical process control to detect any abnormality in the selected parameters. The numerical validation is performed on both linear and nonlinear structures, considering different damage scenarios. The simulation results demonstrate high detection accuracy rate and light computational costs of the developed extended Kalman filter–dynamic statistical process control damage detection method and the potential for implementation in structural health monitoring systems for in-service civil structures.