Structural damage detection using extended Kalman filter combined with statistical process control

Chenhao Jin; Shinae Jang; Xiaorong Sun

doi:10.1117/12.2084466

3 April 2015 Structural damage detection using extended Kalman filter combined with statistical process control

Chenhao Jin, Shinae Jang, Xiaorong Sun

Proceedings Volume 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015; 94352K (2015) https://doi.org/10.1117/12.2084466
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2015, San Diego, California, United States

Abstract

Traditional modal-based methods, which identify damage based upon changes in vibration characteristics of the structure on a global basis, have received considerable attention in the past decades. However, the effectiveness of the modalbased methods is dependent on the type of damage and the accuracy of the structural model, and these methods may also have difficulties when applied to complex structures. The extended Kalman filter (EKF) algorithm which has the capability to estimate parameters and catch abrupt changes, is currently used in continuous and automatic structural damage detection to overcome disadvantages of traditional methods. Structural parameters are typically slow-changing variables under effects of operational and environmental conditions, thus it would be difficult to observe the structural damage and quantify the damage in real-time with EKF only. In this paper, a Statistical Process Control (SPC) is combined with EFK method in order to overcome this difficulty. Based on historical measurements of damage-sensitive feathers involved in the state-space dynamic models, extended Kalman filter (EKF) algorithm is used to produce real-time estimations of these features as well as standard derivations, which can then be used to form control ranges for SPC to detect any abnormality of the selected features. Moreover, confidence levels of the detection can be adjusted by choosing different times of sigma and number of adjacent out-of-range points. The proposed method is tested using simulated data of a three floors linear building in different damage scenarios, and numerical results demonstrate high damage detection accuracy and light computation of this presented method.

Citation Download Citation

Chenhao Jin, Shinae Jang, and Xiaorong Sun "Structural damage detection using extended Kalman filter combined with statistical process control", Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 94352K (3 April 2015); https://doi.org/10.1117/12.2084466

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