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

2015 ◽  
Author(s):  
Chenhao Jin ◽  
Shinae Jang ◽  
Xiaorong Sun
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Damage Detection ◽  
Statistical Process Control ◽  
Extended Kalman Filter ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Statistical Process

An integrated real-time structural damage detection method based on extended Kalman filter and dynamic statistical process control

2016 ◽  
Vol 20 (4) ◽  
pp. 549-563 ◽  
Author(s):  
Chenhao Jin ◽  
Shinae Jang ◽  
Xiaorong Sun
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Damage Detection ◽  
Real Time ◽  
Statistical Process Control ◽  
Extended Kalman Filter ◽  
Structural Damage ◽  
Detection Method ◽  
Structural Parameters ◽  
Statistical Process

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.

scholarly journals Statistical Process Control of a Kalman Filter Model

Sensors ◽  
2014 ◽  
Vol 14 (10) ◽  
pp. 18053-18074 ◽  
Author(s):  
Sonja Gamse ◽  
Fereydoun Nobakht-Ersi ◽  
Mohammad Sharifi
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Filter Model

VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL

2001 ◽  
Vol 15 (4) ◽  
pp. 707-721 ◽  
Author(s):  
MICHAEL L. FUGATE ◽  
HOON SOHN ◽  
CHARLES R. FARRAR
Keyword(s):  
Process Control ◽  
Damage Detection ◽  
Statistical Process Control ◽  
Statistical Process

Synthesis of Cross-Correlation Functions of Partial Responses and the Extended Kalman Filter Approach for Structural Damage Detection Under Ambient Excitations

2018 ◽  
Vol 18 (08) ◽  
pp. 1840003 ◽  
Author(s):  
Y. Lei ◽  
D. D. Xia ◽  
F. Chen ◽  
Y. M. Deng
Keyword(s):  
Kalman Filter ◽  
Damage Detection ◽  
White Noise ◽  
Extended Kalman Filter ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Product Models ◽  
Structural Responses ◽  
Measurement Noises

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method.

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