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

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.

A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

“…To start the Bayesian optimization process, 30 combinations are randomly chosen, and the performance of the parameter identification is evaluated. Then, these points are considered as the input to the Gaussian processes, and the next sampling point is calculated from equation (22). The process continues until a convergence to the minimum value achieves.…”

“…19 EKF has been successfully applied to models with low-to-moderate nonlinearities. [20][21][22] However, since the linearization is performed by a Taylor expansion, and Jacobian matrices are calculated at each step, EKF may become inefficient for high-dimensional variable estimation and large uncertainties.…”

Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation

“…This method was extended by Liu et al [17] for identifying strong nonlinearity parameters. With the combination of EKF and dynamic statistical process control, Jin et al [18] presented a novel real-time damage detection method for identifying the parameters of both linear and nonlinear structures under different damage scenarios. Xiao et al [19] proposed an adaptive three-stage EKF method to solve state and fault estimation in nonlinear discrete-time system.…”

A new physical parameter identification method for shear frame structures under limited inputs and outputs

“…Unfortunately, this requirement is not satisfied in practical civil engineering. As another main time domain method, KF methods, including extended Kalman filter (EKF) (He et al, 2019; Hoshiya and Saito, 1984; Jin et al, 2016; Koh et al, 1991; Lei et al, 2012; Liu et al, 2016; Yang et al, 2006) and unscented Kalman filter (UKF) (Chatzi and Smyth, 2009; Olivier and Smyth, 2017), play an important role on physical parameter identification. For instance, Olivier and Smyth (2017) employed UKF for structural stiffness and damping identification, however, this approach has a drawback that it needs both output responses at all DOFs and input excitation information in advance.…”