Experimental Study on Impact-Induced Damage Detection Using an Improved Extended Kalman Filter

2014 ◽  
Vol 14 (05) ◽  
pp. 1440007 ◽  
Author(s):  
Ying Lei ◽  
Zhilu Lai ◽  
Songye Zhu ◽  
Xiao-Hua Zhang
Keyword(s):  
Experimental Study ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Impact Load ◽  
Structural Parameters ◽  
Optimization Procedure ◽  
Shaking Table ◽  
Improved Performance

This paper presents an experimental study on using an improved extended Kalman filter (EKF) to identify impact-induced structural damage. By introducing the optimization of estimated residual error into the classical EKF, this real-time approach demonstrates an excellent capability to identify the abrupt changes of structural parameters instantly and accurately. The optimization procedure is activated when a prescribed threshold is exceeded. A shaking table test of a three-story steel frame subjected to abrupt damage induced by impact load was conducted to validate the improved EKF approach. The results clearly reveal its improved performance and good anti-noise ability in identifying time-variant structural parameters.

Online Tracking of Structural Parameters Based on Recursive Subspace Identification

2010 ◽  
Author(s):  
Jian-Huang Weng ◽  
Chin-Hsiung Loh
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Structural Parameters ◽  
Shaking Table ◽  
External Loading ◽  
Subspace Identification ◽  
Structural System ◽  
Real Time System ◽  
On Line

An important objective of structural health monitoring (SHM) for civil infrastructure is to identify the state of the structure and to detect these changes when it occurred. The changes of features in a structural system may due to the nonlinear inelastic response of structure or due to the structural damage subjected to severe external loading. Therefore, damage detection in large structural system, such as buildings and bridges, can improve safety and reduce maintenance costs and the design of damage detection system is one of the goals of SHM. The objective of this paper is to develop an on-line and almost real-time system parameter estimation and damage detection technique from the response measurements through using the Recursive Subspace identification (RSI) or Recursive Stochastic Subspace Identification (RSSI) approaches. Verification of the proposed method by using data from both numerical simulation and the shaking table test of a steel structure with abrupt change of inter-story stiffness are discussed. With the implementation of forgetting factor in RSSI/RSI methods the ability of on-line damage detection of structural system can be enhanced.

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 Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

2021 ◽  
Author(s):  
Xiaoxiong Zhang ◽  
Jia He ◽  
Xugang Hua ◽  
Zhengqing Chen ◽  
Ou Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Structural Parameters ◽  
Optimization Procedure ◽  
Unbiased Estimation ◽  
Time Varying ◽  
Time Step ◽  
Varying Parameters ◽  
Unknown Inputs ◽  
Time Varying Parameters

Abstract To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

Experimental Study of Indocyanine Green Pharmacokinetics Based on Adaptive Extended Kalman Filter

2020 ◽  
Vol 47 (9) ◽  
pp. 0907002
Author(s):  
张雁琦 Zhang Yanqi ◽  
张丽敏 Zhang Limin ◽  
赵志超 Zhao Zhichao ◽  
马文娟 Ma Wenjuan ◽  
高峰 Gao Feng
Keyword(s):  
Experimental Study ◽  
Kalman Filter ◽  
Indocyanine Green ◽  
Extended Kalman Filter ◽  
Adaptive Extended Kalman Filter

scholarly journals Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

2019 ◽  
Vol 39 (4) ◽  
pp. 835-849 ◽  
Author(s):  
Jinshan Huang ◽  
Xianzhi Li ◽  
Xiongjun Yang ◽  
Zhupeng Zheng ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Structural Parameters ◽  
Wide Band ◽  
Unknown Inputs ◽  
Filter Methods ◽  
Filter Approach ◽  
Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

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