Novel Outlier-Resistant Extended Kalman Filter for Robust Online Structural Identification (original) (raw)
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International Journal of Sustainable Materials and Structural Systems, 2015
Structural health assessment procedures using extended and unscented Kalman filter concepts are presented and compared. The extended Kalman filter (EKF)-based algorithm proposed earlier for nonlinear system identification comes with limitations. The linearisation process used in EKF may lead to non-convergence for higher level of nonlinearity. To address the deficiency, the authors proposed a new algorithm known as unscented Kalman filter with unknown input and weighted global iteration (UKF-UI-WGI). In this study, a weighted global iteration technique with objective function is incorporated with the UKF algorithm in order to improve its efficiency. To generate the information required to implement the algorithm, it is integrated with least-squares-based algorithm. The stability, convergence, and robustness of the UKF-UI-WGI over EKF-based algorithm are compared in terms of several parameters including the sampling interval, duration of responses, and the dimension of the frames. With the help of examples, the overall superiority of UKF-UI-WGI over EKF-based algorithm is established.
International Journal of Structural Stability and Dynamics, 2016
System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficien...
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A nonlinear system identification-based structural health assessment procedure is presented in this paper. The procedure uses the unscented Kalman filter (UKF) concept. The weighted global iteration with an objective function is incorporated with the UKF algorithm to obtain stable, convergent, and optimal solution. An iterative least squares technique is also integrated with the UKF algorithm. The procedure is capable of assessing health of any type of structures, represented by finite elements. It can identify the structure using limited noise-contaminated dynamic responses, measured at a small part of large structural systems and without using input excitation information. In order to demonstrate its effectiveness, the proposed procedure is compared with the extended Kalman filter (EKF)-based procedure. For numerical verification, a two-dimensional five-story two-bay steel frame is considered. Defect-free and two defective states with small and severe defects are considered. The study shows that the proposed UKF-based procedure can assess structural health more accurately and efficiently than the EKF-based procedures for nonlinear system identification.
Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements
Sensors (Basel, Switzerland), 2018
The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties-including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, ...
Structural Control and Health Monitoring, 2015
A novel concept denoted as unscented Kalman filter with unknown input and weighted global iterations (UKF-UI-WGI) to assess health of large structural systems is proposed. It incorporates the basic features of UKF to identify systems in the presence of severe nonlinearity but then added a few desirable features to increase its implementation potential. Because the information on excitation and unknown initial state vector is necessary to implement any UKF-based approach, a substructure concept is introduced to generate them. The traditional UKF concept attempts to identify small structural systems using large duration of response histories in one global iteration. Because it fails to assess the defective states of large structures in most cases, a weighted multiple global iterations procedure with objective functions using short duration responses is introduced. The superiority of UKF-UI-WGI over the traditional UKF is demonstrated with the help of several illustrative examples using single and multiple substructures in identifying both defect-free and defective states. With the help of the same examples, the superiority of the proposed method over the extended Kalman filter-based method developed by the team earlier is conclusively documented. With the help of parametric studies, it is documented that the proposed method is robust, accurate, and stable. The study confirms that UKF-UI-WGI can identify large structural system using only limited response information measured at a small part of a structure without using any excitation information. The concept significantly advances the state-of-the-art in UKF-based nonlinear system identification and considerably improves its implementation potential.
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Structural health monitoring (SHM) is a means of identifying damage from structural response to environmental loads. Real-time SHM is of particular use for rapid assessment of structural safety by owners and civil defense authorities. This paper presents an algorithm for real-time SHM during earthquake events using only acceleration measurements and infrequently measured displacement motivated by global positioning system. The algorithm identifies a nonlinear baseline model including hysteretic dynamics and permanent deformation using convex integral-based fitting methods and piecewise linear least squares fitting. The methodology identifies pre and postyield stiffness, elastic and plastic components of displacement, and final residual displacement. It thus identifies key measures of damage affecting the immediate safety or use of the structure and the long-term cost of repair and retrofit. The algorithm is tested with simulated response data using the El-Centro earthquake record and with measured response data. Both data sets are based on a four-story nonlinear steel frame structure using the El-Centro ground motion record. Overall, the algorithm is shown to provide accurate indications of the existence, location, and magnitude of structural damage for nonlinear shear-type buildings. Additionally, the identified permanent displacement is a particularly useful damage measure for the construction of probabilistic fragility functions.