scholarly journals Complexities in Assessing Structural Health of Civil Infrastructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Abdullah Al-Hussein ◽  
Achintya Haldar
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Health Assessment ◽  
Time History ◽  
Dynamic Responses ◽  
Simple Problem ◽  
Civil Infrastructures ◽  
Duration Time ◽  
Highly Nonlinear ◽  
Global Iteration

The complexity in the health assessment of civil infrastructures, as it evolves over a long period of time, is briefly discussed. A simple problem can become very complex based on the current needs, sophistication required, and the technological advancements. To meet the current needs of locating defect spots and their severity accurately and efficiently, infrastructures are represented by finite elements. To increase the implementation potential, the stiffness parameters of all the elements are identified and tracked using only few noise-contaminated dynamic responses measured at small part of the infrastructure. To extract the required information, Kalman filter concept is integrated with other numerical schemes. An unscented Kalman filter (UKF) concept is developed for highly nonlinear dynamic systems. It is denoted as 3D UKF-UI-WGI. The basic UKF concept is improved in several ways. Instead of using one long duration time-history in one global iteration, very short duration time-histories and multiple global iterations with weight factors are used to locate the defect spot more accurately and efficiently. The capabilities of the procedure are demonstrated with the help of two informative examples. The proposed procedure is much superior to the extended Kalman filter-based procedures developed by the team earlier.

scholarly journals Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

2016 ◽  
Vol 16 (06) ◽  
pp. 1550016 ◽  
Author(s):  
Mohsen Askari ◽  
Jianchun Li ◽  
Bijan Samali
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Measurement Data ◽  
Linear Structure ◽  
Structural Identification ◽  
Input Output ◽  
Output Measurement ◽  
Highly Nonlinear

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 efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2008 ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Reduced Order ◽  
Highly Nonlinear

Electroslag Remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode that is immersed a small amount in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. A new method of ESR control has been developed that incorporates an accurate, reduced-order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

Damage detection in steel structures with semi-rigid connections using unscented Kalman filter

2017 ◽  
Vol 8 (2) ◽  
pp. 222-239
Author(s):  
Hasan Katkhuda ◽  
Nasim Shatarat ◽  
Khaled Hyari
Keyword(s):  
Kalman Filter ◽  
Damage Detection ◽  
Unscented Kalman Filter ◽  
Steel Structures ◽  
Least Square ◽  
Dynamic Responses ◽  
Optimum Number ◽  
Dynamic Force ◽  
Monitoring Method ◽  
Content Type

Purpose The purpose of this paper is to detect damages in steel structures with actual connections, i.e. semi-rigid connections. The method will detect the damages by tracking the changes in the stiffness of structural members using only a limited number of dynamic responses and without knowing the type or time history of the dynamic force applied on the structure. Design/methodology/approach The paper proposes a technique that combines the iterative least-square and unscented Kalman filter (UKF) methods to identify the stiffness of beams and columns in typical two-dimensional steel-framed structures with semi-rigid connections. The detection of damages is by using nonlinear time-domain structural health monitoring method. Findings The technique is verified by using numerical examples using noise-free and noise-included dynamic responses from two different types of dynamic forces: harmonic and blast loads. The results showed that the UKF method with iterative least-square is a powerful approach to identify and detect damages in structures that have nonlinear behavior and the method was able to detect the damages in beams with a very high accuracy for noise-free and noise-included dynamic responses. In addition, the optimum number and locations of dynamic responses (accelerometer sensors) required for damage detection were determined. Originality/value This paper fulfills an identified need to detect damages in steel structures using only a limited number of accelerometer sensors.

Geomagnetic orbit determination: EKF or UKF?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jin Wu ◽  
Ming Liu ◽  
Chengxi Zhang ◽  
Yulong Huang ◽  
Zebo Zhou
Keyword(s):  
Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Nonlinear Models ◽  
Computational Time ◽  
Content Type ◽  
Spacecraft Navigation ◽  
Highly Nonlinear ◽  
Autonomous Orbit Determination ◽  
Spherical Harmonic Models

Purpose Autonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion. Design/methodology/approach This paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model. Findings While EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination. Practical implications The intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models. Originality/value The results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Highly Nonlinear ◽  
Process Disturbances

Electroslag remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode, wherein a small amount is immersed in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled at all times during the process. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. This condition is encountered during the beginning and closing stages of the ESR process and also during some process disturbances such as when the melt zone passes through a transverse electrode crack. To address these transient melting situations, a new method of the ESR estimation and control has been developed that incorporates an accurate, reduced order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

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