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.

Damage Detection of Building Structures Using Ambient Vibration Measurement

2007 ◽  
Vol 348-349 ◽  
pp. 721-724
Author(s):  
S.H. Yoo ◽  
S.Y. Kim ◽  
S.W. Shin
Keyword(s):  
Damage Detection ◽  
Measurement Data ◽  
Tall Buildings ◽  
Steel Structures ◽  
Least Square ◽  
Shaking Table ◽  
Dynamic Responses ◽  
Vibration Measurement ◽  
Ambient Vibration ◽  
Building Structures

System identification(SI) techniques using dynamic responses and behaviors of structural systems become an outstanding issue of researchers. However the conventional SI techniques are identified to be non-practical to the complex and tall buildings, due to limitation of the availability of an accurate data that is magnitude or location of external loads. Thus a technique to estimate structural properties of building without input measurement data and using limited response is essential in structural health monitoring. In this study, the improved least square method for damage detection is proposed and verified by shaking table tests on three-story plane frame steel structures. In results damage detection was performed in each member level.

Hybrid filters and feedback mechanism for wearable-based human-manipulator interface

2015 ◽  
Vol 42 (5) ◽  
pp. 485-495 ◽  
Author(s):  
Ping Zhang ◽  
Bei Li ◽  
Guanglong Du
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
Unscented Kalman Filter ◽  
Robot Manipulator ◽  
Experimental Studies ◽  
Feedback Mechanism ◽  
Content Type ◽  
System States ◽  
Noisy Measurements ◽  
Hybrid Filters

Purpose – This paper aims to develop a wearable-based human-manipulator interface which integrates the interval Kalman filter (IKF), unscented Kalman filter (UKF), over damping method (ODM) and adaptive multispace transformation (AMT) to perform immersive human-manipulator interaction by interacting the natural and continuous motion of the human operator’s hand with the robot manipulator. Design/methodology/approach – The interface requires that a wearable watch is tightly worn on the operator’s hand to track the continuous movements of the operator’s hand. Nevertheless, the measurement errors generated by the sensor error and tracking failure signicantly occur several times, which means that the measurement is not determined with sufficient accuracy. Due to this fact, IKF and UKF are used to compensate for the noisy and incomplete measurements, and ODM is established to eliminate the influence of the error signals like data jitter. Furthermore, to be subject to the inherent perceptive limitations of the human operator and the motor, AMT that focuses on a secondary treatment is also introduced. Findings – Experimental studies on the GOOGOL GRB3016 robot show that such a wearable-based interface that incorporates the feedback mechanism and hybrid filters can operate the robot manipulator more flexibly and advantageously even if the operator is nonprofessional; the feedback mechanism introduced here can successfully assist in improving the performance of the interface. Originality/value – The interface uses one wearable watch to simultaneously track the orientation and position of the operator’s hand; it is not only avoids problems of occlusion, identification and limited operating space, but also realizes a kind of two-way human-manipulator interaction, a feedback mechanism can be triggered in the watch to reflect the system states in real time. Furthermore, the interface gets rid of the synchronization question in posture estimation, as hybrid filters work independently to compensate the noisy measurements respectively.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer

2018 ◽  
Vol 37 (3) ◽  
pp. 1054-1068 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi ◽  
Youssef Barradi
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Content Type ◽  
Ant Lion Optimization ◽  
Ant Lion

PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

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 RFID/ In-vehicle Sensors-Integrated Vehicle Positioning Strategy Utilising LSSVM and Federated UKF in a Tunnel

2015 ◽  
Vol 69 (4) ◽  
pp. 845-868 ◽  
Author(s):  
Xiang Song ◽  
Xu Li ◽  
Wencheng Tang ◽  
Weigong Zhang
Keyword(s):  
Kalman Filter ◽  
Radio Frequency Identification ◽  
Unscented Kalman Filter ◽  
Least Square ◽  
Support Vector ◽  
Rfid Tags ◽  
Positioning Strategy ◽  
Sensors Fusion ◽  
Vehicle Positioning ◽  
Vehicle Sensors

This paper proposes a Radio Frequency Identification (RFID)/ in-vehicle sensors fusion strategy for vehicle positioning in completely Global Positioning System (GPS)-denied environments such as tunnels. The strategy employs a two-step approach, namely, the calculation of the distances between the RFID tags and the reader, and then the global fusion estimation of vehicle position. First, a Least Square Support Vector Machine (LSSVM) algorithm is developed to obtain distance. Next a novel Federated Unscented Kalman Filter (FUKF) is designed to realise the global fusion. The decentralised federated filter is adopted to combine the data from RFID and in-vehicle sensors, and the UKF is employed to design a local filter since it has better ability to deal with a nonlinear problem than an Extended Kalman Filter (EKF). Due to the optimised layout of RFID tags and the application of the decentralised filter, the number of tags is reduced. Finally, the feasibility and effectiveness of the proposed strategy are evaluated through experiments.

A high precision indoor positioning method based on UKF

2021 ◽  
pp. 1-14
Author(s):  
Mujie Zhao ◽  
Tao Zhang ◽  
Di Wang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
Wide Band ◽  
High Order ◽  
Least Square ◽  
Time Of Arrival ◽  
State Variables ◽  
Fifth Order

Aiming at the nonlinear filter problem in Ultra Wide Band (UWB) navigation and position, a high-order Unscented Kalman Filter (UKF) position method is proposed. On the one hand, the position and velocity are used as state variables to establish a nonlinear filtering model based on UWB position system. On the other hand, based on the fifth order cubature transform (CT), the analytical solution of the high-order unscented Kalman filter is obtained by introducing a free parameter δ. To verify the effectiveness of the proposed method, the Time of Arrival (TOA) location method, the least square method and fifth order CKF method are introduced as comparison methods. The simulation and experimental results show that the proposed high-order UKF method has good positioning accuracy in both static and dynamic UWB positioning methods.

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.

scholarly journals A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter

2020 ◽  
Vol 20 (10) ◽  
pp. 2042006
Author(s):  
Jiantao Li ◽  
Xinqun Zhu ◽  
Siu-Seong Law ◽  
Bijan Samali
Keyword(s):  
Surface Roughness ◽  
Kalman Filter ◽  
Damage Detection ◽  
Interaction Force ◽  
Road Surface ◽  
Dynamic Responses ◽  
The Road ◽  
Stiffness Reduction ◽  
Bridge Damage ◽  
Road Surface Roughness

Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-[Formula: see text] method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.

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