System Identification with Limited Observations and without Input

Wang, Duan; Haldar, Achintya

doi:10.1061/(asce)0733-9399(1997)123:5(504)

Cited by 99 publications

(40 citation statements)

References 6 publications

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“…The EKF technique is explained and as follows [13,14]: Through Eq. 1, the state equation of the dynamic system is expressed as X .…”

Section: Extended Kalman Filter Methods With Weighted Global Iterationmentioning

confidence: 99%

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Structural Parameter Identification in Time Domain using Generalized Hybrid Inversion Method

Wang¹,

Cui²

2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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A structural parameter identification method without input information and with limited observations is proposed to identify the stiffness parameters and damping coefficient, to inverse the input information and to estimate the unmeasured responses in time domain. The unique feature of this technique is that it requires only the displacement responses of substructure. This method has two steps: 1) structural physical parameter identification of substructure and input inversion using hybrid inversion method (HIM) with introducing the combination method of the modified LevenbergMarquadt and the singular value decomposition (SVD-mLM) and rectangular window method, and 2) structural physical parameter identification of whole structure and estimation of unmeasured responses using the extended Kalman filter method with weighted global iteration (EKF-WGI) coupled with memory fading filter (MFF) technique and Runge-Kutta (R-K) method. The improved method is called as generalized hybrid inversion method (GHIM) by the authors. The parameter identification and the response estimation are carried out using the proposed method by a numerical example. Three noise contaminated cases of the displacement responses are considered. The robustness, efficiency and accuracy of the new technique are verified by the results.

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“…The EKF technique is explained and as follows [13,14]: Through Eq. 1, the state equation of the dynamic system is expressed as X .…”

Section: Extended Kalman Filter Methods With Weighted Global Iterationmentioning

confidence: 99%

“…In the EKF-WGI procedure [13,14], the global iterations are carried out by weighting the error covariance matrix at each global iteration. To start the first global iterations, the initial values of can be set to be: …”

Section: Extended Kalman Filter Methods With Weighted Global Iterationmentioning

confidence: 99%

Structural Parameter Identification in Time Domain using Generalized Hybrid Inversion Method

Wang¹,

Cui²

2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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“…Since defects in infrastructures can make them behave nonlinearly, the team decided to use piecewise linear or Extended KF concept or (EKF). The team proposed several methods using the ILS-EKF-UI concept for two-dimensional (2D) [12,13] and threedimensional (3D) [14] infrastructures. Most recently, the authors observed another limitation of EKF-based concept.…”

Section: Complexitymentioning

confidence: 99%

Complexities in Assessing Structural Health of Civil Infrastructures

Al-Hussein

Haldar

2017

Complexity

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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.

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“…Hence the size of the extended state vector and the corresponding state equation are quite large [13]. If the order of the EKF is 2n + m (n = number of structural DOFs, m = number of unknown structural parameters), the computational effort of the EKF is on the order of (2n + m) 3 [14]. An EKF with high order suffers from not only long computational time and may lead to divergent behavior for a large number of unknown parameters as identification is an inversed problem.…”

Section: Introductionmentioning

confidence: 99%

“…Identification of structural parameters and unknown external excitation has been investigated by some researchers, for example, Wang and Haldar [3] developed iterative least squares approaches to identify simultaneously the structural parameters and ground motion of an earthquake. Chen and Li [4] proposed a statistical average algorithm for simultaneous estimation of structural parameters and earthquake-induced ground motion.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network

Lei

Tang

Wang

et al. 2012

International Journal of Distributed Sensor Networks

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It is necessary to investigate techniques for monitoring structures under unknown earthquakes. For this purpose, an algorithm is proposed in this paper for the identification of structures and excitation of multi-story shear buildings with limited measurements of structural responses. The equation of motion of a multi-story shear building under ground motion is established in the absolute co-ordinate system, while the multi-story building is decomposed into substructures. A novel two-step Kalman estimator approach, which is not available in the previous literature, is proposed for the identification substructures and unknown ground motion with less computational effort. Then, for the purpose of intelligent structural monitoring, a new wireless sensor network is developed in this paper. The designed wireless sensor network has a two-level cluster-tree architecture. Hardware designs of the sensor unit and the cluster head are presented; especially the cluster head contains a low power digital signal processor with strong computing capacity. Thus, the wireless sensor network has the unique feature offers distributed computing at group level. Finally, the proposed algorithm is embedded into the wireless sensor network for intelligent structural monitoring and an experimental test shows the technique is effective for intelligent monitoring of multi-story buildings under unknown earthquakes.

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System Identification with Limited Observations and without Input

Cited by 99 publications

References 6 publications

Structural Parameter Identification in Time Domain using Generalized Hybrid Inversion Method

Structural Parameter Identification in Time Domain using Generalized Hybrid Inversion Method

Complexities in Assessing Structural Health of Civil Infrastructures

Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network

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