Modal parameter based structural identification using input–output data: Minimal instrumentation and global identifiability issues

Mukhopadhyay, Suparno; Luş, Hilmi; Betti, Raimondo

doi:10.1016/j.ymssp.2013.11.005

Cited by 27 publications

(38 citation statements)

References 15 publications

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“…In fact, the state of the art of these methods has been reviewed in a number of works . According to most of these works, system identification methods can be classified as parametric and non‐parametric (genetic algorithms , evolutionary strategy , neural networks or least‐squares estimation ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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This is the peer reviewed version of the following article: [Lei, J., Lozano-Galant, J. A., Nogal, M., Xu, D., and Turmo, J. (2017) Analysis of measurement and simulation errors in structural system identification by observability techniques. Struct. Control Health Monit., 24: . doi: 10.1002/stc.1923.], which has been published in final form at http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1923/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.During the process of structural system identification, errors are unavoidable. This paper analyzes the effects of measurement and simulation errors in structural system identification based on observability techniques. To illustrate the symbolic approach of this method a simply supported beam is analyzed step-by-step. This analysis provides, for the very first time in the literature, the parametric equations of the estimated parameters. The effects of several factors, such as errors in a particular measurement or in the whole measurement set, load location, measurement location or sign of the errors, on the accuracy of the identification results are also investigated. It is found that error in a particular measurement increases the errors of individual estimations, and this effect can be significantly mitigated by introducing random errors in the whole measurement set. The propagation of simulation errors when using observability techniques is illustrated by two structures with different measurement sets and loading cases. A fluctuation of the observed parameters around the real values is proved to be a characteristic of this method. Also, it is suggested that a sufficient combination of different load cases should be utilized to avoid the inaccurate estimation at the location of low curvature zones.Peer ReviewedPostprint (author's final draft

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Section: Introductionmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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“…An implied assumption of any system identification method is however that the dynamic states of the system and the time-invariant parameters are observ-30 able ( [1,3]) and identifiable ( [4,5]) respectively. In other words, the augmented state vector created by the underlying dynamic states and the parameters is observable ( [6,7]).…”

Section: Introductionmentioning

confidence: 99%

A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

Chatzis

Chatzi

Triantafyllou

2017

Mechanical Systems and Signal Processing

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Problems that result into locally non-differentiable and hence non-smooth state-space equations are often encountered in engineering. Examples include problems involving material laws pertaining to plasticity, impact and highly non-linear phenomena. Estimating the parameters of such systems poses a challenge, particularly since the majority of system identification algorithms 5 are formulated on the basis of smooth systems under the assumption of observability, identifiability and time invariance. For a smooth system, an observable state remains observable throughout the system evolution with the exception of few selected realizations of the state vector. However, for a non-smooth system the observable set of states and parameters may vary during the evolution of the 10 system throughout a dynamic analysis. This may cause standard identification (ID) methods, such as the Extended Kalman Filter, to temporarily diverge and ultimately fail in accurately identifying the parameters of the system. In this work, the influence of observability of non-smooth systems to the performance of the Extended and Unscented Kalman Filters is discussed and a novel algo-15 rithm particularly suited for this purpose, termed the Discontinuous Extended Kalman Filter (DEKF), is proposed.

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“…In these situations, often referred to as output‐only situations, the information of the applied input force data is not available, and determining the correct mass scaling (normalization) of the identified mode shapes becomes an issue. Because insufficient information may make the inverse problem ill‐conditioned and the parameter set of interest unidentifiable , it is important to quantify what constitutes ‘sufficient’ information in a given situation and what should be measured and what should be assumed to be known a priori. Mukhopadhyay , using a simple three‐DOF system instrumented with two sensors, illustrates that the sufficiency of measured information depends not only on the number but also on the location of sensors on the structure, and highlights the importance of identifiability in Structural Health Monitoring (SHM).…”

Section: Introductionmentioning

confidence: 99%

“…The class of shear type systems finds a wide range of applications, modeling structures like ‘tall buildings, turbine blades and airplane wings’ , and has thus gained considerable attention in structural identification/health monitoring (e.g., , in addition to many other studies considering example applications on such systems). Naturally the identifiability of such systems under various loading scenarios has garnered much attention as well (, with Mukhopadhyay also considering three‐dimensional, possibly laterally torsionally coupled, rigid floor building systems). Specifically, References and investigate the global identifiability of shear‐type systems when the force is applied through actuator(s) at some point(s) on the super‐structure, while References , and study global identifiability when the input is base excitation.…”

Section: Introductionmentioning

confidence: 99%

“…Naturally the identifiability of such systems under various loading scenarios has garnered much attention as well (, with Mukhopadhyay also considering three‐dimensional, possibly laterally torsionally coupled, rigid floor building systems). Specifically, References and investigate the global identifiability of shear‐type systems when the force is applied through actuator(s) at some point(s) on the super‐structure, while References , and study global identifiability when the input is base excitation. While References , and address mainly the question of minimal instrumentation for global identifiability, References and also develop and validate a method for unique mode shape expansion and consequent parametric identification using the measured data from the prescribed instrumentation set‐ups.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Structural identification with incomplete output‐only data and independence of measured information for shear‐type systems

Mukhopadhyay

Luş

Betti

2015

Earthq Engng Struct Dyn

Self Cite

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Summary Structural identification is the inverse problem of estimating physical parameters of a structural system from its vibration response measurements. Incomplete instrumentation and ambient vibration testing generally result in incomplete and arbitrarily normalized measured modal information, often leading to an ill‐conditioned inverse problem and non‐unique identification results. The identifiability of any parameter set of interest depends on the amount of independent available information. In this paper, we consider the identifiability of the mass and stiffness parameters of shear‐type systems in output‐only situations with incomplete instrumentation. A mode shape expansion‐cum‐mass normalization approach is presented to obtain the complete mass normalized mode shape matrix, starting from the incomplete non‐normalized modes identified using any operational modal analysis technique. An analysis is presented to determine the minimum independent information carried by any given sensor set‐up. This is used to determine the minimum necessary number and location of sensors from the point of view of minimum necessary information for identification. The different theoretical discussions are illustrated using numerical simulations and shake table experiments. It is shown that the proposed identification algorithm is able to obtain reliably accurate physical parameter estimates under the constraints of minimal instrumentation, minimal a priori information, and unmeasured input. The sensor placement rules can be used in experiment design to determine the necessary number and location of sensors on the monitored system. John Wiley & Sons, Ltd.

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Modal parameter based structural identification using input–output data: Minimal instrumentation and global identifiability issues

Cited by 27 publications

References 15 publications

Analysis of measurement and simulation errors in structural system identification by observability techniques

Analysis of measurement and simulation errors in structural system identification by observability techniques

A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

Structural identification with incomplete output‐only data and independence of measured information for shear‐type systems

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