Дисертації з теми "Subspace identification methods"
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1
Shi, Ruijie. "Subspace identification methods for process dynamic modeling /." *McMaster only, 2001.
2
Zhao, Yong. "Identification of ankle joint stiffness using subspace methods." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86800.
Анотація:
Studying joint stiffness against a compliant load is a difficult problem because the intrinsic and reflex torques cannot be measured separately experimentally. Moreover, the joint stiffness is operated within a closed loop because the ankle torque is fed back through the load to change the ankle position. In this thesis, a state space model for ankle joint stiffness is developed. Then, a discrete-time, subspace-based method is used to estimate this state space model for overall stiffness. By using appropriate instrumental variables, the subspace method can estimate the state space model for joint stiffness in both open-loop and in closed-loop conditions. This thesis also presents a subspace method to identify state space models for biomedical systems with short transients or systems with time-varying behaviors, from ensembles of short transients. The simulation and experimental results demonstrate that those algorithms provide accurate estimates under their respective conditions.L'étude de la rigidité articulaire en réponse à une charge est un problème difficile car les couples réflexes et intrinsèques ne peuvent pas être mesurés séparément expérimentalement. En outre, la rigidité articulaire opère en boucle fermée car le couple de la cheville est réinjectée à travers la charge pour modifier la position de la cheville. Dans cette thèse, un modèle d'espace d'état pour la rigidité articulaire de la cheville est développé. Une méthode sous-espace à temps discret est ensuite utilisée pour estimer ce modèle d'espace d'état pour la rigidité globale. En considérant les variables instrumentales appropriées, la méthode sous-espace permet d'estimer le modèle espace d'état pour la rigidité articulaire en boucles ouverte et fermée. Cette thèse présente également une méthode sous-espace pour identifier les modèles d'espace d'état pour les systèmes biomédicauxou les systèmes variant dans le temps caractérisés par des phénomènes transitoires de courte durée. Les simulations et les résultats expérimentaux démontrent que ces algorithmes fournissent des estimations précises en fonction de leurs conditions propres.
3
Chui, Nelson Loong Chik. "Subspace methods and informative experiments for system identification." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298794.
4
Dahlen, Anders. "Identification of stochastic systems : Subspace methods and covariance extension." Doctoral thesis, KTH, Mathematics, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.
5
Zhou, Ning. "Subspace methods of system identification applied to power systems." Laramie, Wyo. : University of Wyoming, 2005. http://proquest.umi.com/pqdweb?did=1095432761&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
6
Dahlén, Anders. "Identification of stochastic systems : subspace methods and covariance extension /." Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.
7
Lam, Xuan-Binh. "Uncertainty quantification for stochastic subspace indentification methods." Rennes 1, 2011. http://www.theses.fr/2011REN1S133.
Анотація:
In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios, and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the appropriate uncertainty bounds of these terms. In this thesis, the algorithms, that automatically compute the uncertainty bounds of modal parameters obtained from SSI of a structure based on vibration measurements, are presented. With these new algorithms, the uncertainty bounds of the modal parameters of some relevant industrial examples are computed. To quantify the statistical uncertainty of the obtained modal parameters, the statistical uncertainty in the data can be evaluated and propagated to the system matrices and, thus, to the modal parameters. In the uncertainty quantification algorithm, which is a perturbation-based method, it has been shown how uncertainty bounds of modal parameters can be determined from the covariances of the system matrices, which are obtained from some covariance of the data and the covariances of subspace matrices. In this thesis, several results are derived. Firstly, a novel and more realistic scheme for the uncertainty calculation of the mode shape is presented, the mode shape is normalized by the phase angle of the component having the maximal absolute value instead of by one of its components. Secondly, the uncertainty quantification is derived and developed for several identification methods, first few of them are covariance- and data-driven SSI. The thesis also mentions about Eigensystem Realization Algorithm (ERA), a class of identification methods, and its uncertainty quantification scheme. This ERA approach is introduced in conjunction with the singular value decomposition to derive the basic formulation of minimum order realization. Besides, the thesis supposes efficient algorithms to estimate the system matrices at multiple model orders, the uncertainty quantification is also derived for this new multi-order SSI method. Two last interesting sections of the thesis are discovering the uncertainty of multi-setups SSI algorithm and recursive algorithms. In summary, subspace algorithms are efficient tools for vibration analysis, fitting a model to input/output or output-only measurements taken from a system. However, uncertainty quantification for SSI was missing for a long time. The uncertainty quantification is very important feature for credibility of modal analysis exploitationEn analyse modale operationelle, les paramètres modaux (fréquence, amortissement, déforméees) peuvent être obtenus par des méthodes d'identification de type sous espaces et sont définis à une incertitude stochastique près. Pour évaluer la qualité des résultats obtenus, il est essentiel de connaître les bornes de confiance sur ces résultats. Dans cette thèse sont développés des algorithmes qui calcule automatiquement de telles bornes de confiance pour des paramètres modaux caractèristiques d'une structure mécanique. Ces algorithmes sont validés sur des exemples industriels significatifs. L'incertitude est tout d'abord calculé sur les données puis propagée sur les matrices du système par calcul de sensibilité, puis finalement sur les paramètres modaux. Les algorithmes existants sur lesquels se basent cette thèse dérivent l'incertitude des matrices du système de l'incertitude sur les covariances des entrées mesurées. Dans cette thèse, plusieurs résultats ont été obtenus. Tout d'abord, l'incertitude sur les déformées modales est obtenue par un schema de calcul plus réaliste que précédemment, utilisant une normalisation par l'angle de phase de la composante de valeur maximale. Ensuite, plusieurs méthodes de sous espaces et non seulement les méthodes à base de covariance sont considérées, telles que la méthode de réalisation stochastique ERA ainsi que la méthode UPC, à base des données. Pour ces méthodes, le calcul d'incertitude est explicité. Deu autres problèmatiques sont adressés : tout d'abord l'estimation multi ordre par méthode de sous espace et l'estimation à partir de jeux de données mesurées séparément. Pour ces deux problèmes, les schemas d'incertitude sont développés. En conclusion, cette thèse s'est attaché à développer des schemas de calcul d'incertitude pour une famille de méthodes sous espaces ainsi que pour un certain nombre de problèmes pratiques. La thèse finit avec le calcul d'incertitudes pour les méthodes récursives. Les méthodes sous espaces sont considérées comme une approche d'estimation robuste et consistante pour l'extraction des paramètres modaux à partir de données temporelles. Le calcul des incertitudes pour ces méthodes est maintenant possible, rendant ces méthodes encore plus crédible dans le cadre de l'exploitation de l'analyse modale
8
Nilsen, Geir Werner. "Topics in open and closed loop subspace system identification : finite data-based methods." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1752.
9
Ivanova, Elena. "Identification de systèmes multivariables par modèle non entier en utilisant la méthode des sous-espaces." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0561/document.
Анотація:
L’identification des systèmes par modèle non entier a été initiée dans les années 1990 et de nombreux résultats ont été obtenus depuis. Néanmoins, la plupart de ces résultats utilise les méthodes de la famille des méthodes à erreur de prédiction, basées sur la minimisation de la norme ℓ2 de l’erreur d’estimation. Apparues en 1996, les méthodes des sous-espaces sont relativement nouvelles dans la théorie de l’identification de systèmes linéaires. Basées sur des projections géométriques et l’algèbre linéaire, elles présentent une alternative intéressante aux méthodes classiques basées sur la régression linéaire ou non linéaire. Elles permettent d’estimer les matrices d’un modèle à base d’une représentation d’état. Dans le contexte des systèmes non entiers, la notion de pseudo-représentation d’état généralise la notion de représentation d’état en introduisant un paramètre supplémentaire qui est l’ordre commensurable.Actuellement, la méthode des sous-espaces pour des systèmes non entiers n’a cependant été appliquée que dans le domaine temporel. Elle est alors développée dans cette thèse pour une telle classe de systèmes dans le domaine fréquentiel. De plus, comme les systèmes non entiers sont des systèmes à temps continu, un filtrage des données est nécessaire pour respecter la causalité des signaux et pour pouvoir réaliser l’identification. Une étude comparative des différentes méthodes de filtrage dans le contexte de l’identification pour déduire leurs avantages et inconvénients est réalisée dans le domaine temporel. Enfin,les méthodes développées ont été appliquées à un système réel en diffusion thermique.Les modèles obtenus sont généralisés à des matériaux soumis à plusieurs flux de chaleur en entrée tout en considérant leur température en plusieurs points de mesuresThe identification of systems by fractional models was initiated in the 1990s and various results have been obtained since. Nevertheless, most of these results are based on prediction error methods (PEM) of identification, based on the minimization of the norm of the estimation error. Apparent in 1996, the subspace methods are relatively new in the theory of the identification of linear systems. Based on geometric projections and linear algebra, they present an alternative to classical methods based on linear or nonlinear regression. They allow estimating the matrices of the state-space representation of a system. In the context of fractional systems, a pseudo-state-space representation generalizes the notion of state-space representation by introducing an additional parameter which is the commensurable order.Currently, the subspace method for non-integer systems has only been applied inthe time domain. It is then developed in this thesis for such a class of systems in the frequency domain. Moreover, since non-integer systems are continuous time systems, datapre-filtering is necessary to respect the causality of the signals and to be able to realize the identification. A study of the different filtering methods in the context of subspaceidentification is then carried out in order to deduce their advantages and disadvantages in the time domain. Finally, the method has been applied to a thermal diffusion system.The obtained models are generalized for several input heat flows, considering their temperature available at several measurement points
10
Jorajuria, Corentin. "Estimation de l'amortissement des aubages en analyse modale opérationnelle." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0003.
Анотація:
Les objectifs du secteur aérien européen pour la réduction des impacts environnementaux conduisent à concevoir de nouveaux moteurs d'avion civil. Ces nouvelles conceptions peuvent induire des risques aéroélastiques plus sévères pour les soufflantes de turboréacteur. Dans ce contexte, comprendre et prédire les phénomènes de dissipation d'énergie constitue un enjeu industriel important. Comme ces phénomènes peuvent être variés et complexes, l'approche expérimentale prend une grande importance pour l'étude de l'amortissement. Ces travaux de thèse se concentrent sur l'estimation de l'amortissement au sein de soufflantes de turboréacteurs. Pour cela, ils traitent de méthodes d'estimation modale dans le domaine fréquentiel et temporel. Les problématiques d'estimation modale sont abordées grâce à un banc d'essais permettant de réaliser des mesures vibratoires sur une soufflante à l'échelle 1:1 en rotation sous vide et excitée par des actionneurs piézoélectriques. De plus, les méthodes d'identification de sous-espaces, présentant des avantages intéressants pour l'estimation des modes de soufflantes en rotation, sont traitées de manière plus spécifique. Les performances d'estimation de ces méthodes ont été évaluées sur des modèles numériques. Puis, ces méthodes ont été appliquées sur des mesures vibratoires de soufflante en rotation sous vide. Par ailleurs, les données d'essais en conditions de fonctionnement montrent que les excitations de l'environnement opérationnel peuvent induire des réponses transitoires significatives. En conséquence, nous avons étudié l'influence d'effets instationnaires sur la caractérisation modale grâce à des essais vibratoires pour lesquels les excitations présentent différents taux d'instationnarité. Enfin, les méthodes d'estimation montrant des résultats encourageants sur les essais en rotation sous vide ont été appliquées sur des données expérimentales obtenues en conditions opérationnellesEuropean goals to reduce air traffic environmental impacts leads to design new civilian turbojet engines. These new designs can result in more severe aeroelastic risks for turbojet engines. In this regard, understanding and predicting dissipation phenomena is a key industrial challenge. As these phenomena can be very wide and complex, experimental approaches take an important role to understand damping. This thesis focuses on the estimation of damping of fan of civilian turbojet engines. To this end, estimation methods in frequency and time domain have been studied. The estimation issues are addressed thanks to a test rig making possible to measure vibratory responses of rotating full-scale fan in vacuum conditions using piezoelectric excitations. Moreover, subspace identification methods, showing particular advantages for the estimation of modes of rotating fans, have been investigated more specifically. Estimation performances of these techniques have been assessed over numerical models. Then, these techniques have been applied over vibratory measurements of a rotating fan in vacuum conditions. Furthermore, experimental data of fans in operation show that excitations can induce significant transient responses. Accordingly, an experimental study evaluating the effect of unsteady responses over modal characterization has been carried out. This experimental study has been performed thanks to modal tests using excitations with different unsteady rate. Finally, estimation methods showing encouraging results over modal tests of a rotating fan in vacuum conditions have been applied over experimental data obtained in operational conditions
11
Srinivas, L. "FIR System Identification Using Higher Order Cumulants -A Generalized Approach." Thesis, Indian Institute of Science, 1994. https://etd.iisc.ac.in/handle/2005/637.
Анотація:
The thesis presents algorithms based on a linear algebraic solution for the identification of the parameters of the FIR system using only higher order statistics when only the output of the system corrupted by additive Gaussian noise is observed.
All the traditional parametric methods of estimating the parameters of the system have been based on the 2nd order statistics of the output of the system. These methods suffer from the deficiency that they do not preserve the phase response of the system and hence cannot identify non-minimum phase systems. To circumvent this problem, higher order statistics which preserve the phase characteristics of a process and hence are able to identify a non-minimum phase system and also are insensitive to additive Gaussian noise have been used in recent years.
Existing algorithms for the identification of the FIR parameters based on the higher order cumulants use the autocorrelation sequence as well and give erroneous results in the presence of additive colored Gaussian noise. This problem can be overcome by obtaining algorithms which do not utilize the 2nd order statistics.
An existing relationship between the 2nd order and any Ith order cumulants is generalized to a relationship between any two arbitrary k, Ith order cumulants. This new relationship is used to obtain new algorithms for FIR system identification which use only cumulants of order > 2 and with no other restriction than the Gaussian nature of the additive noise sequence. Simulation studies are presented to demonstrate the failure of the existing algorithms when the imposed constraints on the 2nd order statistics of the additive noise are violated while the proposed algorithms perform very well and give consistent results.
Recently, a new algebraic approach for parameter estimation method denoted the Linear Combination of Slices (LCS) method was proposed and was based on expressing the FIR parameters as a linear combination of the cumulant slices. The rank deficient cumulant matrix S formed in the LCS method can be expressed as a product of matrices which have a certain structure. The orthogonality property of the subspace orthogonal to S and the range space of S has been exploited to obtain a new class of algorithms for the estimation of the parameters of a FIR system. Numerical simulation studies have been carried out to demonstrate the good behaviour of the proposed algorithms.
Analytical expressions for the covariance of the estimates of the FIR parameters of the different algorithms presented in the thesis have been obtained and numerical comparison has been done for specific cases.
Numerical examples to demonstrate the application of the proposed algorithms for channel equalization in data communication and as an initial solution to the cumulant matching nonlinear optimization methods have been presented.
12
Srinivas, L. "FIR System Identification Using Higher Order Cumulants -A Generalized Approach." Thesis, Indian Institute of Science, 1994. http://hdl.handle.net/2005/637.
Анотація:
The thesis presents algorithms based on a linear algebraic solution for the identification of the parameters of the FIR system using only higher order statistics when only the output of the system corrupted by additive Gaussian noise is observed.
All the traditional parametric methods of estimating the parameters of the system have been based on the 2nd order statistics of the output of the system. These methods suffer from the deficiency that they do not preserve the phase response of the system and hence cannot identify non-minimum phase systems. To circumvent this problem, higher order statistics which preserve the phase characteristics of a process and hence are able to identify a non-minimum phase system and also are insensitive to additive Gaussian noise have been used in recent years.
Existing algorithms for the identification of the FIR parameters based on the higher order cumulants use the autocorrelation sequence as well and give erroneous results in the presence of additive colored Gaussian noise. This problem can be overcome by obtaining algorithms which do not utilize the 2nd order statistics.
An existing relationship between the 2nd order and any Ith order cumulants is generalized to a relationship between any two arbitrary k, Ith order cumulants. This new relationship is used to obtain new algorithms for FIR system identification which use only cumulants of order > 2 and with no other restriction than the Gaussian nature of the additive noise sequence. Simulation studies are presented to demonstrate the failure of the existing algorithms when the imposed constraints on the 2nd order statistics of the additive noise are violated while the proposed algorithms perform very well and give consistent results.
Recently, a new algebraic approach for parameter estimation method denoted the Linear Combination of Slices (LCS) method was proposed and was based on expressing the FIR parameters as a linear combination of the cumulant slices. The rank deficient cumulant matrix S formed in the LCS method can be expressed as a product of matrices which have a certain structure. The orthogonality property of the subspace orthogonal to S and the range space of S has been exploited to obtain a new class of algorithms for the estimation of the parameters of a FIR system. Numerical simulation studies have been carried out to demonstrate the good behaviour of the proposed algorithms.
Analytical expressions for the covariance of the estimates of the FIR parameters of the different algorithms presented in the thesis have been obtained and numerical comparison has been done for specific cases.
Numerical examples to demonstrate the application of the proposed algorithms for channel equalization in data communication and as an initial solution to the cumulant matching nonlinear optimization methods have been presented.
13
Bonis, Ioannis. "Optimisation and control methodologies for large-scale and multi-scale systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/optimisation-and-control-methodologies-for-largescale-and-multiscale-systems(6c4a4f13-ebae-4d9d-95b7-cca754968d47).html.
Анотація:
Distributed parameter systems (DPS) comprise an important class of engineering systems ranging from "traditional" such as tubular reactors, to cutting edge processes such as nano-scale coatings. DPS have been studied extensively and significant advances have been noted, enabling their accurate simulation. To this end a variety of tools have been developed. However, extending these advances for systems design is not a trivial task . Rigorous design and operation policies entail systematic procedures for optimisation and control. These tasks are "upper-level" and utilize existing models and simulators. The higher the accuracy of the underlying models, the more the design procedure benefits. However, employing such models in the context of conventional algorithms may lead to inefficient formulations. The optimisation and control of DPS is a challenging task. These systems are typically discretised over a computational mesh, leading to large-scale problems. Handling the resulting large-scale systems may prove to be an intimidating task and requires special methodologies. Furthermore, it is often the case that the underlying physical phenomena span various temporal and spatial scales, thus complicating the analysis. Stiffness may also potentially be exhibited in the (nonlinear) models of such phenomena. The objective of this work is to design reliable and practical procedures for the optimisation and control of DPS. It has been observed in many systems of engineering interest that although they are described by infinite-dimensional Partial Differential Equations (PDEs) resulting in large discretisation problems, their behaviour has a finite number of significant components , as a result of their dissipative nature. This property has been exploited in various systematic model reduction techniques. Of key importance in this work is the identification of a low-dimensional dominant subspace for the system. This subspace is heuristically found to correspond to part of the eigenspectrum of the system and can therefore be identified efficiently using iterative matrix-free techniques. In this light, only low-dimensional Jacobians and Hessian matrices are involved in the formulation of the proposed algorithms, which are projections of the original matrices onto appropriate low-dimensional subspaces, computed efficiently with directional perturbations.The optimisation algorithm presented employs a 2-step projection scheme, firstly onto the dominant subspace of the system (corresponding to the right-most eigenvalues of the linearised system) and secondly onto the subspace of decision variables. This algorithm is inspired by reduced Hessian Sequential Quadratic Programming methods and therefore locates a local optimum of the nonlinear programming problem given by solving a sequence of reduced quadratic programming (QP) subproblems . This optimisation algorithm is appropriate for systems with a relatively small number of decision variables. Inequality constraints can be accommodated following a penalty-based strategy which aggregates all constraints using an appropriate function , or by employing a partial reduction technique in which only equality constraints are considered for the reduction and the inequalities are linearised and passed on to the QP subproblem . The control algorithm presented is based on the online adaptive construction of low-order linear models used in the context of a linear Model Predictive Control (MPC) algorithm , in which the discrete-time state-space model is recomputed at every sampling time in a receding horizon fashion. Successive linearisation around the current state on the closed-loop trajectory is combined with model reduction, resulting in an efficient procedure for the computation of reduced linearised models, projected onto the dominant subspace of the system. In this case, this subspace corresponds to the eigenvalues of largest magnitude of the discretised dynamical system. Control actions are computed from low-order QP problems solved efficiently online.The optimisation and control algorithms presented may employ input/output simulators (such as commercial packages) extending their use to upper-level tasks. They are also suitable for systems governed by microscopic rules, the equations of which do not exist in closed form. Illustrative case studies are presented, based on tubular reactor models, which exhibit rich parametric behaviour.
14
Gautier, Guillaume. "Diagnostic vibratoire des systèmes mécaniques par subspace fitting." Thesis, Tours, 2015. http://www.theses.fr/2015TOUR4026/document.
Анотація:
Dans ce mémoire, une méthode subspace fitting (SF) destinée à l’identification des paramètres mécaniques et l’évaluation de l’état de santé de structures vibrantes, est présentée. La méthode SF s’attache à extraire, à partir des méthodes d’identification par sous-espaces (4SID), une matrice d’observabilité du système et de la corréler, au sens de la norme, à une matrice d’observabilité théorique. L’originalité de ce travail est de construire la matrice d’observabilité théorique sur la base d’un modèle éléments finis (EF) de la structure considérée. En ajustant les paramètres inconnus du modèle EF, les propriétés mécaniques de la structure vibrante sont identifiées. Les coûts de calcul d’une telle procédure sont réduits en considérant une méthode de réduction de modèle basée sur la position des excitations et des capteurs. La méthode est évaluée pour l’identification des fréquences propres d’une structure vibrante. Des applications numériques et expérimentales s’attachent à montrer la pertinence d’une telle approche. En particulier, il est mis en évidence que la méthode SF permet d’identifier précisément les fréquences propres d’une structure, pour des niveaux de bruit importantsIn this thesis, a subspace fitting (SF) method is presented for the identification of mechanical parameters and assessment of the health condition of vibrating structures. The SF method attempts to extract, from subspace identification methods (4SID), a system observability matrix of the system and correlate them with a theoretical observability matrix. The originality of this work is to obtain the theoretical observability matrix from a finite element model (EF) of the structure. By adjusting unknown parameters of the FE model, the mechanical properties of the vibrating structure are identified. Computational costs of such a procedure are reduced by considering a model reduction method based on the excitations and sensors location. The method is evaluated for the identification of natural frequencies of a vibrating structure. Numerical and experimental applications are assessed to show the relevance of such an approach. In particular, it is highlighted that the SF method can accurately identify the natural frequencies of a structure to high noise levels
15
Zhao, Wancheng. "A Structural Damage Identification Method Based on Unified Matrix Polynomial Approach and Subspace Analysis." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1206652627.
16
Kim, Yoonhwak. "The Effects of Assumption on Subspace Identification using Simulation and Experiment Data." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5666.
Анотація:
In the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of sensors is limited by cost and difficulty of data archive. Furthermore, some informative data has technical difficulty when measuring input force and, even if obtaining the desired data were possible, it could include a lot of noise in the measuring data. As a result, researchers have developed many analytical tools with limited informative data. Subspace identification method is used one of tools in these achievements. Subspace identification method includes three different approaches: Deterministic Subspace Identification (DSI), Stochastic Subspace Identification (SSI), and Deterministic-Stochastic Subspace Identification (DSSI). The subspace identification method is widely used for fast computational speed and its accuracy. Based on the given information, such as output only, input/output, and input/output with noises, DSI, SSI, and DSSI are differently applied under specific assumptions, which could affect the analytical results. The objective of this study is to observe the effect of assumptions on subspace identification with various data conditions. Firstly, an analytical simulation study is performed using a six-degree-of-freedom mass-damper-spring system which is created using MATLAB. Various conditions of excitation insert to the simulation test model, and its excitation and response are analyzed using the subspace identification method. For stochastic problems, artificial noise is contained to the excitation and followed the same steps. Through this simulation test, the effects of assumption on subspace identification are quantified. Once the effects of the assumptions are studied using the simulation model, the subspace identification method is applied to dynamic response data collected from large-scale 12-story buildings with different foundation types that are tested at Tongji University, Shanghai, China. Noise effects are verified using three different excitation types. Furthermore, using the DSSI, which has the most accurate result, the effect of different foundations on the superstructure are analyzed.M.S.
Masters
Civil, Environmental, and Construction Engineering
Engineering and Computer Science
Civil Engineering; Structures and Geotechnical Engineering
17
GANDINO, EDOARDO. "Diagnostics of machines and structures: dynamic identification and damage detection." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506356.
Анотація:
This research work deals with damage detection of engineering machines and structures. This topic, developed in particular for bearing diagnostics in the first part of the work, is strictly related to dynamic identification when structures are considered. Thus, subspace-based methods are investigated in the second part of the work, with particular attention to nonlinear system identification.
Changes in operational and environmental conditions for structures (such as air temperature, temperature gradients, humidity, wind, etc.) or machines (such as oil temperature, loads, rotating regimes, etc.) are known to have considerable effects on signal features and, consequently, on the reliability of diagnostics. Useful tools for eliminating this influence are provided by a Principal Component Analysis (PCA)-based method for damage detection.
The same way as many published works applied PCA-based diagnostics of structures, in this research work a bearing diagnostic application is considered. After a detailed description of the test rig, the huge amount of acquired data, on several different damaged bearings, is investigated. Results are useful for giving an overview on how the PCA-based method for damage detection can be applied on a complicated real-life machine.
In general cases of real structures, the application of efficient identification techniques is crucial for correctly exploiting the capabilities of the PCA-based method for damage detection. Moreover, in many cases damage causes a structure that initially behaves in a predominantly linear manner to exhibit nonlinear response: the application of nonlinear system identification methods to the feature-extraction process can also be used as a direct detection of damage. For these reasons, a detailed study of the nonlinear subspace-based identification methods is presented in the second part of this work.
Since the classical data-driven subspace method can in some cases be affected by memory limitation problems, two alternative techniques are developed and demonstrated on numerical and experimental applications. Moreover, a modal counterpart of the nonlinear subspace identification method is introduced, to extend its relevance also to realistic large engineering structures.
In a conclusive application, two of the main sources of non-stationary dynamics, namely the time-variability and the presence of nonlinearity, are analysed through the analytical and experimental study of a time-varying inertia pendulum, having a nonlinear equation of motion due to its large swinging amplitudes.
18
Jenča, Pavol. "Identifikace parametrů elektrických motorů metodou podprostorů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219678.
Анотація:
The electrical motors parameters identification is solved in this master’s thesis using subspace based methods. Electrical motors are simulated in Matlab/Simulink interactive environment, specifically permanent magnet DC motor and permanent magnet synchronous motor. Identification is developed in Matlab interactive environment. Different types of subspace algorithms are used for the estimation of parameters. Results of subspace parameters estimation are compared with least squares parameters estimation. The thesis describes subspace method, types of subspace algorithms, used electrical motors, nonlinear approach of identification and comparation of parameters identification.
19
Mestrah, Ali. "Identification de modèles sous forme de représentation d'état pour les systèmes à sortie binaire." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC255.
Анотація:
Cette thèse porte sur la modélisation paramétrique des systèmes linéaires invariants à partir de mesures binaires de la sortie. Ce problème demodélisation est abordée via l’usage des méthodes des sous-espaces. Ces méthodes permettent l’estimation de modèles sous forme de représentation d’état,un des avantages de ces méthodes étant que leur mise en œuvre ne nécessite pas la connaissance préalable de l’ordre du système. Ces méthodes ne sontinitialement pas adaptées au traitement de données binaires, l’objectif de cette thèse est ainsi leur adaptation à ce contexte d’identification. Dans cette thèse nousproposons trois méthodes des sous-espaces. Les propriétés de convergence de deux d’entre elles sont établies. Des résultats de simulations de Monte-Carlo sontprésentés afin de montrer les bonnes performances, mais aussi les limites, de ces méthodesThis thesis focuses on parametric modeling of invariant linear systems from binary output measurements. This identification problem is addressed via the use ofsubspace methods. These methods allow the estimation of state-space models, an added benefit of these methods being the fact that their implementation doesnot require the prior knowledge of the order of the system. These methods are initially adapted to high resolution data processing, the objective of this thesis istherefore their adaptation to the identification using binary measurements. In this thesis we propose three subspace methods. Convergence properties of two ofthem are established. Monte Carlo simulation results are presented to show the good performance, but also limits, of these methods
20
PIOLDI, Fabio. "Time and Frequency Domain output-only system identification from earthquake-induced structural response signals." Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77137.
Анотація:
Output-only Time and Frequency Domain system identification techniques are developed in this doctoral dissertation towards the challenging assessment of current structural dynamic properties of buildings from earthquake-induced structural response signals, at simultaneous heavy damping. Three different Operational Modal Analysis (OMA) techniques, namely a refined Frequency Domain Decomposition (rFDD) algorithm, an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) procedure and a novel Full Dynamic Compound Inverse Method (FDCIM) are formulated and implemented within MATLAB, and exploited for the strong ground motion modal dynamic identification of selected buildings.
First, the three OMA methods are validated by the adoption of synthetic earthquake-induced structural response signals, generated from numerical integration on benchmark linear shear-type frames. Then, real seismic response signals are effectively processed, by getting even closer to real Earthquake Engineering identification scenarios. In the end, the three OMA methods are systematically applied and compared.
The present thesis demonstrates the reliability and effectiveness of such advanced OMA methods, as convenient output-only modal identification tools for Earthquake Engineering and Structural Health Monitoring purposes.
21
Lassami, Nacerredine. "Représentations parcimonieuses et analyse multidimensionnelle : méthodes aveugles et adaptatives." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0139.
Анотація:
Au cours de la dernière décennie, l’étude mathématique et statistique des représentations parcimonieuses de signaux et de leurs applications en traitement du signal audio, en traitement d’image, en vidéo et en séparation de sources a connu une activité intensive. Cependant, l'exploitation de la parcimonie dans des contextes de traitement multidimensionnel comme les communications numériques reste largement ouverte. Au même temps, les méthodes aveugles semblent être la réponse à énormément de problèmes rencontrés récemment par la communauté du traitement du signal et des communications numériques tels que l'efficacité spectrale. Aussi, dans un contexte de mobilité et de non-stationnarité, il est important de pouvoir mettre en oeuvre des solutions de traitement adaptatives de faible complexité algorithmique en vue d'assurer une consommation réduite des appareils. L'objectif de cette thèse est d'aborder ces challenges de traitement multidimensionnel en proposant des solutions aveugles de faible coût de calcul en utilisant l'à priori de parcimonie. Notre travail s'articule autour de trois axes principaux : la poursuite de sous-espace principal parcimonieux, la séparation adaptative aveugle de sources parcimonieuses et l'identification aveugle des systèmes parcimonieux. Dans chaque problème, nous avons proposé de nouvelles solutions adaptatives en intégrant l'information de parcimonie aux méthodes classiques de manière à améliorer leurs performances. Des simulations numériques ont été effectuées pour confirmer l’intérêt des méthodes proposées par rapport à l'état de l'art en termes de qualité d’estimation et de complexité calculatoireDuring the last decade, the mathematical and statistical study of sparse signal representations and their applications in audio, image, video processing and source separation has been intensively active. However, exploiting sparsity in multidimensional processing contexts such as digital communications remains a largely open problem. At the same time, the blind methods seem to be the answer to a lot of problems recently encountered by the signal processing and the communications communities such as the spectral efficiency. Furthermore, in a context of mobility and non-stationarity, it is important to be able to implement adaptive processing solutions of low algorithmic complexity to ensure reduced consumption of devices. The objective of this thesis is to address these challenges of multidimensional processing by proposing blind solutions of low computational cost by using the sparsity a priori. Our work revolves around three main axes: sparse principal subspace tracking, adaptive sparse source separation and identification of sparse systems. For each problem, we propose new adaptive solutions by integrating the sparsity information to the classical methods in order to improve their performance. Numerical simulations have been conducted to confirm the superiority of the proposed methods compared to the state of the art
22
Liu, Yi-Cheng, and 劉奕成. "Application of Covariance Driven Stochastic Subspace Identification Method." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/33712859772337110292.
Анотація:
碩士國立臺灣大學
土木工程學研究所
99
In this research the application of output-only system identification technique known as Stochastic Subspace Identification (SSI) algorithms in civil structures is carried out. With the aim of finding accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data matrix. A sensitivity study of the implementation of SSI through stabilization diagram is firstly carried out, different scenarios such as noise effect, nonlinearity, time-varying systems and closely-spaced frequencies are considered. Comparison between different SSI approaches was also discussed. In the following, the identification task of a real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures is carried out, for which the capacity of Covariance-driven SSI algorithm (SSI-COV) will be demonstrated. The introduction of a subspace preprocessing algorithm known as Singular Spectrum Analysis (SSA) can greatly enhance the identification capacity, which in conjunction with SSI-COV is called the SSA-SSI-COV method, it also allows the determination of the best system order. The objective of the second part of this research is to develop on-line system parameter estimation and damage detection technique through Recursive Covariance-driven Stochastic Subspace identification (RSSI-COV) approach. The Extended Instrumental Variable version of Projection Approximation Subspace Tracking algorithm (EIV-PAST) is taking charge of the system-related subspace updating task. To further reduce the noise corruption in field experiments, the data pre-processing technique called recursive Singular Spectrum Analysis technique (rSSA) is developed to remove the noise contaminant measurements, so as to enhance the stability of data analysis. Through simulation study as well as the experimental research, both RSSI-COV and rSSA-SSI-COV method are applied to identify the dynamic behavior of systems with time-varying characteristics, the reliable control parameters for the model are examined. Finally, these algorithms are applied to track the evolution of modal parameters for: (1) shaking table test of a 3-story steel frame with instantaneous stiffness reduction. (2) Shaking table test of a 1-story 2-bay reinforced concrete frame, both under earthquake excitation, and at last, (3) damage detection and early warning of an experimental steel bridge under continuous scour.
23
Lin, Chiung-Chin, and 林炅嶔. "Subspace Method for Blind Channel Identification with Unknown Channel Order." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/n347kc.
Анотація:
碩士國立交通大學
電機與控制工程系所
92
Subspace method for blind channel identification of communication channels usually require that the channel order is known. This requirement is not realistic in practice. We propose algorithms for the determination of channel order, when only an upper bound of the channel order is known, so that the subspace method can be used for channel identification.
24
KUMAR, VIPIN. "A STUDY ON ELECTROMECHANICAL MODE ESTIMATION OF POWER SYSTEM BY SUBSPACE IDENTIFICATION METHOD." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16251.
Анотація:
The thesis is focused around the study of electromechanical oscillations in the power system which are primarily responsible for instability in the power system. The analysis is carried out on two platforms – DigSILENT’s Power Factory and Matlab. In this thesis, popular Kundur’s two area system is considered for evaluation of system parameters, considering a three phase fault on the tie lines. Modal analysis of a system is gaining popularity in the analysis of system’s eigen values, that are interpolated to obtain modes present in the system. The two platforms involved are communicated via comma separated values (.csv) files.
Power factory is used in this analysis of two area system. A two area model is implemented consisting of four generators and eleven bus system. Phasor simulation is executed on the model for the three phase fault, thus obtaining graphs of the simulation for tie-line power, rotors speed, and rotor angle with respect to time. Moreover the simulation is extended by taking modal analysis of the current system in operation. Modal properties of the system depicts the behaviour of the system oscillations, the system properties thus obtained shows the system under consideration is stable.
Matlab utilizes the fact that the system under consideration is a linear discrete time system. It explores the concepts of linear algebra for the evaluation of system modal properties by subspace identification method. Matlab is used for study of the two area system undergoing a transient by Subspace identification (SSI) and Recursive adaptive subspace identification (RASSI) method. RASSI method is recursive method that is applied for the continuous monitoring of the system modal properties with the synchrophasor data.
A comparison of these methods has been carried out in this work.
25
HUANG, TENG-YI, and 黃騰毅. "Application of Subspace Identification Method on Dynamic System Modeling of Machine Tool and Evaluation of Cutting Performance." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/se8vzf.
Анотація:
博士國立中正大學
機械工程系研究所
107
The motivation of the dissertation is to develop intelligent manufacturing system of the machine tool and to build the virtual machine tool model based on the industrial 4.0 production mode. The dynamic model which integrates servo interpolation, machine structure, and feed drive system is proposed and the cutting dynamics is also included to investigate vibration suppression and contour error estimation of the machining trajectory. In building the electrical system, not only the servo control but also the nonlinear friction effect is included simultaneously. In establishing the feed drive system, the paper adopts a novel experimental approach instead of using a non-traditional theoretical derivation. The approach is to capture the signal from the interpolator which could compute the path acceleration and deceleration, corner deceleration and other information with synchronous measurement of the acceleration of the working platform using the accelerometer. By applying the subspace system identification method (SIM), the structural and axis driving characteristics of the feed drive system can be realized in the virtual dynamic model. By testing different trajectories using different interpolation parameters, it shows that the subspace model can accurately predict the vibration of the XY table with the RMS (root mean square) error being less than 20%. The further approach is to measure the axis position and the relative position between the spindle tool center (TCP) and platform by the KGM such that the TCP dynamic response and the contour of the cutting path can be evaluated. Besides of building the complete dynamic model, the dissertation also investigates the cutting force simulation and the mechanism of chatting. By integrating the structural, transmission, servo and cutting dynamics, it is wished that the geometric accuracy of workpiece could be predicted. Furthermore, the parameters of interpolator are optimized such that the machining time can be minimized under the given contour errors.
26
Lee, How-Ping, and 李灝平. "Blind Identification of MIMO Systems Using Fast Kurtosis Maximization Algorithm and a Second-Order Statistics Based Subspace Method." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/35017127022244387682.
Анотація:
碩士國立清華大學
通訊工程研究所
91
Blind identification (BID) of multiple input multiple output systems is purposed to estimate an unknow system by using only the measurements. In general, the approaches of BID can be partitioned into two categories─ second-order statistics based algorithms, and higher-order statistics based algorithms. The advantages of second-order statistics based algorithms are shorter data lenght requirement, existence of closed form solution while the disadvantages are sensitivity to noise, requirement of prior imformation about channel lengths, more restrictions on systems. The advantages of higher-order statistics based algorithms are insensitivity to noise, less restrictions on systems, while the disadvantages are longer data length requirement, high computational load. Gorokhov and Loubaton have proposed a second-order statistics based subspace method for blind identification of an unknown K-input P-output (P either larger than or equal to K and K larger than 1) FIR system with the K input signals being mutually independent and temporally independent identically distributed (i.i.d.). However, only a partial system estimate can be obtained using their method. That is, the system estimate will be the unknown system multiplied an unknown ambiguity matrix R(z). In the case of equal channels, R(z) will be a constant nonsingular matrix and in the case of different channel lengths, R(z) will be a nonsingular upper triangular matrix. This thesis is concerned with resolving the ambiguity matrix R(z) embedded in the system estimate obtained by Gorokhov and Louba-ton’s subspace method. An approach is proposed in this thesis by utilizing Chi and Chen’s fast kurtosis maximization algorithm to estimate the inverse of the ambiguity matrix; meanwhile, the unknown system can thus be perfectly identified.
27
Wang, Sheng-Wei, and 王勝威. "An Improved Stochastic Subspace Identification Method Based on Alternative Stabilization Diagram and Hierarchical Sifting Process for Applications in Several Types of Civil Structures." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/56531512683909950024.
Анотація:
博士國立雲林科技大學
工程科技研究所
104
This study first establishes a new methodology based on the covariance type of stochastic subspace identification for extensively identifying the modal parameters of a stay cable. Several details of choosing the parameters in performing SSI are examined. An important discovery is that the lower limit for setting the time lag parameter can be decided by the ratio of the fundamental period of cable to the sampling time increment for a valid identification with the conventional stabilization diagram. Inspired by the above criterion, an alternative stabilization diagram is further proposed to more conveniently distinguish stable modal parameters of cable. A hierarchical process with three stages of sifting is then developed to systematically and automatically extract reliable modal parameters from the alternative stabilization diagram. Demonstrated by analyzing the ambient vibration measurements for three stay cables of Chi-Lu Bridge, the feasibility of the new methodology is verified with successfully obtaining the modal frequencies, damping ratios, and mode shape ratios for almost all the cable modes in the examined frequency range. The same approach is then generalized to the applications in other types of civil structures such as bridge decks or buildings. The measurements from different types of structures are systematically investigated to extend the applicability of the newly discovered criterion for the time lag parameter to ensure stable identification results. Such a criterion is first validated for its applications with single measurements from stay cables, bridge decks, and buildings. Regarding multiple measurements, it is found that the predicted threshold works well for the cases of stay cables and buildings, but makes an evident overestimation for the case of bridge decks. This discrepancy is further explained by the fact that the deck vibrations are induced by multiple excitations independently coming from the passing traffic. In addition, the computation efficiency of the alternative stabilization diagram is also explored to certify that appropriate down sampling can be applied to significantly reduce the computational cost without losing accuracy. Finally, the improved SSI algorithm developed in this study is further applied to investigate several sets of known and blind monitoring data from Ting Kau Bridge under different wind excitation conditions for assessing a benchmark problem launched to explore its mode identifiability. The evaluation of delicately selected cases clearly distinguishes the effect of traffic excitation on the identifiability of the targeted deficient mode from the effect of wind excitation. An additional upper limit for the vertical acceleration amplitude at deck, mainly induced by the passing traffic, is subsequently suggested to supplement the previously determined lower limit for the wind speed. The analysis incorporating the tower measurements solidly verifies that the deficient mode is actually induced by the motion of the central tower. Moreover, it is also confirmed that this mode can be stably identified under all the circumstances with the addition of tower measurements.