Model based fault detection for two-dimensional systems

Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial systems. Extensive research has been carried out on FDI for one dimensional (1-D) systems, where variables vary only with time. The existing FDI strategies are mainly focussed on 1-D systems and can generally be classified as model based and process history data based methods. In many industrial systems, the state variables change with space and time (e.g., sheet forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems represent a great challenge in both theoretical development and applications and only limited research results are available. In this thesis, model based fault detection strategies for 2-D systems have been investigated based on the F-M and the Roesser models. A dead-beat observer based fault detection has been available for the F-M model. In this work, an observer based fault detection strategy is investigated for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique, a dead-beat observer is developed and the state estimate from the observer is then input to a residual generator to monitor occurrence of faults. An enhanced realization technique is combined to achieve efficient fault detection with reduced computations. Simulation results indicate that the proposed method is effective in detecting faults for systems without disturbances as well as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems but strict conditions are required in order for an observer and a residual generator to exist. These strict conditions may not be satisfied for some systems. The effect of process noises are also not considered in the observer based fault detection approaches for 2-D systems. To overcome the disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation error variances. Based on the state estimate from the Kalman filter, a residual is generated reflecting fault information. A model is formulated for the relation of the residual with faults over a moving evaluation window. Simulations are performed on two F-M models and results indicate that faults can be detected effectively and efficiently using the Kalman filter based fault detection. In the observer based and Kalman filter based fault detection approaches, the residual signals are used to determine whether a fault occurs. For systems with complicated fault information and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component analysis (DPCA). Based on historical data, the reference residuals are first generated using either the observer or the Kalman filter based approach. Based on the residual time-lagged data matrices for the reference data, the principal components are calculated and the threshold value obtained. In online applications, the T2 value of the residual signals are compared with the threshold value to determine fault occurrence. Simulation results show that applying DPCA to evaluation of 2-D residuals is effective.Doctoral These

Realization of 2D convolutional codes of rate 1/n by separable Roesser models

In this paper, two-dimensional convolutional codes constituted by sequences in where is a finite field, are considered. In particular, we restrict to codes with rate and we investigate the problem of minimal dimension for realizations of such codes by separable Roesser models. The encoders which allow to obtain such minimal realizations, called R-minimal encoders, are characterized

Linear Algebra Methods for the Control of Multidimensional Systems

The purpose of the thesis is to develop a comprehensive theory of the geometric control for N-dimensional systems. Two possible representations and their structural invariance properties of 2-D systems will be considered and generalised to the N-dimensional case: the Fornasini-Marchesini first order model and Fornasini-Marchesini second order model. In addition, necessary and sufficient conditions for the existence of solutions for the implicit 2-D Fornasini-Marchesini models will be provided, and generalised to the N-dimensional case

Realizations of non-commutative rational functions around a matrix centre, II: The lost-abbey conditions

In a previous paper the authors generalized classical results of minimal realizations of non-commutative (nc) rational functions, using nc Fornasini--Marchesini realizations which are centred at an arbitrary matrix point. In particular, it was proved that the domain of regularity of a nc rational function is contained in the invertibility set of a corresponding pencil of any minimal realization of the function. In this paper we prove an equality between the domain of a nc rational function and the domain of any of its minimal realizations. As for evaluations over stably finite algebras, we show that the domain of the realization w.r.t any such algebra coincides with the so called matrix domain of the function w.r.t the algebra. As a corollary we show that the domain of regularity and the stable extended domain coincide. In contrary to both the classical case and the scalar case -- where every matrix coefficients which satisfy the controllability and observability conditions can appear in a minimal realization of a nc rational function -- the matrix coefficients in our case have to satisfy certain equations, called linearized lost-abbey conditions, which are related to Taylor--Taylor expansions in nc function theory

Realizações mínimas de espaço de estados de códigos convolucionais 2D

Doutoramento em MatemáticaIn this thesis we consider two-dimensional (2D) convolutional codes. As happens in the one-dimensional (1D) case one of the major issues is obtaining minimal state-space realizations for these codes. It turns out that the problem of minimal realization of codes is not equivalent to the minimal realization of encoders. This is due to the fact that the same code may admit different encoders with different McMillan degrees. Here we focus on the study of minimality of the realizations of 2D convolutional codes by means of separable Roesser models. Such models can be regarded as a series connection between two 1D systems. As a first step we provide an algorithm to obtain a minimal realization of a 1D convolutional code starting from a minimal realization of an encoder of the code. Then, we restrict our study to two particular classes of 2D convolutional codes. The first class to be considered is the one of codes which admit encoders of type n 1. For these codes, minimal encoders (i.e., encoders for which a minimal realization is also minimal as a code realization) are characterized enabling the construction of minimal code realizations starting from such encoders. The second class of codes to be considered is the one constituted by what we have called composition codes. For a subclass of these codes, we propose a method to obtain minimal realizations by means of separable Roesser models.Nesta tese consideramos códigos convolucionais a duas dimensões (2D). Como acontece no caso unidimensional (1D) uma das questões fundamentais neste contexto diz respeito à obtenção de realizações mínimas de espaço de estados para estes códigos. O problema da realizacão mínima de códigos não é equivalente ao problema da realizacão mínima de codificadores. Tal acontece uma vez que um dado código admite diferentes codificadores com diferentes graus de McMillan. Nesta tese, focamos a nossa atencão no estudo da minimalidade de realizações de códigos convolucionais 2D através de modelos de Roesser separáveis. Tais modelos podem ser encarados como a conexão em série de dois sistemas 1D. Numa primeira fase propomos um procedimento que possibilita obter realizações mínimas de um código convolutional 1D a partir de realizações mínimas de um codificador desse código. De seguida, restringimos o nosso estudo a duas classes particulares de códigos convolucionais 2D. A primeira classe a ser considerada é a classe de códigos que admite codificadores do tipo n 1. Para estes códigos, são caracterizados os codificadores mínimos (i.e. codificadores para os quais uma realização mínima também é mínima enquanto realização do código), possibilitando a construção de realizações mínimas de códigos a partir dos seus codificadores mínimos. A segunda classe a ser considerada é a classe constituída por códigos a que demos o nome de "composition codes". Para uma subclasse destes códigos, propomos um método de obtenção de realizações mínimas através de modelos de Roesser separáveis

Matrix fraction descriptions in convolutional coding

Doutoramento em MatemáticaOs objectos de estudo desta tese são os códigos convolucionais sobre um corpo, constituídos por sequências com suporte compacto à esquerda. Aplicando a abordagem comportamental à teoria dos sistemas, é obtida uma nova definição de código convolucional baseada em propriedades estruturais do próprio código. Os codificadores e os formadores de síndrome de um código convolucional são, respectivamente, as representações de imagem e as representações de núcleo do código. As suas estruturas e propriedades são estudadas, utilizando representações matriciais fraccionárias (RMF's). Seguidamente, são analisados os codificadores e formadores de síndrome minimais de um código convolucional, sendo apresentada uma parametrização simples das suas RMF's. Mostra-se também como obter todos os codificadores minimais de um código convolucional por aplicação de realimentação estática do estado e précompensação. De modo análogo, obtêm-se todos os formadores de síndrome minimais utilizando injecção da saída e pós-compensação. Finalmente, estudam-se os codificadores desacoplados de um código convolucional, que estão directamente ligados à sua decomposição. Apresenta-se um algoritmo para determinação de um codificador desacoplado maximal, que permitirá obter a decomposição máxima do código. Quando se restringe a análise dos codificadores desacoplados aos minimais, obtém-se um codificador canónico desacoplado e parametriza-se, utilizando RMF's, todos os codificadores minimais que apresentam grau máximo de desacoplamento.The objects of study of this thesis are the convolutional codes over a field, constituted by left compact sequences. To define a convolutional code we consider the behavioral approach to systems theory, and present a new definition of convolutional code, taking into account its structural properties. Matrix Fractions Descriptions (MFD’s) are used as a tool for investigating the structure of the encoders and the syndrome formers of a convolutional code, which are, respectively, the image and the kernel representations of the code. Next, we concentrate on the study of the minimal encoders and syndrome formers, and obtain a simple parametrization of their MFD’s. We also show that static feedback and precompensation allow to obtain all minimal encoders of the code. The same is done for the minimal syndrome formers, using output injection and postcompensation. Finally, we analyse the decoupled encoders of a convolutional code, which are associated with code decomposition. We provide an algorithm to determine a maximally decoupled encoder, and, consequently, the finest decomposition of the code. Restricting to minimal decoupled encoders, we first obtain a canonical decoupled one, and parametrize, via MFD’s, all minimal decoupled encoders realizing the finest decomposition of the code

Relationships between digital signal processing and control and estimation theory

Bibliography: leaves 83-97.NASA Grant NGL-22-009-124 and NSF Grant GK-41647.Alan S. Willsky

Matrix Fraction Descriptions in Convolutional Coding

Doutoramento em MatemáticaOs objectos de estudo desta tese são os códigos convolucionais sobre um corpo, constituídos por sequências com suporte compacto à esquerda. Aplicando a abordagem comportamental à teoria dos sistemas, é obtida uma nova definição de código convolucional baseada em propriedades estruturais do próprio código. Os codificadores e os formadores de síndrome de um código convolucional são, respectivamente, as representações de imagem e as representações de núcleo do código. As suas estruturas e propriedades são estudadas, utilizando representações matriciais fraccionárias (RMF's). Seguidamente, são analisados os codificadores e formadores de síndrome minimais de um código convolucional, sendo apresentada uma parametrização simples das suas RMF's. Mostra-se também como obter todos os codificadores minimais de um código convolucional por aplicação de realimentação estática do estado e précompensação. De modo análogo, obtêm-se todos os formadores de síndrome minimais utilizando injecção da saída e pós-compensação. Finalmente, estudam-se os codificadores desacoplados de um código convolucional, que estão directamente ligados à sua decomposição. Apresenta-se um algoritmo para determinação de um codificador desacoplado maximal, que permitirá obter a decomposição máxima do código. Quando se restringe a análise dos codificadores desacoplados aos minimais, obtém-se um codificador canónico desacoplado e parametriza-se, utilizando RMF's, todos os codificadores minimais que apresentam grau máximo de desacoplamento.The objects of study of this thesis are the convolutional codes over a field, constituted by left compact sequences. To define a convolutional code we consider the behavioral approach to systems theory, and present a new definition of convolutional code, taking into account its structural properties. Matrix Fractions Descriptions (MFD’s) are used as a tool for investigating the structure of the encoders and the syndrome formers of a convolutional code, which are, respectively, the image and the kernel representations of the code. Next, we concentrate on the study of the minimal encoders and syndrome formers, and obtain a simple parametrization of their MFD’s. We also show that static feedback and precompensation allow to obtain all minimal encoders of the code. The same is done for the minimal syndrome formers, using output injection and postcompensation. Finally, we analyse the decoupled encoders of a convolutional code, which are associated with code decomposition. We provide an algorithm to determine a maximally decoupled encoder, and, consequently, the finest decomposition of the code. Restricting to minimal decoupled encoders, we first obtain a canonical decoupled one, and parametrize, via MFD’s, all minimal decoupled encoders realizing the finest decomposition of the code

Structure-Preserving Model Reduction of Physical Network Systems

This paper considers physical network systems where the energy storage is naturally associated to the nodes of the graph, while the edges of the graph correspond to static couplings. The first sections deal with the linear case, covering examples such as mass-damper and hydraulic systems, which have a structure that is similar to symmetric consensus dynamics. The last section is concerned with a specific class of nonlinear physical network systems; namely detailed-balanced chemical reaction networks governed by mass action kinetics. In both cases, linear and nonlinear, the structure of the dynamics is similar, and is based on a weighted Laplacian matrix, together with an energy function capturing the energy storage at the nodes. We discuss two methods for structure-preserving model reduction. The first one is clustering; aggregating the nodes of the underlying graph to obtain a reduced graph. The second approach is based on neglecting the energy storage at some of the nodes, and subsequently eliminating those nodes (called Kron reduction).</p

Measurements, Models, Systems and Design

531 s.