Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

Previously, conventional state estimation techniques have been used for state estimation in power systems. These conventional methods are based on steady state models. As a result of this, power system dynamics during disturbances or transient conditions are not adequately captured. This makes it challenging for operators in control centers to perform visual tracking of the system, proper fault diagnosis and even take adequate preemtive control measures to ensure system stability during voltage dips. Another challenge is that power systems are nonlinear in nature. There are multiple power components in operation at any given time making the system highly dynamic in nature. Consequently, the need to study and implement better dynamic estimation tools that capture system dynamics during disturbances and transient conditions is necessary. For this thesis work, we present the Unscented Kalman Filter (UKF) which integrates Unscented Transformation (UT) to Kalman Filtering. Our algorithm takes as input the output of a synchronous machine modeled in MATLAB/Simulink as well as data from a PMU device assumed to be installed at the terminal bus of the synchronous machine, and estimate the dynamic states of the system using a Kalman Filter. We have presented a detailed and analytical study of our proposed algorithm in estimating two dynamic states of the synchronous machine, rotor angle and rotor speed. Our study and result shows that our proposed methodology has better efficiency when compared to the results of the Extended Kalman Filter (EKF) algorithm in estimating dynamic states of a power system.  Our results are presented and analyzed on the basis of how accurately the algorithm estimates the system states following various simulated transient and small-signal disturbances.

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2016. / Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2016-08-08T20:08:43Z No. of bitstreams: 1 2016_HenriqueMarraTairaMenegaz.pdf: 4698576 bytes, checksum: 0e95622bdc8a0e9ab76ddd058427d256 (MD5) / Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-10-24T13:56:05Z (GMT) No. of bitstreams: 1 2016_HenriqueMarraTairaMenegaz.pdf: 4698576 bytes, checksum: 0e95622bdc8a0e9ab76ddd058427d256 (MD5) / Made available in DSpace on 2016-10-24T13:56:05Z (GMT). No. of bitstreams: 1 2016_HenriqueMarraTairaMenegaz.pdf: 4698576 bytes, checksum: 0e95622bdc8a0e9ab76ddd058427d256 (MD5) / Nesta tese, nós estudamos com profundidade uma técnica cada vez mais popular conhecida como Filtro de Kalman Unscented (FKU). Consideremos tanto aspectos teóricos como práticos da filtragem Unscented. Na primeira parte deste trabalho, propomos uma sistematização da teoria de filtragem de Kalman Unscented. Nessa sistematização nós i) agrupamos todos os FKUs da literatura, ii) apresentamos correções para inconsistências teóricas detectadas, e iii) propomos uma ferramenta para a construção de novos FKU's de forma consistente. Essencialmente, essa sistematização é feita mediante a revisão dos conceitos de conjunto sigma (SS), Transformação Unscented (TU), Transformação Unscented Escalada (TUE), Transformação Unscented Raiz-Quadrada (TURQ), FKU, e Filtro de Kalman Unscented Raiz-Quadrada (FKURQ). Introduzimos FKUs tempo-contínuo e tempo-contínuo-discreto. Ilustramos os resultados em i) alguns exemplos analíticos e numéricos, e ii) um experimento prático que consiste em estimar a posição de uma válvula de aceleração eletrônica utilizando FKUs desenvolvidos neste trabalho; essa estimação da posição de válvula é também uma contribuição por si só desde um ponto de vista tecnológico. Na segunda parte, primeiro, nós i) revelamos inconsistência na teoria por trás dos FKUs e FKURQs para sistemas de quatérnios unitários da literatura — tais como definições de quatérnios aleatórios e de sistemas quaterniônicos com ruídos aditivos —, ii) propomos um FKU englobando todos esses FKU's, e iii) propomos um FKURQ com propriedades numéricas superiores a esses FKURQs. Segundo, propomos uma extensão de alguns resultados da literatura relativos a estatísticas em variedades Riemannianas. Terceiro, usamos esses resultados estatísticos para apresentar uma extensão para sistemas riemannianos da sistematização euclidiana desenvolvida na primeira parte. Nessa sistematização riemanniana, introduzimos i) sistemas riemannianos com ruídos aditivos; e versões riemannianas dos conceitos de SS, TU, TUE, TURQ, FKU, e FKURQ. Diversos novos FKUs são introduzidos. Depois, apresentamos formas fechadas para quase todas as operações contidas nos filtros riemannianos para sistemas de quatérnios unitários. Também introduzimos consistentes i) FKUs para sistemas de quatérnios unitários duais, e ii) FKUs tempo-contínuo e tempo-contínuo-discreto. __________________________________________________________________________________________________ ABSTRACT / In this thesis, we take an in-depth study of an increasingly popular estimation technique known as Unscented Kalman Filter (UKF). We consider theoretical and practical aspects of the unscented filtering. In the first part of this work, we propose a systematization of the Unscented Kalman filtering theory on Euclidean spaces. In this systematization, we i) gather all available UKF's in the literature, ii) present corrections to theoretical inconsistencies, and iii) provide a tool for the construction of new UKF's in a consistent way. Mainly, this systematization is done by revisiting the concepts of sigma set (SS), Unscented Transformation (UT), Scaled Unscented Transformation (SUT), Square-Root Unscented Transformation (SRUT), UKF, and Square-Root Unscented Kalman Filter (SRUKF). We introduce continuous-time and continuous-discrete-time UKF's. We illustrate the results in i) some analytical and numerical examples, and ii) a practical experiment consisting of estimating the position of an automotive electronic throttle valve using UKF's developed in this work; this valve's position estimation is also, from a technological perspective, a contribution on its own. In the second part, first, we i) unfold some consistence issues in the theory behind the UKF's and SRUKF's for unit quaternion systems of the literature—such as definitions of random quaternions and additive-noise quaternion systems—, ii) propose an UKF embodying all these UKF's, and iii) propose an SRUKF with better computational properties than all these SRUKF's. Second, we propose an extension of some results of the literature concerning statistics on Riemannian manifolds. Third, we use these statistical results to present an extension to Riemannian systems of the Euclidean systematization developed in the first part. In this Riemannian systematization, we propose i) additive-noise Riemannian systems; and ii) Riemannian versions of the concepts of SS, UT, SUT, SRUT, UKF, and SRUKF. Several new consistent UKF's are introduced. Afterwards, we present closed forms of almost all the operations contained in the Unscented-type Riemannian filters for unit quaternion systems. We also introduce consistent i) UKF's for systems of unit dual quaternions, and ii) continuous-time and continuous-discrete-time UKF's for Riemannian manifolds.

O filtro de Kalman estendido tem sido a mais popular ferramenta de filtragem não linear das últimas quatro décadas. É de fácil implementação e apresenta baixo custo computacional. Nos casos nos quais as não linearidades do sistema dinâmico são significativas, porém, o filtro de Kalman estendido pode apresentar resultados insatisfatórios. Nessas situações, o filtro de Kalman unscented substitui com vantagens o filtro de Kalman estendido, pois pode apresentar melhores estimativas de estado, embora ambos os filtros exibam complexidade computacional de mesma ordem. A qualidade das estimativas de estado do filtro unscented está intimamente ligada à sintonia dos parâmetros que controlam a transformada unscented. A versão escalada dessa transformada exibe três parâmetros escalares que determinam o posicionamento dos pontos sigma e, consequentemente, afetam diretamente a qualidade das estimativas produzidas pelo filtro. Apesar da importância do filtro de Kalman unscented, a sintonia ótima desses parâmetros é um problema para o qual ainda não há solução definitiva. Não há nem mesmo recomendações heurísticas que garantam o bom funcionamento do filtro unscented na maior parte dos problemas tratáveis por meio de filtros Gaussianos. Essa carência e a importância desse filtro para a área de filtragem não linear fazem da busca por mecanismos de sintonia automática do filtro unscented área de pesquisa ativa. Assim, este trabalho propõe técnicas para sintonia automática dos parâmetros da transformada unscented escalada. Além da sintonia desses parâmetros, também é abordado o problema de sintonizar as matrizes de covariância dos ruídos de processo e de medida demandadas pelo modelo do sistema dinâmico usado pelo filtro unscented. As técnicas propostas cobrem então a sintonia automática de todos os parâmetros do filtro. / The extended Kalman filter has been the most popular nonlinear filter of the last four decades. It is easy to implement and exhibits low computational cost. When nonlinearities are significant, though, the extended Kalman filter can display poor state estimation performance. In such situations, the unscented Kalman filter can yield better state estimates, while displaying the same order of computational complexity as the extended Kalman filter. The quality of the state estimates produced by the unscented Kalman filter is directly influenced by the tuning of the scalar parameters that govern the unscented transform. The scaled version of the unscented transform features three scalar parameters that determine the positioning of the sigma points, thus directly affecting the filter state estimation performance. Despite the importance of the unscented Kalman filter, the optimal tuning of the scaled unscented transform parameters is still an open problem. This work hence discusses algorithms for the automatic tuning of the unscented transform parameters. The discussion includes the tuning of the needed noise covariance matrices, thus covering the automatic tuning of all parameters of the unscented Kalman filter.

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

A detailed analysis and design of a navigation algorithm for a spacecraft to achieve precision lunar descent and landing is presented. The Inertial Navigation System (INS) was employed as the primary navigation system. To increase the accuracy and precision of the navigation system, the INS was integrated with aiding sensors - a star camera, an altimeter and a terrain camera. An unscented Kalman filter was developed to integrate the aiding sensor measurements with the INS measurements, and to estimate the current position, velocity and attitude of the spacecraft. The errors associated with the accelerometer and gyro measurements are also estimated as part of the navigation filter. An STK scenario was utilized to simulate the truth data for the navigation system. The navigation filter developed was tested and simulated, and from the results obtained, the position, velocity and attitude of the spacecraft were observed to be well estimated.

The focus of this research is on the development of vibration response-based damage detection in civil engineering structures. Modal parameter-based and model identification-based approaches have been considered. In the modal parameter-based approach, the flexibility and curvature flexibility matrices of the structure are used to identify the damage. It is shown that changes in these matrices can be related to changes in stiffness values of individual structural members. Using this relationship, a method is proposed to solve for the change in stiffness values. The application of this approach is demonstrated on the benchmark problem developed by the joint International Association of Structural Control and American Society of Civil Engineers Structural Health Monitoring task group. The proposed approach is found to be effective in identifying various damage scenarios of this benchmark problem. The effect of missing modes on the damage identification scheme is also studied. The second method for damage identification aims at identifying sudden changes in stiffness for real time applications. It is shown that the high-frequency content of the response acceleration can be used to identify the instant at which a structure suffers a sudden reduction in its stiffness value. Using the Gibb's phenomenon, it is shown why a high-pass filter can be used for identifying such damages. The application of high-pass filters is then shown in identifying sudden stiffness changes in a linear multi-degree-of-freedom system and a bilinear single degree of freedom system. The impact of measurement noise on the identification approach is also studied. The noise characteristics under which damage identification can or cannot be made are clearly identified. The issue of quantification of the stiffness reduction by this approach is also examined. It is noted that even if the time at which the reduction in stiffness happens can be identified, the quantification of damage requires the knowledge of system displacement values. In principle, such displacements can be calculated by numerical integration of the acceleration response, but the numerical integrations are known to suffer from the low frequency drift error problems. To avoid the errors introduced due to numerical integration of the acceleration response, an approach utilizing the unscented Kalman filter is developed to track the sudden changes in stiffness values. This approach is referred to as the adaptive unscented Kalman filter (AUKF) approach. The successful application of the proposed AUKF approach is shown on two multi-degree of freedom systems that experience sudden loss of stiffness values while subjected to earthquake induced base excitation. / Ph. D.

No description available.

Knowing the uncertainty of a calculated result is always important, but especially so when performing calculations for safety analysis. A traditional way of propagating the uncertainty of input parameters is Monte Carlo (MC) methods. A quicker alternative to MC, especially useful when computations are heavy, is Deterministic Sampling (DS). DS works by hand-picking a small set of samples, rather than randomizing a large set as in MC methods. The samples and its corresponding weights are chosen to represent the uncertainty one wants to propagate by encoding the first few statistical moments of the parameters' distributions. Finding a suitable ensemble for DS in not easy, however. Given a large enough set of samples, one can always calculate weights to encode the first couple of moments, but there is good reason to want an ensemble with only positive weights. How to choose the ensemble for DS so that all weights are positive is the problem investigated in this project. Several methods for generating such ensembles have been derived, and an algorithm for calculating weights while forcing them to be positive has been found. The methods and generated ensembles have been tested for use in uncertainty propagation in many different cases and the ensemble sizes have been compared. In general, encoding two or four moments in an ensemble seems to be enough to get a good result for the propagated mean value and standard deviation. Regarding size, the most favorable case is when the parameters are independent and have symmetrical distributions. In short, DS can work as a quicker alternative to MC methods in uncertainty propagation as well as in other applications.

Nesta tese de doutorado é proposta uma abordagem para estimar a velocidade angular de geradores síncronos conectados em um sistema elétrico de potência, a partir de sinais que podem ser facilmente mensurados, tais como a corrente e a tensão na barra do lado de alta tensão do transformador que conecta o gerador em análise ao restante do sistema. Uma vez que informações precisas sobre o comportamento dinâmico do sistema são de elevada importância para um controle efetivo do SEP, especialmente com o aumento da complexidade da rede, a abordagem proposta nesta tese fornece uma estimativa do sinal de velocidade que pode ser aplicada no estudo da estabilidade a pequenas perturbações para mitigar os problemas inerentes a presença das oscilações eletromecânicas mal amortecidas nos SEPs. A abordagem desenvolvida é composta por dois métodos sendo cada um deles aplicável dependendo das características do problema a ser resolvido e das informações disponíveis para tanto. No primeiro método, uma técnica de sensibilidade da trajetória é aplicada ao sinal de diferença entre a resposta obtida pelo modelo simulado e aquela fornecida por dados amostrados no sistema real emulado. A partir desse sinal de erro e das curvas de sensibilidade, a técnica possibilita calibrar os coeficientes de um modelo linear do SEP e, consequentemente, descrever de forma precisa a resposta da velocidade do gerador em análise. No segundo método, uma técnica de filtragem é utilizada (filtro de Kalman Unscented) a qual fornece uma estimativa adequada para a velocidade angular do rotor mesmo quando elevadas discrepâncias são observadas entre a saída do modelo simulado e a resposta amostrada no sistema real. Os resultados obtidos sobre diferentes sistemas testes evidenciam a eficiência da abordagem proposta. / In this thesis, an approach is proposed to estimate the rotor speed of synchronous generators connected to an electric power system (EPS), from signals that can be easily sampled by measuring equipment, such as current and voltage in high voltage side of the step-up transformer of the power plant. Accurate information about the dynamic behavior of system is essential for effective control and reliable operation of EPS, especially with the increasing complexity of the grid. Hence, the main aim of this work is to provide an estimate of the rotor speed signal that can be applied in the area of small-signal stability, in order to mitigate the detrimental effects of poorly damped electromechanical oscillations in EPSs. The developed approach is composed of two methods, where each of them is applicable depending on the characteristics of the problem to be solved and the available information. In the first method, a trajectory sensitivity technique is applied on the mismatch between the simulated output in the system linear model and the response of the real system. Using this error signal and the sensitivity curves, this method allows to identify and to calibrate some coefficients of the linear model and, consequently, to adequately describe the speed response of the generator under analysis. In this second method, a filtering technique is used, the Unscented Kalman Filter, which provides an adequate estimate for rotor speed even when high discrepancies are observed between the linear model and the sampled response of real system. The results obtained on test systems with different characteristics show the efficiency of the proposed approach.