Parmi les méthodes de diagnostic de défauts issue de l'automatique, la méthode des filtres résiduels permet de synthétiser des filtres générant des signaux (dits résidus) qui sont utilisés à des ns de détection de défauts. Dans notre approche, les filtres résiduels sont obtenus à partir d'observateurs réduits. L'objectif decette thèse est de synthétiser des observateurs et de mettre en évidence leur application à la détection de défauts pour les systèmes non linéaires. Ce mémoire est réparti en deux parties. Dans la première partie, deux contributions ont été présentées. La première concerne les observateurs à entrées inconnues pour les systèmes affines en l'état modulo une injection de sortie. L'approche proposée est une combinaison des techniques de découplage géométrique et des observateurs non linéaires. Des conditions suffisantes ont été données accompagnées d'un algorithme permettant de concevoir un observateur à entrées inconnues permettant d'estimer une partie de l'état indépendamment de la connaissance de certaines entrées. La deuxième contribution consiste à caractériser la classe des systèmes non linéaires qui se transforment en des systèmes en cascade pour lesquels un observateur peut être conçu. Tout d'abord, des conditions nécessaires et suffisantes théoriques ont été données, ensuite un algorithme permettant de calculer ces transformations (diéomorphismes) a été proposé. Enfin, l'ensemble de tous ces difféomorphismes a été caractérisé en montrant que ce dernier est une orbite d'une action d'un groupe particulier sur l'ensemble de tous les difféomorphismes. La deuxième partie de cette thèse concerne la synthèse d'un filtre polytopique Linéaires à Paramètres Variants (LPV) permettant de détecter, isoler et estimer de multiples défauts capteur. L'avantage de ce filtre est de générer deux types de résidus : l'un étant sensible aux défauts et l'autre insensible. Le résidu insensible est utilisé pour fournir une information qualitative supplémentaire de l'efficacité du filtre. La stabilité de ce dernier est analysée au moyen d'Inégalités Matricielles Linéaires (LMI) / Among the faults diagnosis methods, the method of residual filters allows to synthesize filters generating signals ( called residues) that are used for fault detection. In our approach, the residual filters are obtained from reduced observers. The objective of this thesis is to synthesize observers and highlight their application to fault detection for nonlinear systems. This thesis is divided into two parts. In the first part, two papers were presented. The rst one relates to the unknown input observers for state ane systems modulo an output injection. The proposed approach is a combination of geometric decoupling techniques and nonlinear observers. We have given sufficient conditions with an algorithm for designing an unknown input observer to estimate a part of the state without the knowledge of some inputs. The second contribution consists to characterize the class of nonlinear systems which can be transformed into cascade systems for which an observer can be designed. First, necessary and sufficient theoretical conditions were given, then an algorithm to compute these transformations (diffeomorphisms) was proposed. Finally, the set of all dieomorphisms was characterized by showing that it is an orbit of an action of a particular group on the set of all dieomorphisms. The second step of the thesis deals with the synthesis of a polytopic Linear Parameter-Varying (LPV) filter to detect, isolate and estimate multiple sensor faults. The advantage of this lter is to generate two types of residuals : one being sensitive to faults and the other is insensitive. The insensitive residual is used to generate an additional qualitative information of the filter efficiency. The stability of the latter has been performed using Linear Matrix Inequality (LMI)

As the world moves towards a cleaner and greener future, electrical machines for various industrial purposes and transport applications have gained a lot of attention. Permanent magnet synchronous machines (PMSMs) are usually the solution for electric vehicle (EV) applications thanks to their high efficiency, compactness and high-power density. On the downside, although the price of rare-earth materials has recovered close to historical levels, concerns still remain and the questions on the environmental sustainability of these materials have also been raised, which has encouraged the researchers to consider rare-earth-free machines.The switched reluctance machine (SRM) is one of the competitive alternatives, thanks to the simple and robust construction, high reliability and inherent fault tolerance capability. However, it has a bad reputation when it comes to torque ripple and acoustic noise. And the highly nonlinear characteristic brings much difficulty to routine design purposes and machine optimisation.Therefore, some of the above mentioned problems are addressed - a torque-ripple-reduction, reliable and low-cost system of SRMs is presented in this thesis. Firstly from the modelling point of view, a combined magnetic equivalent circuit (MEC) and finite element (FE) model of SRMs is developed for fast characterization the nonlinear behavior. Secondly from the control point of view, various torque-ripple reduction techniques are implemented and compared. Moreover, a minimal current sensing strategy with enhanced fault-detection capability is proposed and validated experimentally. It requires two current sensors, to replace the phase current sensors, with no additional devices for fault detection, to achieve a more compact and low-cost drive. Finally from the reliability point of view, an interturn short-circuit fault detection method and a rotor position estimation approach are investigated and validated experimentally, which leads to a more reliable system. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Existem inúmeras ferramentas e metodologias para o diagnóstico de falhas em transformadores de potência, tanto para a monitoração e acompanhamento do equipamento em operação (planta fixa) como àquele retirado e abrigado em laboratórios de unidades fabris. Em função dos custos envolvidos no transporte dos equipamentos, manuseio para a substituição, energia não faturada nos períodos de manobras e custos adicionais dos ensaios para voltar com o equipamento em operação, as principais metodologias que se destacam são aquelas direcionadas ao equipamento em operação e também as escolhidas para serem analisadas neste trabalho. Após um estudo sistemático dessas metodologias, tais como termográfica, emissão acústica e análise dos gases dissolvidos no óleo, denominada cromatografia, verificou-se que esta última se destaca como a mais econômica e a mais difundida na identificação das falhas. Contudo, na cromatografia, existem diferentes critérios de análise baseados nas relações e quantidades de diferentes tipos de gases e que são usados pelas companhias concessionárias de forma indiscriminada, gerando muitas dúvidas quanto à sua validade. Esta pesquisa teve como principal foco a comparação desses critérios usando como base de dados àqueles fornecidos pela IEC e pela Companhia Paulista de Força e Luz (CPFL). A base de dados da CPFL contem aproximadamente quatro mil ensaios cromatográficos, colhidas nas ultimas três décadas correspondendo a 500 unidades transformadoras, nas potências de 5,0 a 50 MVA, instaladas em subestações nas tensões primárias de 69 e 138 kV, e secundária de 13,8 kV. Também fez parte dessa pesquisa a definição de um conjunto de critérios que melhor identificam as falhas em transformadores. / There are numerous tools and methodologies for fault diagnosis in power transformers, either for monitoring and tracking equipment in operation (fixed plant) or for those removed and housed in laboratories plants. Due to the costs involved in transporting the equipment, handling for the replacement, unbilled energy during periods of maneuvers and additional costs of the tests to return with the equipment in operation, the main methodologies that stand out are those using the equipment in operation and also those chosen to be analyzed in this work. After a systematic study of these methods such as thermography, and acoustic emission and analysis of gases dissolved in the oil, known as chromatography, it was found that the latter stands out as the most economical and most widely in the identification of faults. However, in chromatography, there are different criteria based on the relationships of different types and quantities of gases that are used by electrical companies indiscriminately, raising many questions about its validity. This research was mainly focused on the comparison of these criteria using the database as those provided by IEC and the Companhia Paulista de Força e Luz (CPFL). The database of CPFL contains approximately four thousand chromatographic assays from the last three decades, corresponding 500 transforming units, from 5 to 50 MVA, in substations with primary voltages of 138 kV and 69 and secondary of 13,8 kV. Was also studied the definition of a set of criteria which identify faults in transformers.

O objetivo deste trabalho é estudar a identificação, classificação, localização e setorização de faltas em redes de distribuição radiais, verificar a maneira de aplicar e integrar diversas ferramentas numéricas convencionais, assim como ferramentas de sistemas inteligentes, visando identificar a ocorrência de uma falta, classificar as fases envolvidas com a mesma, e aplicar as diversas ferramentas existentes a fim de localizar em tempo real o eventual local onde houve a ocorrência da falta, permitindo que a mesma possa ser setorizada dentro do sistema da concessionária e informada ao centro de operações, usando uma nova abordagem inteligente baseada em análise espectral de sinais. / The aim of this work is study the identification, classification, location and sectorization of a fault in distribution radial networks, check how to implement and integrate various conventional numerical tools, as well as intelligent systems based tools, to identify the occurrence of a fault, classify the phases involved with it, and apply the various tools available to locate the place where a fault was occurred in real time, enabling it to be sectorized into the utility system and informed to operational center using a new intelligent approach based on spectral signals analysis.

O objetivo deste trabalho é estudar a identificação, classificação, localização e setorização de faltas em redes de distribuição radiais, verificar a maneira de aplicar e integrar diversas ferramentas numéricas convencionais, assim como ferramentas de sistemas inteligentes, visando identificar a ocorrência de uma falta, classificar as fases envolvidas com a mesma, e aplicar as diversas ferramentas existentes a fim de localizar em tempo real o eventual local onde houve a ocorrência da falta, permitindo que a mesma possa ser setorizada dentro do sistema da concessionária e informada ao centro de operações, usando uma nova abordagem inteligente baseada em análise espectral de sinais. / The aim of this work is study the identification, classification, location and sectorization of a fault in distribution radial networks, check how to implement and integrate various conventional numerical tools, as well as intelligent systems based tools, to identify the occurrence of a fault, classify the phases involved with it, and apply the various tools available to locate the place where a fault was occurred in real time, enabling it to be sectorized into the utility system and informed to operational center using a new intelligent approach based on spectral signals analysis.

Existem inúmeras ferramentas e metodologias para o diagnóstico de falhas em transformadores de potência, tanto para a monitoração e acompanhamento do equipamento em operação (planta fixa) como àquele retirado e abrigado em laboratórios de unidades fabris. Em função dos custos envolvidos no transporte dos equipamentos, manuseio para a substituição, energia não faturada nos períodos de manobras e custos adicionais dos ensaios para voltar com o equipamento em operação, as principais metodologias que se destacam são aquelas direcionadas ao equipamento em operação e também as escolhidas para serem analisadas neste trabalho. Após um estudo sistemático dessas metodologias, tais como termográfica, emissão acústica e análise dos gases dissolvidos no óleo, denominada cromatografia, verificou-se que esta última se destaca como a mais econômica e a mais difundida na identificação das falhas. Contudo, na cromatografia, existem diferentes critérios de análise baseados nas relações e quantidades de diferentes tipos de gases e que são usados pelas companhias concessionárias de forma indiscriminada, gerando muitas dúvidas quanto à sua validade. Esta pesquisa teve como principal foco a comparação desses critérios usando como base de dados àqueles fornecidos pela IEC e pela Companhia Paulista de Força e Luz (CPFL). A base de dados da CPFL contem aproximadamente quatro mil ensaios cromatográficos, colhidas nas ultimas três décadas correspondendo a 500 unidades transformadoras, nas potências de 5,0 a 50 MVA, instaladas em subestações nas tensões primárias de 69 e 138 kV, e secundária de 13,8 kV. Também fez parte dessa pesquisa a definição de um conjunto de critérios que melhor identificam as falhas em transformadores. / There are numerous tools and methodologies for fault diagnosis in power transformers, either for monitoring and tracking equipment in operation (fixed plant) or for those removed and housed in laboratories plants. Due to the costs involved in transporting the equipment, handling for the replacement, unbilled energy during periods of maneuvers and additional costs of the tests to return with the equipment in operation, the main methodologies that stand out are those using the equipment in operation and also those chosen to be analyzed in this work. After a systematic study of these methods such as thermography, and acoustic emission and analysis of gases dissolved in the oil, known as chromatography, it was found that the latter stands out as the most economical and most widely in the identification of faults. However, in chromatography, there are different criteria based on the relationships of different types and quantities of gases that are used by electrical companies indiscriminately, raising many questions about its validity. This research was mainly focused on the comparison of these criteria using the database as those provided by IEC and the Companhia Paulista de Força e Luz (CPFL). The database of CPFL contains approximately four thousand chromatographic assays from the last three decades, corresponding 500 transforming units, from 5 to 50 MVA, in substations with primary voltages of 138 kV and 69 and secondary of 13,8 kV. Was also studied the definition of a set of criteria which identify faults in transformers.

<p>This thesis is a result of the increased requirements on availability and costs of the aircraft Jas 39 Gripen. The work has been to specify demands and to find methods suitable for development of a decision support system for the fault isolation of the aircraft. The work has also been to implement the chosen method. Two different methods are presented and a detailed comparison is performed with the demands as a starting point. The chosen method handle multiple faults in O(N2)-time where N is the number of components. The implementation shows how all demands are fulfilled and how new tests can be added during execution. Since the thesis covers the development of a prototype no practical evaluation with compare of manually isolation is done.</p>

<p>This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.</p><p>In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.</p><p>The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.</p><p>The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.</p>

This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection. In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given. The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given. The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.