This thesis aims to identify methods for detection of damper winding faults in synchronous machines (SMs) powered by variable frequency drives (VFDs). The problem of failing damper windings has received attention after reports of serious damage which have been discovered during maintenance checks. Since SMs often are used for critical applications, failures can be devastating if they cause total breakdowns. Also, VFDs are believed to cause additional stress in the damper windings of SMs and since the utilisation of VFDs is increasing, the problem is expected to become more common in the future. Currently, there is no method for detection of failures during normal operation of SMs, thus research in the area is required. Simulations based on the finite element method as well as laboratory experiments have been performed in order to examine the impact of VFDs and to find a way of detecting damper winding failures. The results confirm that utilization of VFDs produce higher currents in the damper winding compared to direct-online operation. The results also show that in case of a damper bar failure, the current distribution among the damper winding segments is affected. However, monitoring of all damper winding segments is unrealistic due to the number of sensors required. Another approach, which has been investigated, involves monitoring of the current through the pole interconnectors of one of the endrings. Potential fault indicators have been found by analysing the currents in the frequency domain. However, further studies are required in order to evaluate the method. Also the temperature of the damper winding was investigated as an indicator.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity

Hybrid systems have been identified as one of the main directions in control theory and attracted increasing attention in recent years due to their huge diversity of engineering applications. Multiplemodel (MM) estimation is the state-of-the-art approach to many hybrid estimation problems. Existing MM methods with fixed structure usually perform well for problems that can be handled by a small set of models. However, their performance is limited when the required number of models to achieve a satisfactory accuracy is large due to time evolution of the true mode over a large continuous space. In this research, variable-structure multiple model (VSMM) estimation was investigated, further developed and evaluated. A fundamental solution for on-line adaptation of model sets was developed as well as several VSMM algorithms. These algorithms have been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by sensors or actuators. Fault detection and target maneuver detection can be formulated as change-point detection problems in statistics. It is of great importance to have the quickest detection of such mode changes in a hybrid system. Traditional maneuver detectors based on simplistic models are not optimal and are computationally demanding due to the requirement of batch processing. In this presentation, a general sequential testing procedure is proposed for maneuver detection based on advanced sequential tests. It uses a likelihood marginalization technique to cope with the difficulty that the target accelerations are unknown. The approach essentially utilizes a priori information about the accelerations in typical tracking engagements and thus allows improved detection performance. The proposed approach is applicable to change-point detection problems under similar formulation, such as fault detection.

L'objectif de cette thèse est le développement d'une méthodologie pour la détection de défauts appliquée aux équipements de production de semi-conducteurs. L'approche proposée repose sur l'Analyse en Composantes Principales (ACP) pour construire un modèle représentatif du fonctionnement nominal d'un équipement. Pour cela, notre méthodologie consiste à exploiter l'ensemble des mesures disponibles, collectées via les capteurs internes et externes au cours desopérations de fabrication pour chaque plaque manufacturée. Nous avons développé un module de pré-traitement permettant de transformer les mesures collectées en données interprétables par l'ACP, tout en filtrant l'information considérée comme non-désirable induite par la présence de valeurs aberrantes et perturbant la construction du modèle. Nous avons combiné des extensions de l'ACP linéaire et notamment l'ACP multiway, l'ACP filtrée ainsi que l'ACP récursive, de façon à adapter la modélisation aux caractéristiques des systèmes. L'utilisation d'un filtre par moyenne mobile exponentielle nous permet de considéré la dynamique du système au cours de la réalisation d'une opération. L'ACP récursive est employée pour adapter le modèle aux changements de comportement du système après certains événements (maintenance, redémarrage, etc.).Les différentes méthodes sont illustrées à l'aide de données réelles, collectées sur un équipement actuellement exploité par STMicroelectronics Rousset. Nous proposons également une application plus générale de la méthode pour différents types d'équipement et sur une période plus importante, de façon à montrer l'intérêt industriel et la performance de cette approche. / This thesis focus on developping a fault detection methodology for semiconductor manufacturing equipment. The proposed approach is based on Principal Components Analysis (PCA) to build a representative model of equipment in adequat operating conditions. Our method exploits collected measurements from equipement sensors, for each processed wafer. However, regarding the industrial context and processes, we have to consider additional problems: collected signals from sensors exhibit different length, or durations. This is a limitation considering PCA. We have also to consider synchronization and alignment problems; semiconductor manufacturing equipment are almost dynamic, with strong temporal correlations between sensor measurements all along processes. To solve the first point, we developped a data preprocessing module to transform raw data from sensors into a convenient dataset for PCA application. The interest is to identify outliers data and products, that can affect PCA modelling. This step is based on expert knowledge, statistical analysis, and Dynamic Time Warping, a well-known algorithm from signal processing. To solve the second point, we propose a combination multiway PCA with the use of an EWMA filter to consider process dynamic. A recursive approach is employed to adapt our PCA model to specific events that can occur on equipment, e.g. maintenance, restart, etc.All the steps of our methodology are illustrated with data from a chemical vapor deposition tool exploited in STMicroelectroics Rousset fab. Finally, the efficiency and industrial interest of the proposed methodologies are verified by considering multiple equipment types on longer operating periods.

Orientador: Fábio Roberto Chavarette / Resumo: Redes Neurais Artificiais (RNAs) são algoritmos de aprendizado, geralmente estruturados em torno de categorização de dados de entrada e/ou seu agrupamento por similaridade. Tendo em vista características desejáveis como aprendizado rápido e estabilidade frente a vetores de entrada altamente mutáveis, adotou-se uma RNA do tipo Fuzzy ARTMAP como mecanismo central de um método de monitoramento de saúde estrutural para detectar e categorizar falhas em dados experimentais provenientes de um sistema mecânico similar a um pequeno prédio de dois andares. Mais especificamente, com o objetivo de detectar alterações das frequências naturais da estrutura, fenômeno ligado à deterioração da mesma, e determinar qual(is) andar(es) está(ão) ligado(s) ao comportamento anômalo, se detectado. A acurácia da rede foi avaliada, sendo realizado um estudo da quantidade de dados necessárias para o desempenho satisfatório da rede. Observou-se desempenho satisfatório, a acurácia do método tendendo a aproximadamente 94% a partir de certas quantidades de dados. / Abstract: Artificial Neural Networks (ANNs) are learning algorithms, largely revolving around categorizing data sets based on measures of similarity between its members. Due to desirable characteristics such as fast learning and stability when dealing with highly mutable input vectors, a Fuzzy ARTMAP ANN was selected as the core mechanism of a structural health monitoring method. Its goal was to detect and categorize faults in experimental data collected from a mechanical system akin to a small two-story building. More specifically, to detect disturbances on the structure's natural frequencies, phenomenon linked to its deterioration, and to determine which story or stories are linked to anomalous behavior, if any. The accuracy of the method was evaluated, and the amount of data needed for optimal operation was determined. Satisfactory performance was observed; the method's accuracy tended towards 94% with enough training samples. / Mestre

[pt] Contratos de software podem ser escritos como expressões lógicas capazes de identificar falhas que ocorrem durante a utilização de um software. É possível implementar a verificação de um contrato em um software através de assertivas executáveis. No entanto, a forma como assertivas convencionais são implementadas não é diretamente aplicável a sistemas distribuídos, uma vez que apresentam dificuldades para avaliar expressões temporais, tampouco as expressões podem envolver propriedades de diferentes processos. Este trabalho propõe um mecanismo baseado em logs com meta-informações para a verificação de contratos em sistemas distribuídos. Uma gramática para redigir contratos possibilita operações temporais, ou seja, permite a especificação de condições entre eventos, em diferentes instantes de tempo, ou mesmo garante uma sequência de eventos, durante um período de tempo. O fluxo de eventos gerado é avaliado assincronamente em relação à utilização do sistema, pela comparação com contratos, previamente escritos de acordo com a gramática, que representam as expectativas sobre o comportamento normal do sistema. / [en] Software contracts can be written as assertions that identify failures observed while using the software. Software contracts can be implemented through executable assertions. However, conventional assertions are not directly applicable in distributed systems, as they present difficulties to evaluate temporal expressions, as well as expressions involving properties of different processes. This work proposes a mechanism based on logs with meta-information to evaluate contracts in distributed systems. A grammar to write contracts enable temporal operations, e.g., allows specifying conditions between events at different timestamps, or even guaranteeing a sequence of events over a period of time. The flow of events is evaluated asynchronously in relation to the system execution, by comparison with contracts, previously written according to the grammar, representing the expectations on the behavior of the system.

A presente tese apresenta proposições para o uso da seleção de variáveis no aprimoramento do controle estatístico de processos multivariados (MSPC) em bateladas, a fim de contribuir com a melhoria da qualidade de processos industriais. Dessa forma, os objetivos desta tese são: (i) identificar as limitações encontradas pelos métodos MSPC no monitoramento de processos industriais; (ii) entender como métodos de seleção de variáveis são integrados para promover a melhoria do monitoramento de processos de elevada dimensionalidade; (iii) discutir sobre métodos para alinhamento e sincronização de bateladas aplicados a processos com diferentes durações; (iv) definir o método de alinhamento e sincronização mais adequado para o tratamento de dados de bateladas, visando aprimorar a construção do modelo de monitoramento na Fase I do controle estatístico de processo; (v) propor a seleção de variáveis, com propósito de classificação, prévia à construção das cartas de controle multivariadas (CCM) baseadas na análise de componentes principais (PCA) para monitorar um processo em bateladas; e (vi) validar o desempenho de detecção de falhas da carta de controle multivariada proposta em comparação às cartas tradicionais e baseadas em PCA. O desempenho do método proposto foi avaliado mediante aplicação em um estudo de caso com dados reais de um processo industrial alimentício. Os resultados obtidos demonstraram que a realização de uma seleção de variáveis prévia à construção das CCM contribuiu para reduzir eficientemente o número de variáveis a serem analisadas e superar as limitações encontradas na detecção de falhas quando bancos de elevada dimensionalidade são monitorados. Conclui-se que, ao possibilitar que CCM, amplamente utilizadas no meio industrial, sejam adequadas para banco de dados reais de elevada dimensionalidade, o método proposto agrega inovação à área de monitoramento de processos em bateladas e contribui para a geração de produtos de elevado padrão de qualidade. / This dissertation presents propositions for the use of variable selection in the improvement of multivariate statistical process control (MSPC) of batch processes, in order to contribute to the enhacement of industrial processes’ quality. There are six objectives: (i) identify MSPC limitations in industrial processes monitoring; (ii) understand how methods of variable selection are used to improve high dimensional processes monitoring; (iii) discuss about methods for alignment and synchronization of batches with different durations; (iv) define the most adequate alignment and synchronization method for batch data treatment, aiming to improve Phase I of process monitoring; (v) propose variable selection for classification prior to establishing multivariate control charts (MCC) based on principal component analysis (PCA) to monitor a batch process; and (vi) validate fault detection performance of the proposed MCC in comparison with traditional PCA-based and charts. The performance of the proposed method was evaluated in a case study using real data from an industrial food process. Results showed that performing variable selection prior to establishing MCC contributed to efficiently reduce the number of variables and overcome limitations found in fault detection when high dimensional datasets are monitored. We conclude that by improving control charts widely used in industry to accomodate high dimensional datasets the proposed method adds innovation to the area of batch process monitoring and contributes to the generation of high quality standard products.

A diminuição no custo de computadores pessoais tem favorecido a construção de sistemas computacionais complexos, tais como aglomerados e grades. Devido ao grande número de recursos existentes nesses sistemas, a probabilidade de que faltas ocorram é alta. Uma abordagem que auxilia a tornar sistemas mais robustos na presença de faltas é a detecção de sua ocorrência, a fim de que processos possam ser reiniciados em estados seguros, ou paralisados em estados que não ofereçam riscos. Abordagens comumente adotadas para detecção seguem, basicamente, três tipos de estratégias: as baseadas em mensagens de controle, em estatística e em aprendizado de máquina. No entanto, elas tipicamente não consideram o comportamento de processos ao longo do tempo. Observando essa limitação nas pesquisas relacionadas, este trabalho apresenta uma abordagem para medir a variação no comportamento de processos ao longo do tempo, a fim de que mudanças inesperadas sejam detectadas. Essas mudanças são consideradas, no contexto deste trabalho, como faltas, as quais representam transições indesejadas entre estados de um processo e podem levá-lo a processamento incorreto, fora de sua especificação. A proposta baseia-se na estimação de cadeias de Markov que representam estados visitados por um processo durante sua execução. Variações nessas cadeias são utilizadas para identificar faltas. A abordagem proposta é comparada à técnica de aprendizado de máquina Support Vector Machines, bem como à técnica estatística Auto-Regressive Integrated Moving Average. Essas técnicas foram escolhidas para comparação por estarem entre as mais empregadas na literatura. Experimentos realizados mostraram que a abordagem proposta possui, com erro \'alfa\' = 1%, um F-Measure maior do que duas vezes o alcançado pelas outras técnicas. Realizou-se também um estudo adicional de predição de faltas. Nesse sentido, foi proposta uma técnica preditiva baseada na reconstrução do comportamento observado do sistema. A avaliação da técnica mostrou que ela pode aumentar em até uma ordem de magnitude a disponibilidade (em horas) de um sistema / The cost reduction for personal computers has enabled the construction of complex computational systems, such as clusters and grids. Because of the large number of resources available on those systems, the probability that faults may occur is high. An approach that helps to make systems more robust in the presence of faults is their detection, in order to restart or stop processes in safe states. Commonly adopted approaches for detection basically follow one of three strategies: the one based on control messages, on statistics or on machine learning. However, they typically do not consider the behavior of processes over time. Observing this limitation in related researches, this work presents an approach to measure the level of variation in the behavior of processes over time, so that unexpected changes are detected. These changes are considered, in the context of this work, as faults, which represent undesired transitions between process states and may cause incorrect processing, outside the specification. The approach is based on the estimation of Markov Chains that represent states visited by a process during its execution. Variations in these chains are used to identify faults. The approach is compared to the machine learning technique Support Vector Machines, as well as to the statistical technique Auto-Regressive Integrated Moving Average. These techniques have been selected for comparison because they are among the ones most employed in the literature. Experiments conducted have shown that the proposed approach has, with error \'alpha\'= 1%, an F-Measure higher than twice the one achieved by the other techniques. A complementary study has also been conducted about fault prediction. In this sense, a predictive approach based on the reconstruction of system behavior was proposed. The evaluation of the technique showed that it can provide up to an order of magnitude greater availability of a system in terms of uptime hours

Identity based cryptography was first proposed by Shamir in 1984. Rather than deriving a public key from private information, which would be the case in traditional public key encryption schemes, in identity based schemes a user's identity plays the role of the public key. This reduces the amount of computations required for authentication, and simplifies key-management. Efficient and strong implementations of identity based schemes are based around easily computable bilinear mappings of two points on an elliptic curve onto a multiplicative subgroup of a field, also called pairing. The idea of utilizing the identity of the user simplifies the public key infrastructure. However, since pairing computations are expensive for both area and timing, the proposed identity based cryptosystem are hard to implement. In order to be able to efficiently utilize the idea of identity based cryptography, there is a strong need for an efficient pairing implementations. Pairing computations could be realized in multiple fields. Since the main building block and the bottleneck of the algorithm is multiplication, we focused our research on building a fast and small arithmetic core that can work on multiple fields. This would allow a single piece of hardware to realize a wide spectrum of cryptographic algorithms, including pairings, with minimal amount of software coding. We present a novel unified core design which is extended to realize Montgomery multiplication in the fields GF(2^n), GF(3^m), and GF(p). Our unified design supports RSA and elliptic curve schemes, as well as identity based encryption which requires a pairing computation on an elliptic curve. The architecture is pipelined and is highly scalable. The unified core utilizes the redundant signed digit representation to reduce the critical path delay. While the carry-save representation used in classical unified architectures is only good for addition and multiplication operations, the redundant signed digit representation also facilitates efficient computation of comparison and subtraction operations besides addition and multiplication. Thus, there is no need for transformation between the redundant and non-redundant representations of field elements, which would be required in classical unified architectures to realize the subtraction and comparison operations. We also quantify the benefits of unified architectures in terms of area and critical path delay. We provide detailed implementation results. The metric shows that the new unified architecture provides an improvement over a hypothetical non-unified architecture of at least 24.88 % while the improvement over a classical unified architecture is at least 32.07 %. Until recently there has been no work covering the security of pairing based cryptographic hardware in the presence of side-channel attacks, despite their apparent suitability for identity-aware personal security devices, such as smart cards. We present a novel non-linear error coding framework which incorporates strong adversarial fault detection capabilities into identity based encryption schemes built using Tate pairing computations. The presented algorithms provide quantifiable resilience in a well defined strong attacker model. Given the emergence of fault attacks as a serious threat to pairing based cryptography, the proposed technique solves a key problem when incorporated into software and hardware implementations. In this dissertation, we also present an efficient accelerator for computing the Tate Pairing in characteristic 3, based on the Modified Duursma Lee algorithm.

Because of the increasingly demanding tasks that robotic systems are asked to perform, there is a need to make them more reliable, intelligent, versatile and self-sufficient. Furthermore, throughout the robotic system?s operation, changes in its internal and external environments arise, which can distort trajectory tracking, slow down its performance, decrease its capabilities, and even bring it to a total halt. Changes in robotic systems are inevitable. They have diverse characteristics, magnitudes and origins, from the all-familiar viscous friction to Coulomb/Sticktion friction, and from structural vibrations to air/underwater environmental change. This thesis presents an on-line environmental Change, Detection, Isolation and Accommodation (CDIA) scheme that provides a robotic system the capabilities to achieve demanding requirements and manage the ever-emerging changes. The CDIA scheme is structured around a priori known dynamic models of the robotic system and the changes (faults). In this approach, the system monitors its internal and external environments, detects any changes, identifies and learns them, and makes necessary corrections into its behavior in order to minimize or counteract their effects. A comprehensive study is presented that deals with every stage, aspect, and variation of the CDIA process. One of the novelties of the proposed approach is that the profile of the change may be either time or state-dependent. The contribution of the CDIA scheme is twofold as it provides robustness with respect to unmodeled dynamics and with respect to torque-dependent, state-dependent, structural and external environment changes. The effectiveness of the proposed approach is verified by the development of the CDIA scheme for a SCARA robot. Results of this extensive numerical study are included to verify the applicability of the proposed scheme.