Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns. Complexities in the data, including high dimensionality, fast-flowing data streams, and high nonlinearity, impose stringent challenges on fault detection applications. From the data modelling perspective, high dimensionality may cause the notorious “curse of dimensionality” and lead to deterioration in the accuracy of fault detection algorithms. Fast-flowing data streams require algorithms to give real-time or near real-time responses upon the arrival of new samples. High nonlinearity requires fault detection approaches to have sufficiently expressive power and to avoid overfitting or underfitting problems. Most existing fault detection approaches work in relatively low-dimensional spaces. Theoretical studies on high-dimensional fault detection mainly focus on detecting anomalies on subspace projections. However, these models are either arbitrary in selecting subspaces or computationally intensive. To meet the requirements of fast-flowing data streams, several strategies have been proposed to adapt existing models to an online mode to make them applicable in stream data mining. But few studies have simultaneously tackled the challenges associated with high dimensionality and data streams. Existing nonlinear fault detection approaches cannot provide satisfactory performance in terms of smoothness, effectiveness, robustness and interpretability. New approaches are needed to address this issue. This research develops an Angle-based Subspace Anomaly Detection (ABSAD) approach to fault detection in high-dimensional data. The efficacy of the approach is demonstrated in analytical studies and numerical illustrations. Based on the sliding window strategy, the approach is extended to an online mode to detect faults in high-dimensional data streams. Experiments on synthetic datasets show the online extension can adapt to the time-varying behaviour of the monitored system and, hence, is applicable to dynamic fault detection. To deal with highly nonlinear data, the research proposes an Adaptive Kernel Density-based (Adaptive-KD) anomaly detection approach. Numerical illustrations show the approach’s superiority in terms of smoothness, effectiveness and robustness.

La détection des défauts non francs est un passage obligé dans la gestion de la maintenance des câbles (Wire Health Monitoring) et permet d’anticiper l’apparition de défauts francs engendrés par l'aggravation de défauts non francs. Ces travaux de thèse visent à étudier la problématique de défauts non francs dans les câbles électriques. L'étude proposée consiste à étudier le problème direct : modélisation électromagnétique et compréhension des phénomènes physiques liés à la présence des défauts non francs, et impact des défauts sur leurs signatures obtenues par réflectométrie. Il est proposé dans ce travail de caractériser des défauts non francs représentatifs de situations réelles, ce qui peut servir ultérieurement dans la résolution du problème inverse : déterminer la localisation et la sévérité des défauts à partir du réflectogramme en vue du diagnostic. L'approche proposée se fait en deux temps. Dans un premier temps, une caractérisation électromagnétique d’une zone localisée d’un câble présentant un défaut non franc est réalisée grâce à une modélisation électromagnétique prenant en compte la géométrie tridimensionnelle du défaut. Deux méthodes numériques ont été exploitées : une méthode de différences finies dans le domaine temporel (FDTD) et une méthode de Galerkin discontinu. Les résultats des simulations permettent d'exprimer les perturbations engendrées par le défaut et de déduire leur influence sur la propagation des signaux dans le câble. Des validations expérimentales menées dans le domaine temporel et le domaine fréquentiel permettent de confronter les mesures aux prédictions numériques. Dans un deuxième temps, des modèles électriques de défauts sont exprimés sous forme de paramètres localisés R, L, C, et G qui peuvent être utilisés dans un modèle de ligne de transmission. Une telle analyse des défauts non francs permet de relier une faible variation locale des caractéristiques physiques et électriques de la ligne à une variation des signaux de réflectométrie et des paramètres électriques de la ligne. L’approche permet de fournir des informations utiles pour caractériser des défauts et peut ainsi contribuer à améliorer les performances des systèmes de détection / The soft fault detection feature is certainly a very important aspect of wire health monitoring and an important process required in electrical wiring system operation. It has a great influence on the security and quality of supply. In transmission line networks, this feature is needed to provide a timely identification of the faulted line thus anticipating the appearance of severe faults that are initially caused by soft fault degradation. This work focuses on soft fault problems in electrical fault diagnosis and their weak impact on coaxial transmission lines. The objective of this work is to carry out a soft fault forward model: electromagnetic modeling and investigating the behavior of the line after soft damages and then to analyze its effects on their signatures obtained by reflectometry. It is proposed in this work to characterize the representative soft damages in real situations, which could be used later in solving the inverse problem: determining the position and severity of defects from the reflectometry response for the diagnosis. The proposed approach is based on the following steps: first, an electromagnetic characterization of a faulty region of a cable is carried out by electromagnetic modeling, by taking into account the three-dimensional geometry of defect. For this purpose, two numerical methods have been used: Finite Difference Time Domain (FDTD) and a Discontinuous Galerkin. The simulation results allow to study the disruption initiated by the fault and to infer their influence on the signal propagation along the cable. The experimental validation provided in frequency- and time-domain allows to confront experimental measurements with simulation predictions. In a second step, electrical fault models are expressed in terms of lumped parameters R, L, C, and G, which can be used in a transmission line model. Such analysis of soft faults allows to relate low local variation of the physical and electrical characteristics of the line to a reflectometry signals variation and electrical parameters changes. The approach can provide useful information to characterize defects and can thus contribute to improve the performance of detection systems

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Les travaux de recherche de cette thèse s'inscrivent dans le cadre général de l'expertise des projets de stockage de déchets radioactifs en couche argileuse. La présence de failles dans ces roches peut modifier ses propriétés de perméabilité ; la détection des failles constitue donc un enjeu majeur. Depuis de nombreuses années, l'IRSN mène des travaux de recherche dans la station expérimentale de Tournemire où des failles à faible décalage vertical sont interceptées dans le milieu argileux depuis des ouvrages souterrains. Les travaux précédents ont montré la difficulté de détecter ces failles depuis des acquisitions de surface que cela soit en sismique réflexion, réfraction ou en électrique.Dans le cadre de cette thèse, nous avons proposé une nouvelle géométrie d'acquisition sismique en transmission (sources en surface-récepteurs dans les ouvrages souterrains). Pour traiter ces données, un code de tomographie a été développé afin de maîtriser parfaitement les paramètres d'inversion et aussi d'introduire de l'information a priori. De nombreux tests synthétiques ont ensuite été menés dans le souci d'analyser de manière fiable les résultats obtenus, notamment en termes de résolution et de pertinence de l'image. L'application de ce code de tomographie aux données en transmission nouvellement acquises permet de mettre en évidence pour la première fois une discontinuité des vitesses sismiques dans les calcaires et argilites de la Station Expérimentale de Tournemire. Cette anomalie de vitesse est localisée à l'aplomb de la zone de faille visible depuis les ouvrages souterrains et est aussi en accord avec les observations de surface. / Deep argillaceous formations have physical properties adapted to the radioactive waste disposal but their permeability properties can be modified by the presence of fractured zones; detection of these faulted zones are thus of primary importance. Several experiments have been led by IRSN in the Experimental Platform of Tournemire where faults with small vertical offsets in the deep argillaceous formation have been identified from underground installations. Some previous studies have shown the difficulty to detect this fractured zone from surface acquisitions using reflection or refraction seismic but also with electrical methods.We here propose a new seismic transmission acquisition geometry in where seismic sources are deployed at the surface and receivers are installed in the underground installations. In the scope to process these data, a new tomography algorithm has been developed in order to control the inversion parameters and also to introduce a priori information. Several synthetic tests have been led to reliably analyze the results in terms of resolution and relevance of the final image. A discontinuity of the seismic velocities in the limestones and argillites of the Tournemire Platform is evidenced for the first time by applying the algorithm to the data recently acquired. This low velocity anomaly is located just above the fracture zone visible from the underground installations and its location is also consistent with observations from the surface.

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

<p>This research presents thermal image analysis for Fault Detection and Diagnosis (FDD) of Photovoltaic (PV) Systems. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Aerial Thermography (AT). Thermal image analysis conducted in this research will contribute to utilizing thermography and UAVs for PV inspection by providing a more accurate and cost-efficient diagnosis of PV faults. In this research, PV module inspection was achieved through two steps: (i) PV monitoring and (ii) PV Fault Detection and Diagnosis (FDD). In the PV monitoring stage, PV cells were monitored by aerial thermography. In this stage, the thermal data was acquired for the next step. In the PV FDD stage, hot spot phenomenon and the condition of the PV modules were detected and measured. The proposed research will help with the problems of the modern PV inspection and, eventually, contribute to the performance of PV power generation.</p>

The protection of the electrical device is provided to prevent the electric shocks, protect the equipment and prevent a damage caused by electrical faults. The current protection for a small fault currents need not always have an effect. At the serious faults the current protections never work. The new type of device to protect against damage caused by electrical failure is an arc protection. The arc protection switches off safely all types of fault arc in different sizes. This work verifies its correct functionality in the case of electric circuit with deceptive signals from LED light sources. The output of the work is the measurement of the switching current of the LED light sources. The work is remarkable thanks the number of measurement for individual angles of switching on which have statistical meaning.

Tato práce se zabývá návrhem nového řídícího systému, odolného proti chybám, pro nestabilní samo-balancující dvoukolové vozidlo typu Segway. Původní systém vozidla je podroben analýze rizikovosti jeho součástí a na základě výsledků jsou navržena opatření pro zvýšení jeho bezpečnosti. Je navržena nová topologie řídícího systému obsahující dvě samostatné řídící jednotky, redundantní senzoriku a voter. Pro řídící jednotky byl vyvinut software obsahující bezpečnostní algoritmy a mechanismy přepínání kontrolních výstupů. V práci jsou také představeny dva matematické modely vozidla různé složitosti, které jsou následně využity při HIL testování nově navrženého systému. Celý návrh byl proveden s využitím nástrojů pro Rapid Control Prototyping.

The increased usage of solar panels worldwide highlights the importance of being able to detect faults in systems that use these panels. In this project, the historical power output (kWh) from solar panels combined with meteorological data was used to train a machine learning model to predict the expected power output of a given solar panel system. Using the expected power output, a comparison was made between the expected and the actual power output to analyze if the system was exposed to a fault. The result was that when applying the explained method an expected output could be created which closely resembled the actual output of a given solar panel system with some over- and undershooting. Consequentially, when simulating a fault (50% decrease of the power output), it was possible for the system to detect all faults if analyzed over a two-week period. These results show that it is possible to model the predicted output of a solar panel system with a machine learning model (using meteorological data) and use it to evaluate if the system is producing as much power as it should be. Improvements can be made to the system where adding additional meteorological data, increasing the precision of the meteorological data and training the machine learning model on more data are some of the options. / Med en ökande användning av solpaneler runt om i världen ökar även betydelsen av att kunna upptäcka driftstörningar i panelerna. Genom att utnyttja den historiska uteffekten (kWh) från solpaneler samt meteorologisk data används maskininlärningsmodeller för att förutspå den förväntade uteffekten för ett givet solpanelssystem. Den förväntade uteffekten används sedan i en jämförelse med den faktiska uteffekten för att upptäcka om en driftstörning har uppstått i systemet. Resultatet av att använda den här metoden är att en förväntad uteffekt som efterliknar den faktiska uteffekten modelleras. Följaktligen, när ett fel simuleras (50% minskning av uteffekt), så är det möjligt för systemet att hitta alla introducerade fel vid analys över ett tidsspann på två veckor. Dessa resultat visar att det är möjligt att modellera en förväntad uteffekt av ett solpanelssystem med en maskininlärningsmodell och att använda den för att utvärdera om systemet producerar så mycket uteffekt som det bör göra. Systemet kan förbättras på några vis där tilläggandet av fler meteorologiska parametrar, öka precision av den meteorologiska datan och träna maskininlärningsmodellen på mer data är några möjligheter.

Fault detection for residential photovoltaic power systems is an often-ignored problem. This thesis introduces a novel method for detecting power losses due to faults in solar panel performance. Five years of data from a residential system in Dalarna, Sweden, was applied on a random forest regression to estimate power production. Estimated power was compared to true power to assess the performance of the power generating systems. By identifying trends in the difference and estimated power production, faults can be identified. The model is sufficiently competent to identify consistent energy losses of 10% or greater of the expected power output, while requiring only minimal modifications to existing power generating systems.