This thesis in microscale gas chromatography (μGC) creates a paradigm shift in rapidly analyzing chemicals in the environment or analytes. We are looking for unexpected chemical changes that have been added purposefully or unintentionally. The work examines various aspects of μGC technology, including the optimization of ionic liquid stationary phase coatings for microfabricated columns, achieving up to 8300 theoretical plates per meter for naphthalene using 1-butylpyridinum bis(trifluoromethylsulfonyl)imide [BPY][NTf2] at 240°C. The development of portable systems for fuel adulteration detection is demonstrated, capable of discriminating 5% kerosene adulterated diesel fuel with four seconds of chromatogram analysis. The research also presents a novel parallel column configuration using three ionic liquid-coated semi-packed columns, each 1 m long and 240 μm deep, for complex gas analysis of up to 46 compounds. Key innovations discussed include optimized coating procedure of GC separation columns and implementation of GC based miniaturized electronic nose with the integration of machine learning algorithms. An evaluation of a prototype modular electric and fluidic μGC was evaluated and validated for benzene toluene, ethylbenzene, and xylene (BTEX). This research highlights the versatility of μGCs in applications ranging from environmental monitoring to quality control in the fuel industry, showcasing their potential as powerful tools for on-site chemical analysis with improved selectivity, resolution, and portability. / Doctor of Philosophy / This thesis advances the development of miniature chemical analytical systems, specifically gas chromatography, which is the gold standard for detecting volatile organic compounds in the environment. The work encompasses comprehensive improvements to these systems, from optimizing fabrication and coating of separation columns for better chemical separation to developing rapid prototyping methods for both hardware and software components. Through the integration of machine learning and innovative system designs, the thesis demonstrates significant improvements in detection capabilities, including identifying fuel tampering within seconds and monitoring harmful air pollutants at parts-per-billion levels over extended periods. These advances pave the way for making sophisticated chemical analysis accessible outside of traditional laboratories, enabling direct testing at locations where immediate results are crucial for safety and quality control.

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<p>This master thesis report discusses the use of self-organizing maps in a diesel engine management system. Self-organizing maps are one type of artificial neural networks that are good at visualizing data and solving classification problems. The system studied is the Vindax(R) development system from Axeon Ltd. By rewriting the problem formulation also function estimation and conditioning problems can be solved apart from classification problems. </p><p>In this report a feasibility study of the Vindax(R) development system is performed and for implementation the inlet air system is diagnosed and the engine torque is estimated. The results indicate that self-organizing maps can be used in future diagnosis functions as well as virtual sensors when physical models are hard to accomplish.</p>

This master thesis report discusses the use of self-organizing maps in a diesel engine management system. Self-organizing maps are one type of artificial neural networks that are good at visualizing data and solving classification problems. The system studied is the Vindax(R) development system from Axeon Ltd. By rewriting the problem formulation also function estimation and conditioning problems can be solved apart from classification problems. In this report a feasibility study of the Vindax(R) development system is performed and for implementation the inlet air system is diagnosed and the engine torque is estimated. The results indicate that self-organizing maps can be used in future diagnosis functions as well as virtual sensors when physical models are hard to accomplish.

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or “dirty” sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

<p>The use of lower airspace for air taxi and cargo applications opens up exciting prospects for futuristic Unmanned Aircraft Systems (UAS). However, ensuring the safety and security of these UAS within densely populated urban areas presents significant challenges. Most modern aircraft systems, whether unmanned or otherwise, rely on the Global Navigation Satellite System (GNSS) as a primary sensor for navigation. From satellite navigations point of view, the dense urban environment compromises positioning accuracy due to signal interference, multipath effects, etc. Furthermore, civilian GNSS receivers are susceptible to spoofing attacks since they lack encryption capabilities. Therefore, in this thesis, we focus on examining the safety and cybersecurity assurance of UAS in dense urban environments, from both theoretical and experimental perspectives. </p> <p>To facilitate the verification and validation of the UAS, the first part of the thesis focuses on the development of a realistic GNSS sensor emulation using a Gazebo plugin. This plugin is designed to replicate the complex behavior of the GNSS sensor in urban settings, such as multipath reflections, signal blockages, etc. By leveraging the 3D models of the urban environments and the ray-tracing algorithm, the plugin predicts the spatial and temporal patterns of GNSS signals in densely populated urban environments. The efficacy of the plugin is demonstrated for various scenarios including routing, path planning, and UAS cybersecurity. </p> <p>Subsequently, a robust state estimation algorithm for dynamical systems whose states can be represented by Lie Groups (e.g., rigid body motion) is presented. Lie groups provide powerful tools to analyze the complex behavior of non-linear dynamical systems by leveraging their geometrical properties. The algorithm is designed for time-varying uncertainties in both the state dynamics and the measurements using the log-linear property of the Lie groups. When unknown disturbances are present (such as GNSS spoofing, and multipath effects), the log-linearization of the non-linear estimation error dynamics results in a non-linear evolution of the linear error dynamics. The sufficient conditions under which this non-linear evolution of estimation error is bounded are derived, and Lyapunov stability theory is employed to design a robust filter in the presence of an unknown-but-bounded disturbance. </p>

Un modèle semi-analytique permettant de simuler le transfert thermique conjugué dans un mini/macro canal plan soumis à des sources de chaleur surfaciques localisées sur les faces externes et variantes en fonction du temps, a été présenté et vérifié. Plus le diamètre hydraulique du canal est petit, plus la caractérisation expérimentale interne (mesure des températures et des flux) en régime thermique permanent ou transitoire à l'aide des capteurs internes est délicate. Une méthode non-intrusive permettant d'estimer les conditions internes à partir des mesures de température par thermographie infrarouge sur les faces externes et d'un modèle semi-analytique, a été effectuée. Comme le coefficient de transfert convectif forcé classique perd son sens en régime instationnaire, une approche alternative basée sur une fonction de transfert, valable pour un système linaire et invariant dans le temps a été mise en œuvre. Cette fonction peut être calculée analytiquement (uniquement pour une géométrie simple) ou estimée expérimentalement (géométrie complexe). Grâce au caractère intrinsèque de cette fonction de transfert, deux capteurs virtuels ont été conçus : capteur virtuel de température et détecteur d'encrassement permettent respectivement d'estimer les températures internes et de détecter l'encrassement qui peut avoir lieu dans l'échangeur à partir des mesures de températures sur les faces externes / A semi-analytical model allowing to simulate the transient conjugate heat transfer in mini/macro plane channel subject to a heat source(s) localized on the external face(s), was presented and verified. The developed model takes into account advection-diffusion in the fluid and conduction in the solid. As the hydraulic diameter of the channel becomes small, the internal experimental characterization (measurement of temperature and heat flux) using internal sensors become tricky because internal sensors located may compromise the structural integrity of the whole system. A non-intrusive method for estimating the internal conditions from infrared temperature measurements on the external faces using the semi-analytical model was performed. Since the classic convective heat transfer coefficient loses its meaning in transient state, an alternative approach based on a transfer function, valid for Linear Time-Invariant (LTI) systems, was highlighted. This function can be calculated analytically only for a simple geometry. For complex geometries it can be estimated experimentally. Thanks to intrinsic character of this function, two characterization methods were designed. The first to estimate the temperature at a point from a measurement at another point in the system (virtual temperature sensor). The second method concerns the detection of fouling layers that may appear in the heat exchanger from temperature measurements on the external faces

L'industrie aéronautique évolue dans un contexte concurrentiel qui encourage les motoristes et avionneurs à réduire les coûts de production et à améliorer leurs services aux compagnies aériennes tels que la réduction des coûts d'exploitation et de maintenances des avions. Afin de relever ce défi économique, nous proposons dans cette thèse de remplacer l'architecture de régulation actuelle de certains équipements du turboréacteur, par une architecture simplifiée plus économe en capteurs et harnais en remplaçant la redondance matérielle des capteurs par une redondance analytique. Ainsi, en cas de fonctionnement anormal, les capteurs virtuels proposés pourront être utilisés pour consolider la prise de décision sur l'état du capteur par des tests de cohérence et de validation croisée et le cas échéant se substituer aux mesures.Dans ce travail de thèse, on s'est intéressé à la surveillance des systèmes de régulation de géométries variables (régulation du flux d'air en entrée et la quantité de carburant) avec comme contrainte forte la non-modification des paramètres des lois de commande existantes et le maintien de l'opérabilité du turboréacteur avec une dégradation des performances acceptables selon les spécifications du cahier des charges.Pour répondre à ces contraintes opérationnelles, une approche FTC (Fault Tolerant Control) passive est proposée. Cette approche nommée, AVG-FTC (Aircraft Variables Geometries-Fault-Tolerant Control) s'articule autour de plusieurs sous-systèmes mis en cascades. Elle tient compte du caractère instationnaire des systèmes étudiés, des différents couplages entre géométries variables et des incertitudes de modélisation. Ainsi, l'approche utilise un modèle neuronal du capteur couplé à un observateur de type Takagi-Sugeno-LPV (Linéaire à Paramètres Variant) et à un estimateur non linéaire robuste de type NEKF (Filtre de Kalman Étendu Neuronal) qui permet de produire une estimation temps réel des grandeurs surveillées. En utilisant la plateforme de prototypage et de tests du motoriste, nous avons pu évaluer l'approche AVG-FTC en simulant plusieurs scénarios de vol en présence de défaillances. Ceci a permis de montrer les performances de l'approche en termes de robustesse, de garantie de stabilité des boucles de régulations et d'opérabilité du turboréacteur. To improve the availability, a solution that aircraft manufacturers and suppliers adopt was the fault tolerance. / Over the years, market pressure has ensured that engine manufacturers invest in technology to provide clean, quiet, affordable, reliable, and efficient power. One of the last improvements is the introduction of virtual sensors that make use of non-like signals (analytical redundancy). This, is expected to improve weight, flight safety and availability. However, this new approach has not been widely investigated yet and needs further attention to remove its limitations for certificated applications.The concept of virtual sensors goes along with fault tolerance control strategies that help in limiting disruptions and maintenance costs. Indeed, a fault-tolerant control (FTC) scheme, allows for a leaner hardware structure without decreasing the safety of the system.We propose in this thesis work, to monitor through a passive FTC architecture, the Variables Geometries subsystems' of the engine: the VSV (Variable Stator Vane) and FMV (Fuel Metering Valve). A strong constrains, is not to change the parameters of the existing controllers. The approach named AVG-FTC (Variable Geometries Aircraft-Fault-Tolerant Control) is based on several cascaded sub-systems that allow to deal with the Linear Parameter Varying (LPV) model of the systems and modelling errors. The proposed FTC scheme uses a neural model of the sensor associated with a Takagi-Sugeno observer and a Neuronal Extended Kalman Filter Neural (NEKF) to account for those dynamics that cannot be explained with the LPV model to produce a real-time estimate of the monitored outputs. In case of sensor abnormality, the proposed virtual sensors can then be used as an arbitrator for sensor monitoring or as a healthy sensor used by the controller. To evaluate the approach, serval closed-loop simulations, on SNECMA jet-engine simulator have been performed. The results for distinct flight scenarios with different sensors faults have shown the capabilities of the approach in terms of stability and robustness.

[pt] As tecnologias de imageamento em três dimensões (3D) vêm tendo seu uso cada vez mais disseminado no meio acadêmico e no setor industrial, especialmente na forma de nuvens de pontos, uma representação matemática da geometria e superfície de um objeto ou área. No entanto, a obtenção desses dados pode ainda ser cara e demorada, reduzindo a eficiência de muitos procedimentos que são dependentes de um grande conjunto de nuvens de pontos, como a geração de datasets para treinamento de aprendizagem de máquina, cálculo de dossel florestal e inspeção submarina. Uma solução atualmente em voga é a criação de simuladores computacionais de sistemas de imageamento, realizando o escaneamento virtual de um cenário feito a partir de arquivos de objetos 3D. Este trabalho apresenta o desenvolvimento de um simulador de sistema LiDAR (light detection and ranging) baseado em algoritmos de rastreamento de raio com paralelismo (GPU raytracing), com o sensor virtual modelado por parâmetros metrológicos e calibrado por meio de comparação com um sensor real, juntamente com um gerador flexível de cenários virtuais. A combinação destas ferramentas no simulador resultou em uma geração robusta de nuvens de pontos sintéticas em cenários diversos, possibilitando a criação de datasets para uso em testes de conceitos, combinação de dados reais e virtuais, entre outras aplicações. / [en] Three dimensional (3D) imaging technologies have been increasingly used in academia and in the industrial sector, especially in the form of point clouds, a mathematical representation of the geometry and surface of an object or area. However, obtaining this data can still be expensive and time consuming, reducing the efficiency of many procedures dependent on a large set of point clouds, such as the generation of datasets for machine learning training, forest canopy calculation and subsea survey. A trending solution is the development of computer simulators for imaging systems, performing the virtual scanning of a scenario made from 3D object files. At the end of this process, synthetic point clouds are obtained. This work presents the development of a LiDAR system simulator (light detection and ranging) based on parallel ray tracing algorithms (GPU raytracing), with its virtual sensor modeled by metrological parameters. A way of calibrating the sensor is displayed, by comparing it with the measurements of a real LiDAR sensor, in addition to surveying error models to increase the realism of the virtual scan. A flexible scenario creator was also implemented to facilitate interaction with the user. The combination of these tools in the simulator resulted in a robust generation of synthetic point clouds in different scenarios, enabling the creation of datasets for use in concept tests, combining real and virtual data, among other applications.