No description available.

A very short and disagreeable audible and tactile response from a vehicle driveline may be excited when the throttle is abruptly applied or released, or when the clutch is rapidly engaged. The condition is most noticeable in low gear and in slow moving traffic, when other background engine and road noise levels are low. This phenomenon is known as clonk and is often associated with the first cycle of shuffle response, which is a low frequency longitudinal vehicle movement excited by throttle demand. It is often reported that clonk may coincide with each cycle of the shuffle response, and multiple clonks may then occur. The problem is aggravated by backlash and wear in the drivetrain, and it conveys a perception of low quality to the customer. Hitherto, reported investigations do not reveal or discuss the mechanism and causal factors of clonk in a quantitative manner, which would relate the engine impulsive torque to the elastic response of the driveline components, and in particular to the noise radiating surfaces. Crucially, neither have the issues of sensitivity, variability and non-linearity been addressed and published. It is also of fundamental importance that clonk is seen as a total system response to impulsive torque, in the presence of distributed lash at the vibro-elastic impact sites. In this thesis, the drivetrain is defined as the torque path from the engine flywheel to the road wheels. The drivetrain is a lightly damped and highly non-linear dynamic system. There are many impact and noise emitting locations in the driveline that contribute to clonk, when the system is subjected to shock torque loading. This thesis examines the clonk energy paths, from the initial impact to many driveline lash locations, and to the various noise radiating surfaces. Both experimental and theoretical methods are applied to this complex system. Structural and acoustic dynamics are considered, as well as the very important frequency couplings between elastic structures and acoustic volumes. Preliminary road tests had indicated that the clonk phenomenon was a, very short transient impact event between lubricated contacts and having a high frequency characteristic. This indicated that a multi-body dynamics simulation of the driveline, in conjunction with a high frequency elasto-acoustic coupling analysis, would be required. In addition, advanced methods of signal analysis would be required to handle the frequency content of the very short clonk time histories. These are the main novelties of this thesis. There were many successful outcomes from the investigation, including quantitative agreement between the numerical and experimental investigations. From the experimental work, it was established that vehicle clonk could be accurately reproduced on a driveline rig and also on a vehicle chassis dynamometer, under controlled test conditions. It then enabled Design of Experiments to be conducted and the principal causal factors to be identified. The experimental input and output data was also used to verify the mathematical simulation. The high frequency FE analysis of the structures and acoustic cavities were used to predict the dynamic modal response to a shock input. The excellent correlation between model and empirical data that was achieved, clearly established the clonk mechanism in mathematical physics terms. Localised impact of meshing gears under impulsive loads were found to be responsible for high frequency structural wave propagation, some of which coupled with the acoustics modes of cavities, when the speed of wave propagation reached supersonic levels. This finding, although previously surmised, has been shown in the thesis and constitutes a major contribution to knowledge.

The towed streamer controlled source electromagnetic (CSEM) system collects data faster than the conventional static node-based CSEM system. However, the towed streamer CSEM is typically much noisier than the conventional static node-based CSEM. Identifying and quantifying various sources of noise is important for the development of future robust electromagnetic streamer system. This is the problem I address in this thesis. I achieve this in three parts. First, I examine the idea that the towed streamer suffers from noise induced by its motion through the Earth’s magnetic field according to Faraday’s law of induction. I derive expressions for the motionally-induced noise for the cases of a horizontal streamer parallel to the acquisition vessel’s path and a curved streamer caused by a constant cross-current. These expressions demonstrate that the motionally-induced noise is sensitive to the magnitude of the feather angle at the head and at the tail of the streamer, and to the vertical and lateral motion of the streamer. The key finding is that no motionally-induced noise is generated when the streamer is horizontal and moving in a constant magnetic field. By contrast, when the streamer shape is curved because of cross-currents, motionally-induced noise is generated if the velocity of the streamer varies over time. Second, I analyse and compare the noise recorded using the first generation of towed streamer with the noise recorded using a static ocean bottom cable (OBC) CSEM. I find out that within the frequency range of interest, 0.01–1 Hz the towed streamer noise is 20 dB greater (factor of 10) than the noise recorded with the OBC CSEM. I show also that the motion of the telluric cable between the pair of electrodes in the towed streamer is responsible for this difference in amplitude between the two systems. In the frequency ranges, 0.03–0.1 Hz and 0.03–0.2 Hz, the motionally-induced noise is shown to be uncorrelated across all channels. However, within the frequency band 0.1–0.3 Hz, the motionally-induced noise correlation gradually increases and becomes well correlated at about 0.2 Hz. This correlated noise could be caused by ocean swell from surface waves, water flowing around the streamer or cross-currents. Finally, to identify and quantify the contribution of several distinct sources of noise, and to describe the mechanisms generating each source of noise, I co-designed a prototype towed streamer CSEM. I carried out an experiment with the prototype streamer suspended 1 m below the water surface in the controlled environment of the Edinburgh wave tank located in King’s building campus (the University of Edinburgh). I then subjected the streamer to flow running at velocities of 0–1ms−1 along its length and to waves propagating in the same direction, at 45°, and perpendicular relative to the streamer direction.

Theoretical analysis (i.e. simulation) promoted by the laboratory tests of final product is the important aspect for determination relating to choice and using the new infrastructural constituent. However, the laboratory analysis does not need to make conclusive results in some cases. At that time, it is necessary to complete the process of validation by the measurement in-situ (directly in the track section). The European legislation requires the long-term monitoring of the test constructions in this case. During this time, both the periodic monitoring of the chosen parameters and the following comparison with reference construction are recommended. The reference construction shall be inserted to the same track section and in the same time period as well as the test construction. The track gauge, the longitudinal shifts of rails vis-a-vis the sleepers, the clamping force of fastening, the rail head profile, the sleepers and the state of rail fastenings are possible to classify like the long-term monitored parameters. All these characteristics are so-called static. They are checked in time when no carriages pass through the monitored track section. However, the analysis and determination of the test construction behaviour (dynamic characteristic) are the most accurate by the moving load. Dissertation thesis is focused on measurement and analysis of the dynamic effects at the plain line. The aim of the thesis is the completion of recent system of the new track structures verification for the dynamic parameters observe. Methodology of the measurement and convenient mathematical apparatus for analysing the dynamic effects is proposed.

We are seeing a revolution in computing and artificial intelligence; intelligent machines have become ingrained in and improved every aspect of our lives. Despite the increasing number of intelligent devices and breakthroughs in artificial intelligence, we have yet to achieve truly intelligent environments. Audio is one of the most common sensing and actuation modalities used in intelligent devices. In this thesis, we focus on how we can more robustly integrate audio intelligence into a wide array of resource-constrained platforms that enable more intelligent environments. We present systems and methods for adaptive audio filtering that enables us to more robustly embed acoustic intelligence into a wide range of real time and resource-constrained mobile, embedded, and cyber-physical systems that are adaptable to a wide range of different applications, environments, and scenarios. First, we introduce methods for embedding audio intelligence into wearables, like headsets and helmets, to improve pedestrian safety in urban environments by using sound to detect vehicles, localize vehicles, and alert pedestrians well in advance to give them enough time to avoid a collision. We create a segmented architecture and data processing pipeline that partitions computation between embedded front-end platform and the smartphone platform. The embedded front-end hardware platform consists of a microcontroller and commercial-off-the shelf (COTS) components embedded into a headset and samples audio from an array of four MEMS microphones. Our embedded front-end platform computes a series of spatiotemporal features used to localize vehicles: relative delay, relative power, and zero crossing rate. These features are computed in the embedded front-end headset platform and transmitted wirelessly to the smartphone platform because there is not enough bandwidth to transmit more than two channels of raw audio with low latency using standard wireless communication protocols, like Bluetooth Low-Energy. The smartphone platform runs machine learning algorithms to detect vehicles, localize vehicles, and alert pedestrians. To help reduce power consumption, we integrate an application specific integrated circuit into our embedded front-end platform and create a new localization algorithm called angle via polygonal regression (AvPR) that combines the physics of audio waves, the geometry of a microphone array, and a data driven training and calibration process that enables us to estimate the high resolution direction of the vehicle while being robust to noise resulting from movements in the microphone array as we walk the streets. Second, we explore the challenges in adapting our platforms for pedestrian safety to more general and noisier scenarios, namely construction worker safety sounds of nearby power tools and machinery that are orders of magnitude greater than that of a distant vehicle. We introduce an adaptive noise filtering architecture that allows workers to filter out construction tool sounds and reveal low-energy vehicle sounds to better detect them. Our architecture combines the strengths of both the physics of audio waves and data-driven methods to more robustly filter out construction sounds while being able to run on a resource-limited mobile and embedded platform. In our adaptive filtering architecture, we introduce and incorporate a data-driven filtering algorithm, called probabilistic template matching (PTM), that leverages pre-trained statistical models of construction tools to perform content-based filtering. We demonstrate improvements that our adaptive filtering architecture brings to our audio-based urban safety wearable in real construction site scenarios and against state-of-art audio filtering algorithms, while having a minimal impact on the power consumption and latency of the overall system. We also explore how these methods can be used to improve audio privacy and remove privacy-sensitive speech from applications that have no need to detect and analyze speech. Finally, we introduce a common selective audio filtering platform that builds upon our adaptive filtering architecture for a wide range of real-time mobile, embedded, and cyber-physical applications. Our architecture can account for a wide range of different sounds, model types, and signal representations by integrating an algorithm we present called content-informed beamforming (CIBF). CIBF combines traditional beamforming (spatial filtering using the physics of audio waves) with data driven machine learning sound detectors and models that developers may already create for their own applications to enhance and filter out specified sounds and noises. Alternatively, developers can also select sounds and models from a library we provide. We demonstrate how our selective filtering architecture can improve the detection of specific target sounds and filter out noises in a wide range of application scenarios. Additionally, through two case studies, we demonstrate how our selective filtering architecture can easily integrate into and improve the performance of real mobile and embedded applications over existing state-of-art solutions, while having minimal impact on latency and power consumption. Ultimately, this selective filtering architecture enables developers and engineers to more easily embed robust audio intelligence into common objects found around us and resource-constrained systems to create more intelligent environments.

Ocean noise pollution is an invisible but growing threat. There are many sources of sound in the ocean but human underwater radiated noise, in particular from shipping is one of the most prominent one. Ocean noise pollution can interfere or sometimes even directly harm marine life.  This thesis is in collaboration with Kongsberg Maritime which aims to develop an underwater radiated noise prediction method for the ELegance pod system. In particular, the focus is on the noise generated as a direct effect of the permanent magnet motor vibrations. Kongsberg wants to be able to calculate the underwater radiated noise for different pod geometries and engine configurations in order to find an optimal operating speed of the electric motor. The underwater radiated noise prediction is carried out using two methods. The first one is a 2-way coupled fluid-structure interaction harmonic response model, dealing with the vibrations. In addition, the flow induced noise is evaluated using CFD combined with Ffowcs-Williams Hawkings acoustic analogy.  The harmonic response model is used to calculate the sound in terms of a frequency response, which can be translated to revolutions per minute of the rotor. This allows Kongsberg to identify rotor speeds where the operation may or may not be optimal. The flow induced noise is investigated for a typical transit speed. The results show this noise is multiple orders of magnitude smaller than the sound caused by the vibrations. This together with the fact that the computational cost of CFD is large suggests that the flow induced noise is not something Kongsberg needs to consider at an early design stage. Neither the propeller nor cavitation is considered in this thesis, due to the limited computational resources but also that Kongsberg designs propellers that are vessel specific. These sources of sound become important when considering the full acoustic profile of a propulsion unit of this type.

<p dir="ltr">The industry's interest in electrified powertrain-equipped vehicles has increased due to environmental and economic reasons. Electrified powertrains, in general, produce lower sound and vibration level than those equipped with internal combustion engines, making noise and vibration (N&V) from other non-engine powertrain components more perceptible. One such N&V type that arouses concern to both vehicle manufacturers and passengers is gear growl, but the signal characteristics of gear growl noise and vibration and the threshold of those characteristics that can be used to determine whether a gear growl requires attention are not yet well understood. This study focuses on developing a method to detect gear-growl based on the N\&V measurements and determining thresholds on various severities of gear-growl using supervised machine learning classification. In general, a machine learning classifier requires sufficient high-quality training data with strong information independence to ensure accurate classification performance. In industrial practices, acquiring high-quality vehicle NVH data is expensive in terms of finance, time, and effort. A physically informed data augmentation method is, thus, proposed to generate realistic powertrain NVH signals based on high-quality measurements which not only provides a larger training data set but also enriches the signal feature variations included in the data set. More specifically, this method extracts physical information such as angular speed, tonal amplitudes distribution, and broadband spectrum shape from the measurement data. Then, it recreates a synthetic signal that mimics the measurement data. The measured and simulated (via data augmentation) are transformed into feature matrix representation so that the N\&V signals can be used in the classification model training process. Features describing signal characteristics are studied, extracted, and selected. While the root-mean-square (RMS) of the vibration signal and spectral entropy were sufficient for detecting gear-growl with a test accuracy of 0.9828, the acoustic signal required more features due to background noise, making data linearly inseparable. The minimum Redundancy Maximum Relevance (mRMR) feature scoring method was used to assess the importance of acoustic signal features in classification. The five most important features based on the importance score were the angular acceleration of the driveshaft, the time derivative of RMS, the tone-to-noise ratio (TNR), the time derivative of the spectral spread of the tonal component of the acoustic signal, and the time derivative of the spectral spread of the original acoustic signal (before tonal and broadband separation). A supervised classification model is developed using a support vector machine from the extracted acoustic signal features. Data used in training and testing consists of steady-state vehicle operations of 25, 35, 45, and 55 mph, with two vehicles with two different powertrain specs: axles with 4.56 and 6.14 gear ratios. The dataset includes powertrains with swapped axles (four different configurations). Techniques such as cost weighting, median filter, and hyperparameter tuning are implemented to improve the classification performance where the model classifies if a segment in the signal represents a gear-growl event or no gear-growl event. The average accuracy of test data was 0.918. A multi-class classification model is further implemented to classify different severities based on preliminary subjective listening studies. Data augmentation using signal simulation showed improvement in binary classification applications. In this study, only gear-growl was used as a fault type. Still, data augmentation, feature extraction and selection, and classification methods can be generalized for NVH signal-based fault diagnosis applications. Further listening studies are suggested for improved classification of multi-class classification applications.</p>

La prédiction des vibrations induites par un écoulement est essentielle dans la conception des conduits de nombreuses installations industrielles, en particulier dans l’industrie du gaz. Notre étude concerne la prévision du bruit et la vibration des conduits soumis à un écoulement turbulent à faible nombre de Mach. Notre objectif est de présenter une étude numérique et expérimentale permettant aux ingénieurs de mieux comprendre le couplage entre l’excitation aléatoire et le conduit pour deux géométries (circulaire ou rectangulaire). Une approche expérimentale est développée et utilisée pour valider les prévisions numériques. Deux cas sont étudiés : (i) un conduit droit sans singularité, où les modes acoustiques du conduit sont excités par une couche limite turbulente (TBL) et (ii) un conduit droit avec un diaphragme inséré en amont qui génère une source acoustique localisée. La contribution acoustique est déterminée soit par des méthodes de mesure d’interspectres, soit à l’aide des outils de mécanique des fluides numérique (CFD) et d’analogies aéroacoustiques. La réponse de la structure est estimée par une approche dite de « couplage faible » qui utilise des fonctions de transfert modale d’un conduit fini simplement appuyé. Les mesures conduiront à évaluer et suggérer des améliorations de modèles empiriques existants de densité interspectrale de puissance (CPSD) dans un contexte d’écoulements internes turbulents. Une analyse modale expérimentale d’un conduit rectangulaire finie est confrontée à des méthodes de calcul pour évaluer l’effet des conditions aux limites, du rayonnement acoustique et de l’amortissement aérodynamique. Le couplage fluide structure est analysé par la fonction de « joint acceptance » à la fois dans le domaine spatial et dans le domaine des nombres d’onde. L’excitation comprend à la fois les contributions acoustiques et hydrodynamiques à l’aide des CPSD exprimées sur la base des fonctions de cohérence de type Corcos, champ diffus et modes acoustiques d’ordre élevé. Enfin, les études numériques et expérimentales de cette thèse ont été utilisées pour développer un cadre d’étude et de modélisation du bruit et des vibrations dans les conduites, qui relie la dynamique des fluides, les modèles analytiques et empiriques à des techniques efficaces d’analyse aléatoire. / Pipeline and duct vibrations can cause a range of issues from unplanned shutdownsto decreased equipment life time. Thus, the prediction of flow-induced vibrations is essential in piping design in many industrial plants, especially, for Gas industry. This study deals with the prediction of pipe flow noise and vibration at low Mach number. We aim to present a numerical and experimental study which can offer engineers a better understanding of the coupling between random excitation and duct section for two geometries (circular or rectangular). An experimental facility and measurement approach is developed and used to validate numerical predictions. Two cases are investigated: (i) a straight duct with no singularity, duct acoustic modes are excited by the Turbulent Boundary Layer (TBL) and (ii) a straight duct with a diaphragm inserted upstream generating a localized acoustic source. The acoustic contribution is either measured via cross-spectra based methods or calculated using Computational Fluid Dynamics (CFD) and aeroacoustic analogies. The response of the structure is estimated via a ‘blocked’ approach using analytical modal Frequency Response Functions (FRFs) of a simply supported finite duct. Measurements will lead to evaluate and suggest improvements to existing Cross Power Spectral Density (CPSD) empirical models in a context of internal turbulent flows. Experimental modalanalysis of a finite rectangular duct are confronted to computational methods to assess the effect of the Boundary Conditions (BCs), the resistive damping from coupling with the internal acoustic medium and aerodynamic damping. The fluid-structure coupling is analyzed through the joint acceptance function both in the spatial and wave number domain. The excitation includes both the acoustic and hydrodynamic contributions using CPSD written on the basis of Corcos, Diffuse Acoustic Field (DAF) and acoustic duct mode coherence functions. Finally, the numerical and experimental studies in this thesis were used to develop a framework for studying and modelling pipe flow noise and vibration which links CFD, analytical and empirical models to efficient random analysis techniques.

Είναι ευρύτατα γνωστό ότι ο θόρυβος δημιουργείται από δονούμενες επιφάνειες. Για την αντιμετώπιση του θορύβου στην πηγή του, δηλαδή τη δονούμενη επιφάνεια, δύο κυρίως τρόποι έχουν αναπτυχθεί. Ο πρώτος τρόπος αφορά τη χρησιμοποίηση παθητικών μέσων, δηλαδή ηχομονωτικών υλικών που αποσβένουν συγκεκριμένες συχνότητες. Ο δεύτερος τρόπος αφορά τη χρήση ενεργητικών μέσων. Τα ενεργητικά μέσα είναι διατάξεις που αποτελούνται από ένα σύστημα ελέγχου και ένα σύνολο αισθητήρων και ενεργοποιητών. Η λειτουργία ενός τέτοιου Συστήματος Ενεργού Ελέγχου Δονήσεων (ΣΕΕΔ) βασίζεται στην καταγραφή μέσω των αισθητήρων του τρόπου δόνησης της επιφάνειας (πρωτεύον πεδίο δόνησης), την δημιουργία σημάτων ελέγχου από τον ελεγκτή (ίδιου πλάτους αλλά με διαφορά φάσης 180o) και την αποστολή τους στους ενεργοποιητές που θα δημιουργήσουν ένα δευτερεύον πεδίο δόνησης. Η υπέρθεση των δύο πεδίων έχει σαν αποτέλεσμα την δημιουργία ενός εναπομείναντος πεδίου με πλάτη δόνησης αισθητά χαμηλότερα από αυτά του πρωτεύοντος. Το αντικείμενο της παρούσας διατριβής είναι η ανάπτυξη ενός γενικευμένου ΣΕΕΔ, ο έλεγχος του οποίου βασίζεται σε εργαλεία Τεχνητής Νοημοσύνης όπως τα Τεχνητά Νευρωνικά Δίκτυα και οι Γενετικοί Αλγόριθμοι για την αναγνώριση του τρόπου δόνησης οποιασδήποτε επιφάνειας και το βέλτιστο έλεγχο της δόνησής της, χωρίς να απαιτείται καμία πρότερη γνώση της δυναμικής συμπεριφοράς της επιφάνειας. Επιπλέον, το υπό μελέτη ΣΕΕΔ είναι ικανό να ελέγχει τέσσερις συχνότητες αντί μιας που απαντάται συνήθως στην πλειονότητα των εφαρμογών. Ο σκοπός της διατριβής αυτής είναι η απόδειξη της αρχής λειτουργίας ενός τέτοιου συστήματος. Η προσέγγιση για την επίτευξη αυτού του στόχου περιλαμβάνει πειραματικές μετρήσεις ενός πρωτότυπου ΣΕΕΔ σε μία απλοποιημένη πειραματική διάταξη. Τα αποτελέσματα από την εφαρμογή του εν λόγω ΣΕΕΔ δείχνουν ότι παρά τους περιορισμούς που υπεισέρχονται λόγω των δυνατοτήτων του υλικού (hardware) του χρησιμοποιούμενου εξοπλισμού, το υπό μελέτη ΣΕΕΔ λειτουργεί επιτυχώς στη βασική αρχή του, ενώ έχει τις προϋποθέσεις και τη δυναμική για περαιτέρω βελτιστοποίηση και εξέλιξη σε ένα ευρύ φάσμα εφαρμογών. / It is generally approved that noise is created by vibrating surfaces. In order to tackle this phenomenon at its source, mainly two approaches have been followed. The first approach involves passive means, that is sound insulation materials that dampen certain frequencies. The second approach involves the use of active means. The active means are arrangements that consist of a control system and a set of sensors and actuators. The application of such an arrangement for vibration control is called Active Vibration Control (AVC) and is based on the sampling (by means of sensors) of the primary field of vibration of the surface, the creation of control signals by the controller (secondary field - of the same amplitude but with phase difference of 180o) and finally applying these control signals on the vibrating surface, by means of the actuators. The superimposing of the two vibration signals (primary and secondary) results to a residual field where the amplitudes of vibration are significantly lower than in the primary. The objective of the thesis at hand is to develop a Generic AVC with the controller developed using Artificial Intelligence tools such as the Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), in order to identify the vibration patterns of any surface and the optimal control of its vibration, without any prior knowledge of the dynamic behavior of the surface. Moreover, the developed AVC system will be able to identify and control four dominating frequencies instead of one that is usually the choice in the majority of similar applications. The scope of this work is the ‘Proof of Concept’ of the successful operation of such a generic AVC system. The approach to this end includes experimental testing of a prototype AVC system on a simplified experimental set-up. The results of the application of the developed AVC system, performed also by independent parties in the framework of a EC-funded Basic Research project, prove the successful operation of the developed AVCS, even within the limitation of the contemporary data acquisition platform (hardware and software) used, imposes limitations in the efficiency of the AVCS, and provide the basis for its further development and application in a multitude of problems.

The awareness of the human responsibility in global warming has led to various private and public initiatives to reduce the emission of greenhouse gases, up to international level. In this context the development of renewable technologies in two sectors having an important ecological footprint, i.e. production of electricity and transportation, is targeted.In the firstly mentioned sector, the progression of wind energy is at present the most rapid among all renewable energies. But wind turbines still suffer from a global lack of reliability and accessibility compared to classical power plants, leading to potentially important production losses and repair costs. The first part of the present work focuses on the improvement of the electrical chain reliability by the combination of an estimator and a fault-detection algorithm to achieve sensor-fault tolerance, taking benefit from the already available measurement redundancies on doubly-fed-induction-machine (DFIG) drives.Estimators and sensor-fault detection and isolation (FDI) in DFIGs have been the object of many research papers. However, most of them only consider one unique type of measurement and only a few works consider magnetic saturation. A new combination of a closed-loop observer with a cumulative-sum-based FDI technique, considering magnetic saturation and using limited computational resources is proposed here to estimate electromagnetic torque, rotor currents and position for sensor-fault detection and tolerance. This algorithm is then validated in steady state and in case of moderate transients, unbalanced conditions and misestimation of DFIG parameters. The estimator can also start on the fly during the start-up process of the generator.In the transportation sector, new hybrid and full-electric vehicles start to be visible on the roads, but still need important technological improvements in terms of autonomy, performances, but also produced noise and vibrations. The objectives of the second part of this doctoral thesis are related to this last challenge and consist of the experimental investigation of noise, vibration and harshness (NVH) aspects of an 8/6 switched-reluctance machine (SRM) designed for an electrical vehicle (EV).The NVH issues of SRMs, limiting their usage in automotive and other domains, have been the subject of various papers. However, most of them focus on modal analysis or detailed phenomena, while a global evaluation of NVH aspects of SRMs in normal working conditions is rarely made, as well as the use of reproducible sound metrics. A global and relatively fast experimental method to assess the evolution of noise and vibration is proposed. Tests are performed in transient regime, using continuously varying working conditions when possible, for the excitation of a large band of frequencies. The resulting current, radial vibration and acoustic noise are presented as spectrograms for an easy distinction of affected and unaffected frequencies and compared with the associated loudness and sharpness.Furthermore, the implementation of a new faster-sampled current-hysteresis controller has allowed to improve the quality of the control and of the acoustic noise by reducing the current-ripple amplitude and the excitation of resonances. The various tests show that the switching frequency has to be high enough to avoid exciting the ovalization mode of the SRM, but not too high to avoid producing a too sharp noise. The ripple amplitude also has to be considered to limit the loudness. Therefore, soft chopping, or a reduced DC-bus voltage at low speeds, has to be preferred with a relative small hysteresis bandwidth. Finally, the case of an open-phase fault has been investigated showing amplified even current orders in the vibration and acoustic-noise plots. / La prise de conscience de la responsabilité humaine dans le réchauffement climatique est à la source de nombreuses initiatives publiques et privées parfois internationales pour réduire les émissions de gaz à effet de serre. Dans ce contexte, le développement de technologies durables dans deux secteurs à forte empreinte écologique est visé: la production d'énergie électrique et les transports. Dans le premier secteur, la progression de l'éolien est à présent la plus rapide parmi toutes les énergies renouvelables. Cependant, les éoliennes souffrent d'un manque global de fiabilité et d'accessibilité par rapport aux centrales électriques classiques, ce qui conduit potentiellement à des pertes de production et des coûts de réparation importants. La première partie de ce travail se focalise sur l'amélioration de la chaîne électrique en la rendant tolérante aux défauts de capteurs au moyen de la combinaison d'un estimateur et d'un algorithme de détection de défauts, tirant avantage de la redondance de mesures déjà présente sur les entraînements à machines asynchrones à double alimentation (MADA). Les estimateurs et la détection et l'isolation de défauts de capteurs sur les MADA a fait l'objet de nombreuses publications scientifiques. Cependant, la plupart d'entre elles considèrent un seul type de mesure et peu de travaux prennent en compte la saturation magnétique. Une nouvelle combinaison d'un observateur et d'un algorithme de détection de défauts de type ‘CUSUM', considérant la saturation magnétique et nécessitant une puissance de calcul limitée, est proposée dans cette thèse pour l'estimation du couple électromagnétique, des courants et de la position rotoriques en vue d'obtenir la tolérance aux défauts de capteurs. Cet algorithme est validé en régime permanent et cas de transitoires modérés, de tensions du réseau déséquilibrées et d'erreurs d'estimation des paramètres de laMADA. L'estimateur est aussi capable de démarrer seul lors du démarrage de la génératrice. Dans le secteur des transports, des véhicules hybrides et électriques commencent à être visibles sur les routes, malgré que des progrès technologiques importants en termes d'autonomie, de performances, mais aussi de bruits et vibrations soient encore nécessaires pour une utilisation plus intensive. L'objectif de la deuxième partie de cette thèse se rapporte à ce dernier défi et consiste à analyser les aspects acoustiques et vibratoires d'une machine à réluctance variable 8/6 conçue pour propulser un véhicule électrique. Ces problèmes acoustiques et vibratoires, qui limitent notamment l'usage de telles machines dans des applications de propulsion, ont été l'objet de divers articles scientifiques. Cependant, la plupart d'entre eux sont focalisés sur des analyses modales ou de phénomènes particuliers, alors qu'une évaluation globale des problèmes de bruit et de vibration des machines à réluctance variable en conditions normales de fonctionnement est rarement proposée, de même que l'utilisation de critères de qualité sonore. Une méthode expérimentale globale et relativement rapide pour évaluer l'évolution du bruit et des vibrations est proposée dans ce travail. Les essais sont réalisés en régime transitoire pour exciter une large bande de fréquences et en faisant varier continuellement, quand cela est possible, les conditions de fonctionnement. Les courants, vibrations radiales et bruits acoustiques résultants sont présentés sous formes de cartographies couleur pour une distinction aisée des fréquences affectées et non-affectées et comparés aux niveaux calculés de bruyance et d'acuité correspondants. Par ailleurs, la mise en place d'un nouveau régulateur à hystérèse en courant à plus grande fréquence d'échantillonnage a permis d'améliorer la qualité de la commande et du bruit acoustique associé en réduisant l'amplitude des oscillations de courant et l'excitation des fréquences de résonance. Les essais montrent que la fréquence de commutation doit être suffisamment élevée pour éviter l'excitation du mode d'ovalisation de la machine, mais pas trop pour éviter une trop grande acuité du son produit. L'amplitude des oscillations doit aussi être considérée pour limiter la bruyance. En conséquence, une commande en ‘soft chopping', ou une tension réduite du bus continu à basse vitesse, doit être combinée à une bande d'hystérèse relativement faible. Enfin, le cas d'un défaut de phase ouverte a été étudié et a montré une amplification des ordres pairs du courant dans les spectres vibratoires et acoustiques. / De bewustwording van de menselijke verantwoordelijkheid in de opwarming van de aarde heeft tot verschillende private en publieke initiatieven geleid om de uitstoot van broeikasgassen te verminderen. In deze context is de ontwikkeling van hernieuwbare technologieën hoofdzakelijk gericht op twee sectoren met een belangrijke ecologische impact: elektriciteitsproductie en transport.In de eerste sector ontwikkelt windenergie zich op dit moment sneller dan alle andere hernieuwbare energieën. Maar windturbines lijden nog steeds aan een gebrek aan betrouwbaarheid en toegankelijkheid, en dus aan potentieel hogere productieverliezen en herstelkosten, als ze met klassieke krachtcentrales worden vergeleken. In het eerste deel van deze doctoraatsthesis wordt op de verbetering van de elektrische keten geconcentreerd door de combinatie van een schatter en een foutdetectie- en -isolatiealgoritme (FDI-algoritme) om sensorfouttolerantie te verkrijgen dankzij de reeds aanwezige meetovertolligheid op dubbelgevoede inductiemachine (DFIG) aandrijvingen.Schatters en sensor-FDI-algoritmen zijn het onderwerp van vele wetenschappelijke artikelen geweest. Meestal wordt maar één sensortype beschouwd en met de magnetische verzadiging wordt niet vaak rekening gehouden. Een nieuwe combinatie van een schatter met gesloten terugkoppeling en een FDI-techniek gebaseerd op het ‘cumulative-sum' principe is voorgesteld. Zo kan het elektromagnetische koppel, de rotorstromen en positie worden geschat voor sensor FDI en fouttolerantie met beperkte rekenkosten en zonder de magnetische verzadering te verwaarlozen. Het algoritme wordt in stabiele toestand gevalideerd, maar ook in het geval van gematigde transiënte situaties, onevenwichtige netwerkomstandigheden en een verkeerde schatting van DFIG parameters. Het kan ook vanzelf starten tijdens de startprocedure van de generator.In de vervoersector beginnen hybride en elektrische voertuigen op de wegen te rijden. Maar vooreen intensiever gebruik van zo'n wagens zijn er nog technologische verbeteringen nodig met betrekking tot autonomie, prestaties en ook geluid en trillingen (NVH). Het tweede deel van de thesis betreft die laatste uitdaging en bestaat uit het experimentele onderzoek van geluid en trillingen op een 8/6 variabelereluctantiemachine (SRM) ontwikkeld voor elektrische voertuigen.De NVH-problemen van SRM's beperken hun gebruik in automobiele en andere toepassingen enonderzoek wordt erover voortgezet. Vele wetenschappelijke artikelen focussen toch op modale analyse of gedetailleerde fenomenen terwijl een globale evaluatie van NVH aspecten in SRM's in gewone operatiecondities nauwelijks wordt gemaakt. Hetzelfde geldt voor het gebruik van reproduceerbare geluidsmetrieken. Een globale en vrij vlugge experimentele methode is hier voorgesteld om het NVH gedrag te schatten. Testen worden in transiënte situaties uitgevoerd om een brede frequentieband te exciteren, indien mogelijk met voortdurend variërende condities. De gemeten fasestroom, trilling en geluid worden als kleurmappen geplot om het verschil tussen beïnvloede en niet geaffecteerde frequenties te vergemakkelijken en met de berekende akoestische luidheid en scherpte vergeleken.Bovendien heeft de implementatie van een sneller bemonsterd stroomhysteresisregelaar geleid tot een verbetering van de regulatie- en akoestische kwaliteit door de amplitude van de stroomrimpeling en de excitatie van resonantiefrequenties te verminderen. De testresultaten tonen dat de schakelfrequentie voldoende hoog moet zijn om de excitatie van de ovale vervormingsmode te vermijden, maar niet te hoog om de scherpte van het geluid te beperken. De amplitude van de rimpel beïnvloedt ook de luidheid en daarvoor moet in aanmerking worden genomen. Bijgevolg zou ‘soft chopping'mode, of een lagere spanning op de DC-bus bij lage toerentallen, met een relatief klein hysteresisband beter worden gebruikt. Uiteindelijk wordt het geval van een openfasefout bestudeerd en onthult versterkte gelijke frequentievolgorden in de trilling- en geluidplots. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished