It is preferable that control and bearing condition monitoring are integrated, as the condition of the system should influence control actions. As wind turbines mainly work in remote areas, it becomes necessary to develop a wireless platform for the control system. A fuzzy system with self-tuning mechanism was developed. The input speed error and speed change were selected to control the shaft speed, while the kurtosis and peak-to-peak values were used as another set of inputs to monitor the bearing conditions. To enhance effectiveness, wait-and-see (WAS) logic was used as the pre-processing step for the raw vibration signal. The system was implemented on the LabVIEW platform. Experiments have shown that the system can effectively adjust motor rotating speed in response to bearing conditions. For future studies, more advanced fault detection methods can be integrated with proper tuning mechanisms to enrich the performance and function of the controller.

Synchronous generators play a vital role in power systems. One of the major mechanical faults in synchronous generators is related to bearings. The popular vibration analysis method has been utilized to detect bearing faults for years. However, bearing health monitoring based on vibration analysis is expensive. One of the reasons is because vibration analysis requires costly vibration sensors and the extra costs associated with its proper installation and maintenance. This limitation prevents continuous bearing condition monitoring, which gives better performance for rolling element bearing fault detection, compared to the periodic monitoring method that is a typical practice for bearing maintenance in industry. Therefore, a cost effective alternative is necessary. In this study, a sensorless bearing fault detection method for synchronous generators is proposed based on the analysis of electrical signatures, and its bearing fault detection capability is demonstrated. Experiments with staged bearing faults are conducted to validate the effectiveness of the proposed fault detection method. First, a generator test bed with an in- situ bearing damage device is designed and built. Next, multiple bearing damage experiments are carried out in two vastly different operating conditions in order to obtain statistically significant results. During each experiment, artificially induced bearing current causes accelerated damage to the front bearing of the generator. This in-situ bearing damage process entirely eliminates the necessity of disassembly and reassembly of the experimental setup that causes armature spectral distortions. The electrical fault indicator is computed based on stator voltage signatures without the knowledge of machine and bearing specific parameters. Experimental results are compared using the electrical indicator and a vibration indicator that is calculated based on measured vibration data. The results indicate that the electrical indicator can be used to analyze health degradation of rolling element bearings in synchronous generators in most instances. Though the vibration indicator enables early bearing fault detection, it is found that the electrical fault indicator is also capable of detecting bearing faults well before catastrophic bearing failure.

It is preferable that control and bearing condition monitoring are integrated, as the condition of the system should influence control actions. As wind turbines mainly work in remote areas, it becomes necessary to develop a wireless platform for the control system. A fuzzy system with self-tuning mechanism was developed. The input speed error and speed change were selected to control the shaft speed, while the kurtosis and peak-to-peak values were used as another set of inputs to monitor the bearing conditions. To enhance effectiveness, wait-and-see (WAS) logic was used as the pre-processing step for the raw vibration signal. The system was implemented on the LabVIEW platform. Experiments have shown that the system can effectively adjust motor rotating speed in response to bearing conditions. For future studies, more advanced fault detection methods can be integrated with proper tuning mechanisms to enrich the performance and function of the controller.

It is preferable that control and bearing condition monitoring are integrated, as the condition of the system should influence control actions. As wind turbines mainly work in remote areas, it becomes necessary to develop a wireless platform for the control system. A fuzzy system with self-tuning mechanism was developed. The input speed error and speed change were selected to control the shaft speed, while the kurtosis and peak-to-peak values were used as another set of inputs to monitor the bearing conditions. To enhance effectiveness, wait-and-see (WAS) logic was used as the pre-processing step for the raw vibration signal. The system was implemented on the LabVIEW platform. Experiments have shown that the system can effectively adjust motor rotating speed in response to bearing conditions. For future studies, more advanced fault detection methods can be integrated with proper tuning mechanisms to enrich the performance and function of the controller.

This diploma thesis deals with the design and creation of an algorithm for generating simulated signal data from a vibration diagnostics device. The first part is focused on theoretical acquaintance with vibration diagnostics and characteristics of individual defects of rotary machines. The next part deals with the possibilities of mathematical and kinematic simulations using a computer software. The main part of this work is dedicated to design and creation of software for generating simulated signal data. In the last part, the principle of simulation of specific defects of rotary machines is clearly demonstrated.

Os motores de indução desempenham um importante papel na indústria, fato este que destaca a importância do correto diagnóstico e classificação de falhas ainda em fase inicial de sua evolução, possibilitando aumento na produtividade e, principalmente, eliminando graves danos aos processos e às máquinas. Assim, a proposta desta tese consiste em apresentar um multiclassificador inteligente para o diagnóstico de motor sem defeitos, falhas de curto-circuito nos enrolamentos do estator, falhas de rotor e falhas de rolamentos em motores de indução trifásicos acionados por diferentes modelos de inversores de frequência por meio da análise das amplitudes dos sinais de corrente de estator no domínio do tempo. Para avaliar a precisão de classificação frente aos diversos níveis de severidade das falhas, foram comparados os desempenhos de quatro técnicas distintas de aprendizado de máquina; a saber: (i) Rede Fuzzy Artmap, (ii) Rede Perceptron Multicamadas, (iii) Máquina de Vetores de Suporte e (iv) k-Vizinhos-Próximos. Resultados experimentais obtidos a partir de 13.574 ensaios experimentais são apresentados para validar o estudo considerando uma ampla faixa de frequências de operação, bem como regimes de conjugado de carga em 5 motores diferentes. / Induction motors play an important role in the industry, a fact that highlights the importance of correct diagnosis and classification of faults on these machines still in early stages of their evolution, allowing increase in productivity and mainly, eliminating major damage to the processes and machines. Thus, the purpose of this thesis is to present an intelligent multi-classifier for the diagnoses of healthy motor, short-circuit faults in the stator windings, rotor broken bars and bearing faults in induction motors operating with different models of frequency inverters by analyzing the amplitude of the stator current signal in the time domain. To assess the classification accuracy across the various levels of faults severity, the performances of four different learning machine techniques were compared; namely: (i) Fuzzy ARTMAP network, (ii) Multilayer Perceptron Network, (iii) Support Vector Machine and (iv) k-Nearest-Neighbor. Experimental results obtained from 13.574 experimental tests are presented to validate the study considering a wide range of operating frequencies and also load conditions using 5 different motors.

Ce travail de thèse traite de la détection des défauts mécaniques des roulements à billes par analyse de grandeurs mécaniques et électriques dans des machines synchrones à aimants permanents haute vitesse. Le domaine applicatif de ce travail concerne l'aéronautique. Généralement, pour surveiller l'état des roulements à billes dans un actionneur électrique, des mesures vibratoires sont réalisées. Elles permettent, en exploitant le spectre du signal vibratoire, de mettre facilement en évidence la détérioration du roulement. Cette méthode de surveillance est cependant relativement couteuse en termes d'instrumentation et le placement d'un capteur vibratoire dans des équipements à fort degré d'intégration est parfois difficile. Nous proposons dans ce mémoire d'utiliser d'autres grandeurs physiques prélevées sur le système pour réaliser la surveillance de ces défauts. Il peut s'agir de grandeurs mécaniques (vitesse, position par exemple) et de grandeurs électriques (courant statorique, courant onduleur par exemple). L'utilisation de données déjà disponibles dans l'équipement pour les besoins de la commande permet ainsi de supprimer le système d'acquisition vibratoire. A partir d'enregistrements temporels de données réalisées au cours de campagnes d'essais, nous proposons des méthodologies de traitement du signal permettant d'extraire automatiquement des informations sensibles au défaut à surveiller. L'idée finale est de construire des indicateurs de l'état de santé des roulements permettant de prendre « juste à temps » des décisions fiables relatives à la maintenance ou à la sécurisation de l'équipement. Pour construire ces indicateurs, les signatures spécifiques aux défauts de roulements sont étudiées de manière théorique et expérimentale, pour l'ensemble des grandeurs prélevées. Leurs propriétés sont mises en évidence, permettant ainsi de définir les bandes fréquentielles les plus contributives au diagnostic. L'extraction de ces signatures est réalisée dans le domaine fréquentiel selon plusieurs méthodes. Deux types d'indicateurs automatiques différents sont proposés. Le premier est construit directement à partir du spectre d'amplitude des grandeurs par extraction de l'amplitude des harmoniques dans des bandes fréquentielles particulières. Le second intègre une dimension statistique dans l'analyse en exploitant le caractère aléatoire de certains harmoniques pour détecter la présence du défaut. Des critères de comparaison sont définis et utilisés pour étudier les performances des indicateurs proposés pour deux campagnes d'essais avec des roulements artificiellement dégradés, pour différentes vitesses de fonctionnement et pour différents paramètres de réglage des indicateurs. / This Ph.D. thesis deals with detection of mechanical bearings faults by analysis of mechanical and electrical signals in high speed permanent magnet synchronous machine. The application domain of this work concerns aeronautics. Generally, to monitor the ball bearings status in electrical actuator, the vibration measurements are used. They allow, by extracting the vibration spectrum, to easily detect the deterioration of the bearing. This monitoring method is relatively expensive in terms of instrumentation and placing a vibration sensor in equipment with a high integration degree can be difficult. We propose in this paper to use other physical quantities taken from the system to perform the monitoring of these defects. It may be mechanical quantities (for example speed, position) and electrical quantities (for example stator current, power inverter). From time recording of data carried out during test campaigns, we propose signal processing methodologies to automatically extract information sensitive to the monitored fault. The final idea is to construct indicators of bearings health and make decisions relating to maintenance or equipment security. To construct these indicators, specific bearing defects signatures are studied theoretically and experimentally, for all collected variables. The extraction of these signatures is carried out in frequency domain. Two different types of automatic indicators are proposed. The first is constructed directly from the amplitude spectrum by extraction of the harmonic amplitude of the spectrum in particular frequency bands. The second includes a statistical dimension analysis by exploiting the random nature of some harmonics to detect fault presence. Criteria of comparison are defined and used to study the proposed indicators performances for two trial campaigns with artificially degraded bearings, for different speed functioning and for different regulation of indicators parameters.

Les travaux de cette thèse portent sur la détection et le diagnostic des défauts naissants dans les systèmes d’ingénierie et industriels, par des approches statistiques non-paramétriques. Un défaut naissant est censé provoquer comme tout défaut un changement anormal dans les mesures des variables du système. Ce changement est imperceptible mais aussi imprévisible dû à l’important rapport signal-sur défaut, et le faible rapport défaut-sur-bruit caractérisant le défaut naissant. La détection et l’identification d’un changement général nécessite une approche globale qui prend en compte la totalité de la signature des défauts. Dans ce cadre, la divergence de Kullback-Leibler est proposée comme indicateur général de défauts, sensible aux petites variations anormales cachées dans les variations du bruit. Une approche d’analyse spectrale globale est également proposée pour le diagnostic de défauts ayant une signature fréquentielle. L’application de l’approche statistique globale est illustrée sur deux études différentes. La première concerne la détection et la caractérisation, par courants de Foucault, des fissures dans les structures conductrices. La deuxième application concerne le diagnostic des défauts de roulements dans les machines électriques tournantes. En outre, ce travail traite le problème d’estimation de l’amplitude des défauts naissants. Une analyse théorique menée dans le cadre d’une modélisation par analyse en composantes principales, conduit à un modèle analytique de la divergence ne dépendant que des paramètres du défaut. / This phD dissertation deals with the detection and diagnosis of incipient faults in engineering and industrial systems by non-parametric statistical approaches. An incipient fault is supposed to provoke an abnormal change in the measurements of the system variables. However, this change is imperceptible and also unpredictable due to the large signal-to-fault ratio and the low fault-to-noise ratio characterizing the incipient fault. The detection and identification of a global change require a ’global’ approach that takes into account the total faults signature. In this context, the Kullback-Leibler divergence is considered to be a ’global’ fault indicator, which is recommended sensitive to abnormal small variations hidden in noise. A ’global’ spectral analysis approach is also proposed for the diagnosis of faults with a frequency signature. The ’global’ statistical approach is proved on two application studies. The first one concerns the detection and characterization of minor cracks in conductive structures. The second application concerns the diagnosis of bearing faults in electrical rotating machines. In addition, the fault estimation problem is addressed in this work. A theoretical study is conducted to obtain an analytical model of the KL divergence, from which an estimate of the amplitude of the incipient fault is derived.

Les entraînements électriques à base de machine asynchrone sont largement utilisés dans les applications industrielles en raison de leur faible coût, de leurs performances et de leur robustesse. Cependant, des modes de fonctionnement dégradés peuvent apparaître durant la vie de la machine. L'une des raisons principales de ces défaillances reste les défauts de roulements à billes. Afin d'améliorer la sûreté de fonctionnement des entraînements, des schémas de surveillance peuvent être mis en place afin d'assurer une maintenance préventive. Ce travail de thèse traite de la détection et du diagnostic des défauts mécaniques et plus particulièrement des défauts de roulements dans une machine asynchrone. Généralement, une surveillance vibratoire peut être mise en place. Cette méthode de surveillance est cependant souvent chère du fait de la chaîne de mesure. Une approche, basée sur l'analyse et le traitement des courants statoriques, est alors proposée, afin de suppléer à l'analyse vibratoire. L'étude est basée sur l'existence et la caractérisation des effets des oscillations du couple de charge sur les courants d'alimentation. Un schéma de détection est alors introduit pour détecter différents types de défauts de roulements. De plus, des variables mécaniques, telles que la vitesse ou le couple, sont également reconstruites afin de fournir une indication sur la présence de défauts de roulements. Par ailleurs, un diagnostic des modulations des courants statoriques est proposé, en régime permanent et en régime transitoire, quel que soit le rapport entre les fréquences porteuse et modulante. Les méthodes étudiées sont la transformée de Hilbert, la transformée de Concordia, l'amplitude et la fréquence instantanées ainsi que la distribution de Wigner-Ville. / Asynchronous drives are widely used in many industrial applications because of their low cost, high performance and robustness. However, faulty operations may appear during the lifetime of the system. The most frequently encountered faults in asynchronous drives come from rolling bearings. To improve the availability and reliability of the drives, a condition monitoring may be implemented to favor the predictive maintenance. This Ph.D. thesis deals with detection and diagnosis of mechanical faults, particularly rolling bearings defects in induction motors. Traditionally, bearing monitoring is supervised using vibration analysis. Measuring such quantities is often expensive due to the measurement system. An other approach, based on stator current analysis, is then proposed. The characterization of load torque oscillation effects on stator currents is studied. A detection scheme is then proposed to detect several types of bearing faults. Moreover, mechanical variables, such as rotating speed or torque, are estimated in order to detect bearings defects. In addition, a diagnosis of stator currents modulations is proposed, in steady and transient state, whatever the career and modulation frequencies. Hilbert transform, Concordia transform, instantaneous amplitude and frequency are studied. The Wigner-Ville distribution is used in transient state.

Os motores de indução desempenham um importante papel na indústria, fato este que destaca a importância do correto diagnóstico e classificação de falhas ainda em fase inicial de sua evolução, possibilitando aumento na produtividade e, principalmente, eliminando graves danos aos processos e às máquinas. Assim, a proposta desta tese consiste em apresentar um multiclassificador inteligente para o diagnóstico de motor sem defeitos, falhas de curto-circuito nos enrolamentos do estator, falhas de rotor e falhas de rolamentos em motores de indução trifásicos acionados por diferentes modelos de inversores de frequência por meio da análise das amplitudes dos sinais de corrente de estator no domínio do tempo. Para avaliar a precisão de classificação frente aos diversos níveis de severidade das falhas, foram comparados os desempenhos de quatro técnicas distintas de aprendizado de máquina; a saber: (i) Rede Fuzzy Artmap, (ii) Rede Perceptron Multicamadas, (iii) Máquina de Vetores de Suporte e (iv) k-Vizinhos-Próximos. Resultados experimentais obtidos a partir de 13.574 ensaios experimentais são apresentados para validar o estudo considerando uma ampla faixa de frequências de operação, bem como regimes de conjugado de carga em 5 motores diferentes. / Induction motors play an important role in the industry, a fact that highlights the importance of correct diagnosis and classification of faults on these machines still in early stages of their evolution, allowing increase in productivity and mainly, eliminating major damage to the processes and machines. Thus, the purpose of this thesis is to present an intelligent multi-classifier for the diagnoses of healthy motor, short-circuit faults in the stator windings, rotor broken bars and bearing faults in induction motors operating with different models of frequency inverters by analyzing the amplitude of the stator current signal in the time domain. To assess the classification accuracy across the various levels of faults severity, the performances of four different learning machine techniques were compared; namely: (i) Fuzzy ARTMAP network, (ii) Multilayer Perceptron Network, (iii) Support Vector Machine and (iv) k-Nearest-Neighbor. Experimental results obtained from 13.574 experimental tests are presented to validate the study considering a wide range of operating frequencies and also load conditions using 5 different motors.