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

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

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-05-22T15:08:58Z No. of bitstreams: 1 arquivo total.pdf: 10123779 bytes, checksum: a9bc3c38b693c977dc116bf2cde4af83 (MD5) / Made available in DSpace on 2017-05-22T15:08:58Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 10123779 bytes, checksum: a9bc3c38b693c977dc116bf2cde4af83 (MD5) Previous issue date: 2015-08-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Electrical motors are responsible for 95% of primary movement source in industrialized nations. Among those, 90% are Three-Phase Induction Motors, present in almost all industrial sectors. Due to its importance on this sector, there is a need for monitoring them in order to avoid production stops and operational disasters. In this work, studies were conducted on common fault diagnostics in Three-Phase Induction Motors intending industrial applications. Different sensoring techniques were used and their performance were analyzed. An embedded system was developed to make field applications with different techniques possible. This installation can be done noninvasively and data collection can be obtained in real time. Fast Fourier Transform (FFT) and wavelet processing techniques are used as tools in mathematical processing of the data. In the first moment, fault analyses were conducted offline making use of data acquisition devices and further processing of the information. In a second phase, the embedded system was used to monitor automatically (online) the evolution of the damage developed. The system receives the motor current signal, and using local processing, conducts the spectral analysis of the signal identifying incipient faults. These data are available for communication with or without wires. For the embedded system implementation, the algorithms were adjusted to comply with the embedded hardware resources restrictions. Through theoretical and experimental development, several techniques were used and compared with the objective of performing a full diagnostic TIM malfunction. The experimental results corroborate the theoretical ones, and it was conducted a detailed study of the methods on the state of the art and new approaches were made. / industrializadas. Desses, 90% são motores de indução trifásicos (MIT), estando presentes em praticamente todos os setores industriais. Devido a sua importância no setor produtivo, existe a necessidade que os mesmos sejam devidamente monitorados evitando interrupções na produção e desastres operacionais. Nesse trabalho foram realizados estudos para diagnósticos de falhas comuns em motores de indução trifásicos visando aplicação industrial. As diferentes técnicas de sensoriamento foram utilizadas e o desempenho de cada método foi analisado. Um sistema embarcado foi desenvolvido com o intuito de se viabilizar as aplicações em campo, cuja instalação pode ser realizada de forma não invasiva e as informações podem ser obtidas em tempo real. A Transformadas Rápida de Fourier (FFT) e as técnicas de processamento wavelet serão utilizadas como ferramentas matemáticas no tratamento dos dados. Em um primeiro momento, as análises das falhas foram feitas de forma off-line, fazendo uso de placas de aquisição de dados e um posterior tratamento das informações. Em um segundo momento, foi desenvolvido o sistema embarcado que faz um monitoramento automático (online) da evolução da avaria, que recebe o sinal de corrente do motor e, utilizando processamento local, faz a análise espectral do sinal identificando falhas incipientes. Esses dados ficam disponíveis (off line e on line) por comunicação com ou sem fios. Para a implementação do sistema embarcado, os algoritmos foram ajustados de modo a respeitar as restrições de recursos do hardware embarcado. Através de desenvolvimento teórico e experimental, várias técnicas foram utilizadas e comparadas com o objetivo de se realizar um diagnóstico completo de avaria de MIT. Os resultados experimentais corroboram com os teóricos e foi realizado um estudo aprofundado dos métodos do estado da arte e novas abordagens foram realizadas.

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.