Submitted by RODOLFO DE SOUSA SANTOS null (rodolfosousa4@gmail.com) on 2017-08-24T18:31:09Z No. of bitstreams: 1 TESE _RODOLFO_CORRIGIDA_19_08_2017_Final.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-08-25T16:18:27Z (GMT) No. of bitstreams: 1 santos_rs_dr_guara.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) / Made available in DSpace on 2017-08-25T16:18:27Z (GMT). No. of bitstreams: 1 santos_rs_dr_guara.pdf: 4285264 bytes, checksum: b5dac391b40121a31b55502fba5c1c43 (MD5) Previous issue date: 2017-07-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os sinais de vibrações de máquinas rotativas conduzem a informações dinâmicas da máquina e esta análise é de grande importância no que diz respeito ao monitoramento de condição e diagnósticos de máquinas. Vários métodos de análises têm sido empregados no sentido de diagnosticar falhas em componentes de máquinas tais como engrenagens, rolamentos, dentre outros. Este trabalho apresenta uma análise sobre detecção de falhas em rolamentos de máquinas rotativas, e para esta apreciação utilizou-se os bancos de dados da CASE WESTERN RESERV UNIVERSITY e o banco de dados da FEG/UNESP. O objetivo principal deste trabalho foi a implementação de técnicas avançadas para identificar e caracterizar as falhas que são geradas em rolamentos, vislumbrando o aprimoramento da manutenção baseada na condição. Inicialmente, realizou-se a implementação e simulação no banco de dados da (CWRU), utilizando o software MATLAB e por meio da técnica de ressonância de alta frequência (HFRT), obteve-se resultados satisfatórios, entretanto esta metodologia é limitada uma vez que ela é empregada apenas para regime estacionário. A implementação da técnica HFRT não identificou em alguns casos a frequências para caracterização dos defeitos nas pistas dos rolamentos. Em seguida, utilizou-se a técnica Short Time Fourier Transform-STFT. A implementação proporcionou uma análise bem mais sensível aos impactos gerados nas pistas, pois, com a utilização da STFT, foi possível identificar as frequências características de defeitos. Para efeito de comparação optou-se por utilizar a técnica Wavelet combinada com a técnica do envelope. Esta análise foi aplicada usando a Wavelet Daubechies de ordem 4 (db4), em cuja implementação, realizou-se a decomposição do sinal de um rolamento com defeito e verificou-se qual destes apresentou o maior nível RMS e selecionou-se este sinal, pois o mesmo é o nível ideal para aplicação do método. Realizou-se a mesma apreciação ao banco de dados da FEG/UNESP. A análise realizada da técnica de Wavelet combinada com a técnica HFRT foi a que demonstrou melhor capacidade em relação às técnicas HFRT e STFT. Em seguida realizou-se a implementação da técnica de curtose espectral associada à técnica do envelope foi a que proporcionou os resultados mais precisos e satisfatórios, pois com a aplicação dessa metodologia foi possível a determinação de forma automática da região de ressonância e consequentemente uma melhora na caracterização das frequências de defeitos observadas nos rolamentos dos experimentos realizados em máquinas rotativas. / The vibration signals from rotating machines provide a set of dynamic information, which are very important for continuous condition monitoring of machinery. Several analytical methods have been employed in order to diagnose faults in machines components such as gears, bearings and others. This paper presents a fault detection analysis of rotating machinery bearings, using data from CASE WESTERN UNIVERSITY RESERVOIR and the FEG / UNESP database. The main objective of this work is the implementation of advanced techniques to identify and characterize bearing failures, with the purpose to improve maintenance under working conditions. At first, the implementation and simulation were done with data extracted from the database of (CWRU) using MATLAB software and high-frequency resonance technique (HFRT), which led to satisfactory results. However, this technique is limited since it is used only in a stationary regime. In some cases, the implementation of HFRT technique was not able to identify the defect frequencies of the bearing’s races. Next the STFT Short-Time Fourier Transform technique was used. Its implementation provided a much more sensitive analysis of the impacts on the slopes; using STFT allowed to identify the characteristic defect frequencies. For comparison purposes, the wavelet technique combined with the envelope technique were used. This analysis was applied using Daubechies Wavelet of order 4 (DB4). In its implementation, a defective bearing signal was decomposed into various parts. The signal part with the highest RMS level was selected, because it provides best conditions for applying the method. Analogously, data from the FEG / UNESP database were treated. The Wavelet analysis technique combined with HFRT technique demonstrated better capability with respect to the HFRT and STFT techniques. The implementation of the spectral kurtosis technique associated with the envelope technique provided the most accurate and satisfactory results, since with the application of this methodology it was possible to determine the resonance region automatically. Consequently, this is an improvement regarding the characterization of the defect frequencies of the bearings observed in experiments with rotating machinery.

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.