Detection of faults in electrical motors is very important for avoiding unpredicted failures of the machines.Early detection and diagnosis of faults that may occur are desirable to ensure that operational effectiveness of an induction motor can be improved. In this paper, faults detection and classification using motor current signature analysis (MCSA) are presented. A series of simulations using the models of three phase cage induction motor is performed in different fault conditions, such as static, dynamic and mixed eccentricity and broken rotor bars. Designed models were implemented with the help of finite element method to provide data that makes it possible to diagnose presence of any type of faults, as well as to analyze obtained and calculated results. Models were designed on the basis of characteristics and parameters of real motor. The results are illustrated in the form of graphs and tables that make visible illustration for effectiveness of the used diagnosis method.

In this paper the detection and classification of faults in induction motor using motor current signature analysis and monitoring of stray flux are presented. During the research motors with static, dynamic and mixed eccentricity were measured. The results were analyzed and compared with the data obtained from the simulated motor models. The behavior of sidebands of principal slot harmonics was examined. The results are presented in the form of graphs that illustrate the effectiveness and advantage of the method for diagnosis of the motor and detection of faults in it. Streszczenie. W artykule przedstawiono metodę detekcji I klasyfikacji defektów silników indukcyjnych na podstawie analizy prądu i strumienia rozprposzonego. Możliwe jest wykrywanie statycznych i dynamicznych ekcentryczności. Zmierzone parametry były porównywane z danymi otrzymanymi metoda symulacji. Detekcja i klasyfikacja defektów silnika indukcyjnego na podstawie analizy prądu i strumienia rozproszonego.

No abstract