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EN
Induction motors (IMs) have a crucial and significant role in various industrial sectors. With the prolonged operation of IMs, faults tend to develop that can be classified into five major categories, i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and load torque fluctuations. If the faults go undetected, it may lead to catastrophic failure. Hence, the predictive-based condition monitoring technique has evolved as a real-time fault diagnosis that exploits the revolutionary idea of cyber-physical system (CPS). Furthermore, motor current signature analysis (MCSA) is a non-invasive fault diagnosis technique of a motor that can be used to investigate the presence of five fault types. However, the major constraint that industries face today is the on-field implementation of MCSA-based fault diagnosis involving CPS-based architecture, executed in an automated manner. Hence, the present article depicts algorithms that aim at real-time monitoring of IMs through a CPS framework. The proposed methodology is automated, does not involve any human intervention, and has been validated with real-time experiments, depicting its effectiveness and practicality.
EN
This paper presents mechanical fault detection in squirrel cage induction motors (SCIMs) by means of two recent techniques. More precisely, we have analyzed the rolling element bearing (REB) faults in SCIM. Rolling element bearing faults constitute a major problem among different faults which cause catastrophic damage to rotating machinery. Thus early detection of REB faults in SCIMs is of crucial importance. Vibration analysis is among the key concepts for mechanical vibrations of rotating electrical machines. Today, there is massive competition between researchers in the diagnosis field. They all have as their aim to replace the vibration analysis technique. Among them, stator current analysis has become one of the most important subjects in the fault detection field. Motor current signature analysis (MCSA) has become popular for detection and localization of numerous faults. It is generally based on fast Fourier transform (FFT) of the stator current signal. We have detailed the analysis by means of MCSA-FFT, which is based on the stator current spectrum. Another goal in this work is the use of the discrete wavelet transform (DWT) technique in order to detect REB faults. In addition, a new indicator based on the MCSA-DWT technique has been developed in this study. This new indicator has the advantage of expressing itself in the quantity and quality form. The acquisition data are presented and a comparative study is carried out between these recent techniques in order to ensure a final decision. The proposed subject is examined experimentally using a 3 kW squirrel cage induction motor test bed.
EN
The aim of the paper is to study the evolution of the additional frequency components in the stator phase currents of induction cage motors when they are simultaneously affected by electrical and mechanical faults. In particular effects of interaction of pulsating torque components caused by cage broken bars and low frequency mechanical torque oscillations are studied. It is shown by simulation that the overlapping of the frequencies of those torques may lead to an incorrect diagnosis of the motor cage damage by means of the Motor Current Signature Analysis what has been confirmed by experimental results.
PL
Praca prezentuje analizę zmian widma prądów fazowych stojana silnika klatkowego w przypadkach jednoczesnego występowania uszkodzenia mechanicznego w układzie napędowym oraz uszkodzenia klatki. W szczególności badane są przypadki interakcji składowej pulsacyjnej w momencie elektromagnetycznym wywołanej uszkodzenia pręta klatki oraz w momencie obciążenia gdy ta częstotliwość jest bliska lub równa składowej generowanej przez silnik z uszkodzoną klatką. W pracy wykazano, że gdy te częstotliwości się pokrywają diagnoza stanu klatki na podstawie charakterystycznych dla uszkodzenia klatki składowych prądów stojana może być błędna.
EN
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.
PL
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.
EN
Double frequency tests are used for evaluating stator windings analyzing the temperature. Likewise, signal injection on induction machines is a well-known technique on sensorless motor control fields to find out the rotor’s position. Motor Current Signature Analysis (MCSA) is the most widely used method for identifying faults in induction motors. MCSA focuses its efforts on the spectral analysis of stator current. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics respectively. In both cases, the problem of frequency accuracy is very tricky since the sideband harmonic is close to the fundamental harmonic. This paper proposes injecting an additional voltage into the machine under test at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.
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