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This paper addresses the problem of detecting voltage dips in Wind Turbine Generator connected to electrical grid. A procedure based on analysis of voltage indicators is proposed. It used the artificial neural network in order to extract the features (magnitudes and angle of each phase). The method is tested in simulation and the results approved its efficiency and rapidity. It could not only detect...
In this paper, an automatic system of diagnosis was developed to detect and locate in real time the defects of the wound rotor asynchronous machine associated to electronic converter. For this purpose, we have treated the signals of the measured parameters (current and speed) to use them firstly, as indicating variables of the machine defects under study and, secondly, as inputs to the Artificial...
In this paper, the artificial neural network (ANN) inputs selection for detecting and quantifying the progressive value of an incipient defect in gears is carried out by experimental design evaluation. Several parameters in time-domain (root mean squared, crest factor, energy ratio, FM0, Kurtosis, FM4, NA4, M6A, NB4) and multiscale Hilbert-wavelet transformations are evaluated as a possible inputs...
In this work the strategy of the artificial intelligence (neural networks) is used to detect and localize the defects of the double stator asynchronous machine. In fact, several neural networks have been applied to the detection of defects. Then, we used a selector which allows activating only one network at a time. In this case, the selected network detects only defects corresponding to the torque...
The aim of this paper is to propose a method for the detection of faults in industrial systems, such as electrical machines and drives, through on-line monitoring system. Early fault detection, which reduces the possibility of catastrophic damage, is possible by comparing the measured signals with a database that contains characteristic signals for machines operating with and without faulty conditions...
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