Original title: Inter Turn Short-Circuit Detection In Vector Controlled Pms Motor Using Ai
Authors: Zezula, Lukáš
Document type: Papers
Language: cze
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This paper deals with the diagnostics of inter turn faults in a vector controlled synchronous motor with permanent magnets. Inter turn faults are detected by a convolution neural network from adequately preprocessed current signals of the stator phases. The goal is to create a model within which different severity of inter turn faults will be simulated. Data from the simulations are preprocessed and transformed using Wavelet transform and the resulting scalograms are fed to a pre-trained convolution neural network GoogLeNet. This neural network’s diagnostic capabilities are tested on a physical drive, capable of emulating faults.
Keywords: Convolutional neural network; Inter turn fault; Inter turn short-circuit; ITF; Motor fault diagnostics; PMSM; Vector control
Host item entry: Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers, ISBN 978-80-214-5867-3

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200521

Permalink: http://www.nusl.cz/ntk/nusl-447573


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