Abstract
Automatic continuous or periodic control is the most promising method of diagnosing electric motors in productionnowadays. Itis aimed at predicting breakdowns and the remaining useful lifetimeof motors. However, research in this arearemains purely theoretical and eitherfocuses on very narrow problemsor providestoo superficial overview.Consequently,manual control devices orpartially automated devices are mainlyusedin practice[1]. In view of this, there is great interest in the development of a software method for predictive maintenance of electric motors. In this research recurrent neural networks with long short-term memory layers (LSTM-layers)are investigated due to their abilityto effectively model sequential data and learn complex dependencies [2].
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