Real-time safety monitoring in the induction motor using deep hierarchic long short-term memory

The safety of industrial installations requires real-time monitoring of the occurrence of defects in induction machines that are widely used in this field. The implementation of this type of system typically needs to process a large amount of data provided by sensors and thus necessitates high computing mass, which complicates sensor utilization in real time. In this paper, we propose a hierarchical recurrent neural network by stacking two long short-term memory layers to form a single end-to-end network. Trained to establish complex temporal relations in raw time series signals. Those signals are directly provided by the sensors without any preprocessing or hand engineered features extraction. To train the network, we use the stator currents of a three-phase induction motor captured in a steady state. The currents represent several operation modes, which comprise the healthy and failed states with several types of mechanical defects, electrical defects, and combinations thereof. The experimental results were obtained using data from a real test bed to demonstrate the robustness and speed of the proposed approach for real-time monitoring of the operating status of an induction motor.