Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor

Abstract

The objective of this paper is to apply deep learning network running on an embedded system platform to diagnose faults of a three-phase electric motor by a non-contact method based on operating motor noise. To accomplish this, at first, deep learning network should be designed and trained on a computer, and then converted to an equivalent network to run on the embedded system. The network input data is a two-dimension spectrogram image of the noise emitted by the motor in four main cases, including normal operation, phase shift, phase loss and bearing failure. The execution time and accuracy of these deep learning network structures will be deployed on three microcontrollers including ESP32, ESP32-C3 and nRF52840 to determine the suitable embedded platform and network structure for real-time running. Experimental results show that the proposed deep learning network models could diagnose the faults well on both computer and embedded platform with the highest accuracies are 99,7% and 99,3%, respectively. In particular, the preliminary results are remarkable with the recognition time and accuracy at 1,7 seconds and 72%, respectively associated with the proposed deep learning network on realtime embedded system performance.