Abnormal Health Monitoring and Assessment of a Three‐Phase Induction Motor Using a Supervised CNN‐RNN‐Based Machine Learning Algorithm

Abstract

This paper shows the health monitoring and assessment of a three-phase induction motor in abnormal conditions using a machine learning algorithm. The convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are the prominent methods used in machine learning algorithms, and the combined method is known as the CRNN method. The abnormal conditions of a three phase-induction motor are represented by three-phase faults, line-to-ground faults, etc. The pattern of fault current is traced, and key features are extracted by the CRNN algorithm. The performance parameters like THD (%), accuracy, and reliability of abnormal conditions are measured with the CRNN algorithm. The assessment of abnormal conditions is being realized at the terminals of a three-phase induction motor. A fuzzy logic controller (FLC) is also used to assess such abnormalities. It is observed that performance parameters are found to be better with the CRNN method in comparison to CNN, RNN, ANN, and other methods. Such a realization makes the system more compatible with abnormality recognition.