Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Abstract

This paper describes an Artificial Neural Network (ANN) based fault diagnosis methodology for Induction Motors (IM) operating under the same conditions for various speeds and loads. In this study, ten different IM fault conditions are considered. We considered five mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, bowed rotor, rotor with broken bar), four electrical faults (phase unbalance fault with two levels of severity, stator winding fault with two levels of severity), and one healthy motor condition. The current and vibration signals were considered in this work as these signals are generally considered to be the most efficient for the detection of mechanical and electrical faults in IM when used simultaneously. A machine fault simulator was used for the generation of vibration and current signals from different fault conditions. An ANN model was developed in which raw time domain vibration (in three directions) as well as current (in three phases) data are used simultaneously as input and then the fault diagnosis (training and testing) is performed. In this work, the fault diagnosis was attempted when testing was done for the same operating conditions as training. The developed fault diagnosis methods were found to be robust for various operating conditions (speeds and loads) of the IM.