Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

Abstract

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.