Artificial neural networks broken rotor bars induction motor fault detection

Paper deals with application of online rotor broken bar fault detection via artificial neural networks. Fault can be detected by monitoring abnormalities of the spectrum amplitudes at certain frequencies in the motor current spectrum. These discriminative features are used for training of feed-forward backpropagation artificial neural network. Trained network is capable to successfully classify induction motor rotor condition. Results are presented in tables and figures.

I. Introduction

It is well known that induction motors dominate the field of electromechanical energy conversion. These machines find a wide role in most industries in particular in the electric utility industry as auxiliary drives in central power plants of power systems, as well as a restricted role in low MVA power supply systems as induction generators, mining industries, petrochemical industries, as well as in aerospace and military equipment. Therefore, assessments of the running conditions and reliability of these drive systems is crucial to avoid unexpected and catastrophic failures. Consequently, the issue of preventive maintenance and noninvasive diagnosis of the condition of these induction motors drives is of great concern, and is becoming increasingly important, [1].