Neuro-detector Based on Coherence Analysis for Stator Insulation in Electric Motors

Abstract

This research describes the monitoring of the fundamental spectral features of stator insulation damage through accelerated aging studies for induction motors with a power rating of 5 HP. In order to accomplish this goal, even-harmonic values of the line frequency defined between the 4th and the 16th harmonics, which are computed by the coherence approach between the stator currents and vibration signals, are determined as indicators of stator insulation damage. After this determination, a neuro-detector based on the auto-associative neural structure is trained in the frequency domain. This uses coherence variations and even-harmonic values as indicators of the insulation damage of an induction motor by observing the changes in the errors (residuals) generated by the neural net.

Keywords:

• induction motors
• aging process
• coherence
• even harmonics
• stator insulation fault
• neural network

Acknowledgments

The authors would like to thank the Maintenance and Reliability Center and Nuclear Engineering Department at The University of Tennessee, Knoxville, for permission of the experimental data. Also, thanks to Dr. A. S. Erbay for providing technical help during this research.