Artificial neural network based fault identification scheme implementation for a three-phase induction motor

Sri R Kolla; Shawn D Altman

doi:10.1016/j.isatra.2006.08.002

Artificial neural network based fault identification scheme implementation for a three-phase induction motor

ISA Trans. 2007 Apr;46(2):261-6. doi: 10.1016/j.isatra.2006.08.002. Epub 2007 Mar 2.

Authors

Sri R Kolla¹, Shawn D Altman

Affiliation

¹ Electronics and Computer Technology Program, Department of Technology Systems, Bowling Green State University, Bowling Green, OH 43403, USA. skolla@bgsu.edu

PMID: 17336306
DOI: 10.1016/j.isatra.2006.08.002

Abstract

This paper presents results from the implementation and testing of a PC based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three-phase voltages and currents from a 1/3 HP squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure using the JavaNNS program. The trained network is placed in a LabVIEW based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor off. The complete system is successfully tested in real time by creating different faults on the motor.