Electric motor faults diagnosis using artificial neural networks

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This paper presents electric motor fault diagnosis using two kinds of Artificial Neural Networks (ANN): feedforward networks and self organising maps (SOM). Major faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using measurement data from stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that the feedforward ANN with a very simple internal structure can give satisfactory results, while SOMs can classify the type of motor faults during steady state working conditions. The experiment results also show that the feedforward ANN is the more promising scheme in this case where fault data from electric motors is available.

Document Type: Research Article

DOI: http://dx.doi.org/10.1784/insi.46.10.616.45210

Affiliations: The Department of Mechanical and Materials Engineering, Queens' University, Kingston, Ontario, Canada, K7L 3N6.

Publication date: October 1, 2004

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