Pruned Ann Model for Three Phase Induction Motor Operating Conditions Classification

https://doi.org/10.24237/djes.2008.01103

Authors

  • Ali Khudhair Mutlag Department of Electronics Engineering, College of Engineering, University of Diyala

Keywords:

Neural Networks, Optimal Brain Surgeon, Induction Motor

Abstract

The Artificial Neural Networks (ANN) have become a very useful tool in a wide range of engineering applications. In this paper, feed forward neural network Optimal Brain Surgeon (OBS) pruned model is proposed to classify the operating conditions of three phase induction motor. The proposed model had been trained with error back propagation training algorithm using frequency and voltage as input signals with nine possible operating conditions (faulty and healthy) acting as output neurons of the neural network model. The simulation results show that the pruned ANN model can perform perfect classification for the operating conditions of three phase induction motor.

Downloads

Download data is not yet available.

References

. Pöyhonen, P. Jover, and H. Hyötyniemi, "Independent Component Analysis of Vibrations for Fault Diagnosis of an Induction Motor", Proceedings of the IASTED International Conference CIRCUITS, SIGNALS, AND SYSTEMS May 19-21, 2003, Cancun, Mexico.

A. Siddique, G. Yadava and B. Sin, "Applications of Artificial Intelligence Techniques for Induction Machine Stator Fault Diagnostics: Review", Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003

N.A.Rahim, M.N.Taib, A.H.Adom, and M.Y.Mashor, " The NARMAX Model for a DC Motor Using MLP Neural Network ",

http://publicweb.unimap.edu.my/~norasmadi/conference/ICoMMS2006.pdf.

S.M.Ali and M.A.Salman, "Variable Voltage and Variable Frequency Fault Detection For Three Phase Induction Motors Using Artificial Neural Networks", Diyala Journal For Applied Researches, Vol. 3, No. 2, 2007.

S.Haykin, " Feedforward Neural Networks: An Introduction ",

http://media.wiley.com/product_data/excerpt/19/04713491/0471349119.pdf.

C.F.Wong, J.shippen, and B.Jones, " Neural Networks Control Strategies for Low Specification Servo Actuators ", Inter. Journal of Machine Tools & Manufacture, Vol. 38, pp. 1109-1124,1998.

H.B.Demuth and M.Beala, “ Neural Network Toolboxa: User’s Guide”, Ver. 3, The Mathworks, Inc., Natik, MA, 1998.

J.C.Principe, N.R.Euliano and W.C.Lefebvre, “ Neural and Adaptive Systems : fundamentals Through Simulations ”, JOHN WILEY & SONS, INC., 2000.

M.Norgaard, O.Ravn, N.K.Poulsen, and L.K.Hansen, "Neural Network for Modelling and Control of Dynamic Systems", Springer-Verlag, London, 2000.

M.Hintz-Madsen, L.K.Hansen, J.Larsen, M.W.Pedersen and M.Larsen, “ Neural Classifier Construction Using Regularization, Pruning and Test Error Estimation”, Neural Networks, Vol. 11, pp.1659-1670, 1998.

M.Norgaard, "Neural Network Based System Identification Toolbox", Technical Report, Dept. of Automation, Technical University of Denmark, 2000.

Published

2008-09-01

How to Cite

[1]
Ali Khudhair Mutlag, “Pruned Ann Model for Three Phase Induction Motor Operating Conditions Classification”, DJES, vol. 1, no. 1, pp. 33–44, Sep. 2008.