Title :
Induction motor faults diagnostic via artificial neural network (ANN)
Author :
Di Stefano, R. ; Meo, S. ; Scarano, M.
Author_Institution :
Dipartimento di Ingegneria Ind., Cassino Univ., Italy
Abstract :
The paper deals with the analysis of an artificial neural network (ANN) approach suitable for online faults detection of induction machines. The aim of this paper is to develop an alternative with respect to traditional fault detector techniques that overcomes the limitations of present technology. After an analytical discussion about theoretical principles and relations used for the diagnosis of electrical machines failures, a simple ANN algorithm is presented and tested. The results prove the feasibility of to tool arranged by means of artificial neural network for a fast and accurate detection of stator faults in induction machines. Furthermore a good accuracy of the results have been achieved notwithstanding the great simplicity of the algorithm with respect to a complete model arranged by a space vector analysis
Keywords :
electric machine analysis computing; fault location; induction motors; machine theory; neural nets; power engineering computing; stators; accuracy; algorithms; artificial neural networks; faults diagnosis; induction motors; model; space vector analysis; stator faults; Algorithm design and analysis; Artificial neural networks; Circuit faults; Current measurement; Electrical fault detection; Induction machines; Induction motors; Mathematical model; Stators; Vibration measurement;
Conference_Titel :
Industrial Electronics, 1994. Symposium Proceedings, ISIE '94., 1994 IEEE International Symposium on
Conference_Location :
Santiago
Print_ISBN :
0-7803-1961-3
DOI :
10.1109/ISIE.1994.333115