Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor
Makarand S. Ballal
Zafar J. Khan
Hiralal M. Suryawanshi
Ram L. Sonolikar
Nagpur Univ.;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 54,
Issue: 1
On page(s): 250-258
ISSN: 0278-0046
INSPEC Accession Number: 9299048
Digital Object Identifier: 10.1109/TIE.2006.888789
Current Version Published: 2007-02-05
Abstract
The positive features of neural networks and fuzzy logic are combined together for the detection of stator inter-turn insulation and bearing wear faults in single-phase induction motor. The adaptive neural fuzzy inference systems (ANFISs) are developed for the detection of these two faults. These faults are created experimentally on a single-phase induction motor in the laboratory. The experimental data is generated for the five measurable parameters, viz, motor intakes current, speed, winding temperature, bearing temperature, and the noise of the machine. Earlier, the ANFIS fault detectors are trained for the two input parameters, i.e., speed and current, and the performance is tested. Later, the three remaining parameters are added and the five input ANFIS fault detector is trained and tested. It observed from the simulation results that the five input parameter system predicts more accurate results
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