Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Difference Histograms: A new tool for time series analysis applied to bearing fault diagnosis
Pattern Recognition Letters
Induction machine fault detection using support vector machine based classifier
WSEAS TRANSACTIONS on SYSTEMS
Optimal MLP neural network classifier for fault detection of three phase induction motor
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
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The paper deals with diagnosis problems of the induction motors in the case of rotor, stator and rolling bearing faults. Two kinds of neural networks (NN) were proposed for diagnostic purposes: multilayer perceptron networks and self organizing Kohonen networks. Neural networks were trained and tested using measurement data of stator current and mechanical vibration spectra. The efficiency of developed neural detectors was evaluated. Feedforward NN with very simple internal structure, used for the detection of all fault kinds, gave satisfactory results, which is very important in practical realization. Experiments with Kohonen networks indicated that they could be used for the initial classification of motor faults, as an introductory step before the proper neural detector based on multiplayer perceptron is used. The obtained results lead to a conclusion that neural detectors for rotor and stator faults as well as for rolling bearings and supply asymmetry faults can be developed based on measurement data acquired on-line in the drive system.