2019
DOI: 10.35940/ijitee.i7591.078919
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Induction Motor Fault Classification using Pattern Recognition Neural Network

Abstract: The industrial growth has escalated the use of induction motors as prime movers in modern industry. This is due to its low cost, simple construction and ruggedness. Although rugged, these may fail earlier than expected life due to, excessive mechanical, electrical and environmental stresses. Automatic Artificial Intelligence (AI)-based systems are nowadays widely employed in the domain of induction motor fault identification with high success rate. Artificial neural network are utilized extensively for the det… Show more

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Cited by 1 publication
(2 citation statements)
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“…This can be obtained by the use of artificial intelligence methods and techniques, which are nowadays also popular in the diagnostics of IM drives [16,17]. Different structures of neural networks (NNs) have been used in the detection of electric motor faults, e.g., multi-layer perceptron (MLP) [5,8,10,[18][19][20][21], general regression neural network [21,22], self-organizing networks [23,24], adaptive neuro-fuzzy inference system (neuro-fuzzy inference) [25], and the following deep neural network (DNN) models: deep Boltzmann machines, deep belief networks and stacked auto-encoders [26], convolutional neural network [27][28][29][30] and one-dimensional convolutional neural networks (CNN) [3,31].…”
Section: Introductionmentioning
confidence: 99%
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“…This can be obtained by the use of artificial intelligence methods and techniques, which are nowadays also popular in the diagnostics of IM drives [16,17]. Different structures of neural networks (NNs) have been used in the detection of electric motor faults, e.g., multi-layer perceptron (MLP) [5,8,10,[18][19][20][21], general regression neural network [21,22], self-organizing networks [23,24], adaptive neuro-fuzzy inference system (neuro-fuzzy inference) [25], and the following deep neural network (DNN) models: deep Boltzmann machines, deep belief networks and stacked auto-encoders [26], convolutional neural network [27][28][29][30] and one-dimensional convolutional neural networks (CNN) [3,31].…”
Section: Introductionmentioning
confidence: 99%
“…In the paper [20], MLP-based neural detectors whose task was to identify seven technical conditions of an IM, including an undamaged motor with a damaged bearing, a damaged rotor bar and a different number of shorted stator turns, were presented. The NNs contained seven neurons in the output layer.…”
Section: Introductionmentioning
confidence: 99%