Title: Fault detection and isolation of asynchronous machine based on the probabilistic neural network

Authors: Rahma Ouhibi; Salma Bouslama; Kaouther Laabidi

Addresses: National Engineering School of Tunis, LR11ES20 laboratoiry Analyse, Conception et Commande des Systemes, BP 37, le Belvedere 1002 Tunis, Tunisia ' National Engineering School of Tunis, LR11ES20 laboratoiry Analyse, Conception et Commande des Systemes, BP 37, le Belvedere 1002 Tunis, Tunisia ' National Engineering School of Tunis, LR11ES20 laboratoiry Analyse, Conception et Commande des Systemes, BP 37, le Belvedere 1002 Tunis, Tunisia

Abstract: In this paper, we propose three neural networks based methods for fault detection and isolation of asynchronous machine: a probabilistic neural network (PNN), multi-layer perceptron (MLP), and generalised regression neural network (GRNN). To perform efficient diagnostic results the cross-validation procedure input data is partitioned into three sets: a training set, a validation set and a test set. The stator RMS values of three-phase voltages and currents are used as model inputs to identify the different types of faults and the normal operating mode. Efficiency of these three neural based methods is compared using a test set of 100 data.

Keywords: asynchronous machine; fault detection and isolation; FDI; artificial intelligence; probabilistic neural network; PNN; multi layer perceptron; MLP; generalised regression neural network; GRNN.

DOI: 10.1504/IJIEI.2018.091879

International Journal of Intelligent Engineering Informatics, 2018 Vol.6 No.3/4, pp.378 - 395

Received: 03 Apr 2017
Accepted: 20 Jun 2017
Published online: 20 May 2018 *

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