@article{Sakhara_Brahimi_Nacib_Layadi_2023, title={Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines}, url={http://eie.khpi.edu.ua/article/view/274687}, DOI={10.20998/2074-272X.2023.3.03}, abstractNote={<p><strong><em>Introduction.</em></strong><em> Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The <strong>aim</strong> of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. <strong>Method</strong>. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. <strong>Obtained results</strong> show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision. </em></p>}, number={3}, journal={Electrical Engineering & Electromechanics}, author={Sakhara, S. and Brahimi, M. and Nacib, L. and Layadi, T. M.}, year={2023}, month={Apr.}, pages={21–27} }