Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines
Introduction. 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 aim of this research study is to design a neural network model...
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National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
2023
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eiekhpieduua-article-2746872023-04-23T20:54:24Z Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines Sakhara, S. Brahimi, M. Nacib, L. Layadi, T. M. дискретне вейвлет-перетворення асинхронна машина трифазна модель багатошарова персептронна нейронна мережа discrete wavelet transform induction machine three-phase model multilayer perceptron neural network Introduction. 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 aim 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. Method. 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. Obtained results 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. Вступ. Нині діагностика несправностей асинхронних машин відіграє значну роль у промисловості. У цій статті запропоновано модель штучної нейронної мережі для автоматичної діагностики несправностей асинхронної машини. Метою цього дослідження є розробка моделі нейронної мережі, що дозволяє генерувати велику базу даних. Ця база може охоплювати максимально можливі несправності статора. Несправності, розглянуті у цьому дослідженні, враховують коротке замикання при великих коливаннях навантаження машини. Крім того, мета полягає в тому, щоб автоматизувати алгоритм діагностики за допомогою класифікатора штучної нейронної мережі. Метод. База даних, що використовується для штучної нейронної мережі, заснована на показниках, отриманих в результаті вейвлет-аналізу струму статора машини однієї фази. Розроблена нейронна модель дозволяє враховувати дисбаланси, що виникають при коротких замиканнях у статорі машини. Реалізована математична модель в експертній системі ґрунтується на трифазній моделі. Математичні параметри, що враховуються в цій моделі, розраховуються онлайн. Характеристичний вектор моделі штучної нейронної мережі формується шляхом розкладання сигналу струму статора з використанням вейвлет-дискретного методу. Отримані результати показують, що дана методика дозволяє забезпечити більше виявлення з чіткою оцінкою числа витків при короткому замиканні. Також розроблена експертна система для конфігурацій, що приймаються, відрізняється високою точністю. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2023-04-23 Article Article application/pdf http://eie.khpi.edu.ua/article/view/274687 10.20998/2074-272X.2023.3.03 Electrical Engineering & Electromechanics; No. 3 (2023); 21-27 Электротехника и Электромеханика; № 3 (2023); 21-27 Електротехніка і Електромеханіка; № 3 (2023); 21-27 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/274687/272458 Copyright (c) 2023 S. Sakhara, M. Brahimi, L. Nacib, T. M. Layadi http://creativecommons.org/licenses/by-nc/4.0 |
| institution |
Electrical Engineering & Electromechanics |
| baseUrl_str |
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| datestamp_date |
2023-04-23T20:54:24Z |
| collection |
OJS |
| language |
English |
| topic |
discrete wavelet transform induction machine three-phase model multilayer perceptron neural network |
| spellingShingle |
discrete wavelet transform induction machine three-phase model multilayer perceptron neural network Sakhara, S. Brahimi, M. Nacib, L. Layadi, T. M. Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| topic_facet |
дискретне вейвлет-перетворення асинхронна машина трифазна модель багатошарова персептронна нейронна мережа discrete wavelet transform induction machine three-phase model multilayer perceptron neural network |
| format |
Article |
| author |
Sakhara, S. Brahimi, M. Nacib, L. Layadi, T. M. |
| author_facet |
Sakhara, S. Brahimi, M. Nacib, L. Layadi, T. M. |
| author_sort |
Sakhara, S. |
| title |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_short |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_full |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_fullStr |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_full_unstemmed |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_sort |
application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| title_alt |
Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines |
| description |
Introduction. 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 aim 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. Method. 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. Obtained results 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. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine |
| publishDate |
2023 |
| url |
http://eie.khpi.edu.ua/article/view/274687 |
| work_keys_str_mv |
AT sakharas applicationofawaveletneuralnetworkapproachtodetectstatorwindingshortcircuitsinasynchronousmachines AT brahimim applicationofawaveletneuralnetworkapproachtodetectstatorwindingshortcircuitsinasynchronousmachines AT nacibl applicationofawaveletneuralnetworkapproachtodetectstatorwindingshortcircuitsinasynchronousmachines AT layaditm applicationofawaveletneuralnetworkapproachtodetectstatorwindingshortcircuitsinasynchronousmachines |
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2025-07-17T11:49:38Z |
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2025-07-17T11:49:38Z |
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