Inter-turn short circuit fault detection in induction motor using XGBoost, KNN and Random Forest

• Autorzy: Chahmi Abdelghani , Benkaihoul Said , Naas Toufik Tayeb , Yildirim Özüpak

Identyfikatory

DOI

10.15199/48.2025.02.55

Warianty tytułu

PL

Wykrywanie zwarć międzyzwojowych w silniku indukcyjnym przy użyciu XGBoost, KNN i Random Forest

• Języki publikacji: EN

Abstrakty

EN

In this research endeavor, the focus was directed towards investigating a specific fault occurrence within an induction motor, namely an inter-turn short circuit (ITSC), intentionally induced within phase A of the motor. The employed dataset encompassed both correct operational states and instances afflicted with the aforementioned fault, with parameters such as current flows and torque outputs meticulously recorded and analyzed. When employing a methodology rooted in machine learning, a suite of algorithms was applied to discern and identify the presence of the fault. From among the array of algorithms utilized, the notable contenders included Random Forest (RF), k-nearest neighbors (KNN), and Extreme Gradient Boosting (XGBoost), each meticulously trained and tested on the dataset to gauge their efficacy in fault detection. The outcomes obtained in the mentioned study unequivocally demonstrate the superiority of the Random Forest algorithm in terms of accuracy assessment, boasting a remarkable accuracy rate of 99.7%. In the stark contrast, both KNN and XGBoost algorithms exhibited comparatively lower accuracy rates, standing at 96.6% and 96.5%, respectively.

PL

W tym przedsięwzięciu badawczym skupiono się na badaniu konkretnego wystąpienia usterki w silniku indukcyjnym, a mianowicie zwarcia międzyzwojowego (ITSC), celowo indukowanego w fazie A silnika. Zastosowany zbiór danych obejmował zarówno prawidłowe stany operacyjne, jak i przypadki dotknięte wyżej wymienionymi usterkami, przy czym parametry takie jak przepływy prądu i wyjściowy moment obrotowy były skrupulatnie rejestrowane i analizowane. Stosując metodologię opartą na uczeniu maszynowym, zastosowano zestaw algorytmów w celu rozpoznania i zidentyfikowania obecności usterki. Wśród szeregu wykorzystywanych algorytmów godnymi uwagi konkurentami byli Random Forest (RF), k-najbliżsi sąsiedzi (KNN) i Extreme Gradient Boosting (XGBoost), każdy skrupulatnie przeszkolony i przetestowany na zbiorze danych w celu oceny ich skuteczności w wykrywaniu usterek. Wyniki uzyskane w tym badaniu jednoznacznie wskazują na wyższość algorytmu Random Forest pod względem oceny dokładności, który może pochwalić się niezwykłym współczynnikiem dokładności wynoszącym 99,7%. Dla kontrastu, zarówno algorytmy KNN, jak i XGBoost wykazywały stosunkowo niższe wskaźniki dokładności, wynoszące odpowiednio 96,6% i 96,5%.

Słowa kluczowe

EN

fault detection; machine learning; accuracy; ITSC fault

PL

wykrywanie błędów; uczenie maszynowe; dokładność; błąd ITSC

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2025
• Tom: R. 101, nr 2
• Strony: 248--253
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Chahmi Abdelghani

• Electrical Drives Laboratory, University of Sciences and Technology, Oran, Algeria

• https://orcid.org/0000-0002-6294-4707

autor

Benkaihoul Said

• Laboratoy Applied Automation and Industrial Diagnostics at the Department of Electrical Engineering in the Faculty of Science and Technology. University of Djelfa

autor

Naas Toufik Tayeb

• Energy Systems Applications Laboratory (LASER), Faculty of Science and Technology, University of Djelfa, Algeria

autor

Yildirim Özüpak

• Department of Electrical Energy, Silvan Vocational School, Dicle University

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