Broken magnets fault detection in PMSM using a convolutional neural network and SVM
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Galileo Institute of Technology and Education of the Amazon (ITEGAM)
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Permanent Magnet Synchronous Motor (PMSM) stands as a pivotal component in various applications, yet it remains susceptible to an array of faults within both its rotor and stator, there arises an imperative to swiftly and intelligently address these issues. In this study, a novel approach was undertaken wherein a PMSM design was conceptualized within the Ansys Maxwell program, followed by the deliberate introduction of a fault at the rotor's magnetic level. Specifically, three distinct fault scenarios were delineated based on the number of broken magnets (BM), namely 2, 3, and 4, localized within specific rotor areas. Notably, the magnetic flux density was selected as the focal parameter for this investigation. To effectively detect and diagnose faults stemming from BM, an innovative Convolutional Neural Network (CNN) architecture was devised. Leveraging images of the PMSM design captured during operational phases at various time intervals, the CNN exhibited remarkable efficacy in discerning and categorizing fault instances. Upon analysis of the derived outcomes, it becomes evident that the CNN exhibited unparalleled accuracy in fault detection, achieving a remarkable 100% success rate when juxtaposed with alternative methodologies such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), which yielded accuracy rates of 97%.
Açıklama
Anahtar Kelimeler
Broken magnets, Convolutional neural network, Fault detection, Magnetic flux density, PMSM
Kaynak
Journal of Engineering and Technology for Industrial Applications
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
10
Sayı
48
Künye
Benkaihoul, S., Mazouz, L., Naas, T. T., Özüpak, Y. ve Mohammedi, R. R. (2024). Broken magnets fault detection in PMSM using a convolutional neural network and SVM. Journal of Engineering and Technology for Industrial Applications, 10(48), 55-62.
