Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

In critical applications of electrical machines, ensuring validity and safety is paramount to prevent system failures with potentially hazardous consequences. The integration of machine learning (ML) technologies plays a crucial role in monitoring system performance and averting failures. Among vari...

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Bibliographic Details
Main Authors: Amin Ghafouri Matanagh, Salih Baris Ozturk, Taner Goktas, Omar Hegazy
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Energies
Subjects:
convolutional neural network (CNN)
fault diagnostics
permanent magnet synchronous motors (PMSMs)
broken magnet
transform learning
signal processing
Online Access:https://www.mdpi.com/1996-1073/17/2/368

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https://www.mdpi.com/1996-1073/17/2/368

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