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What is Rotor Fault Detection in Induction Motors about?

This paper presents a novel method for automatically classifying rotor health states in induction motors driven by soft starters, utilizing the Persistence Spectrum of stray-flux signals and a Convolutional Neural Network (CNN). The approach incorporates Data Augmentation Techniques to enhance the dataset, achieving a classification accuracy of 100% for individual models and 99.89% overall. The study highlights the effectiveness of stray-flux analysis in detecting rotor faults, particularly in the context o

Author

j.hamdoud

Language

EN