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What is Diagnosing Bearing Defects in Motors about?

This thesis by Maryam Farajzadeh-Zanjani focuses on data-driven techniques for diagnosing bearing defects in induction motors, emphasizing the importance of early fault detection to reduce maintenance costs and prevent downtime. It addresses challenges such as class imbalance in training datasets and outlines methods for feature extraction, dimensionality reduction, and classification. The research includes collaboration with other scholars and incorporates previously published works related to the topic.

Author

bodee.kaeson

Language

EN