Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset

Data-driven methods have prominently featured in the progressive research and development of modern condition monitoring systems for electrical machines. These methods have the advantage of simplicity when it comes to the implementation of effective fault detection and diagnostic systems. Despite th...

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Bibliographic Details
Main Authors:	Bokoro, P. (Author), Doorsamy, W. (Author), Swana, E.F (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	Bayesian classification Classification (of information) Condition monitoring Condition monitoring systems Data handling Electric generators Fault classification Fault data Fault detection imbalanced data Imbalanced data k-nearest neighbor k-nearest neighbors Membership functions Motion compensation Nearest neighbor search Over sampling support vector machine Support vector machines Support vectors machine synthetic minority over-sampling sampling Synthetic minority over-sampling sampling Tomek link Tomek links Wound rotor induction generators wound-rotor induction generator
Online Access:	View Fulltext in Publisher

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