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
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