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Article

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

1
Department of Electrical and Electronics Engineering Technology, Doornfontein Campus, University of Johannesburg, Johannesburg 2028, South Africa
2
Institute for Intelligent Systems, Auckland Park Campus, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Academic Editors: Ada Fort and Tommaso Addabbo
Sensors 2022, 22(9), 3246; https://doi.org/10.3390/s22093246
Received: 1 March 2022 / Revised: 14 April 2022 / Accepted: 20 April 2022 / Published: 23 April 2022
(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)
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 their many advantages, the practical implementation of data-driven approaches still faces challenges such as data imbalance. The lack of sufficient and reliable labeled fault data from machines in the field often poses a challenge in developing accurate supervised learning-based condition monitoring systems. This research investigates the use of a Naïve Bayes classifier, support vector machine, and k-nearest neighbors together with synthetic minority oversampling technique, Tomek link, and the combination of these two resampling techniques for fault classification with simulation and experimental imbalanced data. A comparative analysis of these techniques is conducted for different imbalanced data cases to determine the suitability thereof for condition monitoring on a wound-rotor induction generator. The precision, recall, and f1-score matrices are applied for performance evaluation. The results indicate that the technique combining the synthetic minority oversampling technique with the Tomek link provides the best performance across all tested classifiers. The k-nearest neighbors, together with this combination resampling technique yielded the most accurate classification results. This research is of interest to researchers and practitioners working in the area of condition monitoring in electrical machines, and the findings and presented approach of the comparative analysis will assist with the selection of the most suitable technique for handling imbalanced fault data. This is especially important in the practice of condition monitoring on electrical rotating machines, where fault data are very limited. View Full-Text
Keywords: imbalanced data; Bayesian classification; support vector machine; k-nearest neighbor; Tomek link; synthetic minority over-sampling sampling; wound-rotor induction generator imbalanced data; Bayesian classification; support vector machine; k-nearest neighbor; Tomek link; synthetic minority over-sampling sampling; wound-rotor induction generator
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MDPI and ACS Style

Swana, E.F.; Doorsamy, W.; Bokoro, P. Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset. Sensors 2022, 22, 3246. https://doi.org/10.3390/s22093246

AMA Style

Swana EF, Doorsamy W, Bokoro P. Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset. Sensors. 2022; 22(9):3246. https://doi.org/10.3390/s22093246

Chicago/Turabian Style

Swana, Elsie Fezeka, Wesley Doorsamy, and Pitshou Bokoro. 2022. "Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset" Sensors 22, no. 9: 3246. https://doi.org/10.3390/s22093246

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