Generation of Unmeasured Loading Levels Data for Condition Monitoring of Induction Machine Using Machine Learning

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2024-03

Department

Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

4

Series

IEEE Transactions on Magnetics, Volume 60, issue 3

Abstract

This article presents a novel data augmentation method that generates feature values for unmeasured loading levels based on limited measured and simulated loading level data. The incorporation of offline simulated data in the augmentation framework and the mapping of the error distribution over the loading levels greatly reduce the dependency on including a large number of loading levels in the curve fitting process. Furthermore, the proposed method shows high potential to minimize the deviation between measured and simulated data at the feature level. The method is applied to the induction machine (IM) to generate feature values at 25% and 50% loading levels for healthy, one, two, and three broken rotor bars (BRBs) conditions. An excellent agreement is observed between the augmented and actual feature values calculated from the measured data at 25% and 50% loading levels. The inclusion of this augmented data in the training phase aids in resolving the generalization issue and enhancing the average classification accuracy of the extreme gradient boosting (XGBoost) algorithm by 9.4% and 4.4% at 25% and 50% loading levels, respectively.

Description

Publisher Copyright: IEEE

Keywords

Broken rotor bar, Load modeling, Loading, Mathematical models, Rotors, Standards, Stators, Training, condition monitoring, data augmentation, induction machine, machine learning

Other note

DOI

10.1109/TMAG.2023.3312267

Citation

Billah, M M, Saberi, A N, Hemeida, A, Martin, F, Kudelina, K, Asad, B, Naseer, M U, Mukherjee, V & Belahcen, A 2024, 'Generation of Unmeasured Loading Levels Data for Condition Monitoring of Induction Machine Using Machine Learning', IEEE Transactions on Magnetics, vol. 60, no. 3, 8201104. https://doi.org/10.1109/TMAG.2023.3312267

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https://urn.fi/URN:NBN:fi:aalto-202310116274

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[article-cris] Sähkötekniikan korkeakoulu / ELEC