Using DCGAN and WGAN-GP to generate artificial thermal RGB images for induction motors

Hejazi, Shahd, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Y 2024. Using DCGAN and WGAN-GP to generate artificial thermal RGB images for induction motors. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023. Published in: Spezi, Emiliano and Bray, Michaela eds. Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press, pp. 113-117. 10.18573/conf1.aa

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Abstract

The paper investigates the feasibility of using GANs to create realistic induction motor thermal RGB image datasets for multimodal conditionmonitoring systems. Generating high-quality thermal images presents computational challenges, and in this study, two GAN frameworks, DCGAN and WGAN-GP, were used under different health conditions. Firstly, DCGAN was used on three conditions using various hyperparameters, but the results required further improvement. Secondly, WGAN-GP was used with an extensive training duration of 11 hours, utilising 10,000 epochs and a batch size of 64, targeting the inner fault dataset, which resulted in generating artificial images that closely resembled real images. This study highlights the impact of hyperparameters on GAN performance and demonstrates the capability of GANs in creating artificial thermal image datasets.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TR Photography T Technology > TS Manufactures
Additional Information:	Contents are extended abstracts of papers, not full papers
Publisher:	Cardiff University Press
ISBN:	978-1-9116-5349-3
Date of First Compliant Deposit:	10 June 2024
Date of Acceptance:	2024
Last Modified:	29 Jul 2024 15:22
URI:	https://orca.cardiff.ac.uk/id/eprint/169690

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