A Novel approach using WGAN-GP and conditional WGAN-GP for generating artificial thermal images of induction motor faults

Hejazi, Shahd, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2023. A Novel approach using WGAN-GP and conditional WGAN-GP for generating artificial thermal images of induction motor faults. Presented at: 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023), 06-08 September 2023. Procedia Computer Science. , vol.225 Elsevier, pp. 3681-3691. 10.1016/j.procs.2023.10.363

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Abstract

This paper proposes a novel approach for generating artificial thermal images for induction motor faults using Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) frameworks. Traditional fault classification methods based on vibration signals often require extensive preprocessing and are more susceptible to noise. In contrast, thermal images offer easier classification and require less preprocessing. However, challenges arise due to the limited availability of thermal images representing different fault conditions and data confidentiality. To overcome these challenges, this paper introduces the utilisation of WGAN-GP and cWGAN-GP with health condition labels to create high-quality thermal images artificially. The results demonstrate that the cWGAN-GP approach is superior in generating thermal images that closely resemble real images of induction motors under various health conditions with a Maximum Mean Discrepancy (MMD) score of 1.023 compared to 1.078 using WGAN-GP. Furthermore, cWGAN-GP requires less training time (7.25 hours to train all health conditions classes) compared to WGAN-GP (12 hours to train the Inner fault class only) using NVIDIA V100. In addition to using EMD and MMD metrics for quantitative analysis of the GAN model, the evaluation process incorporated the expertise of a pre-trained CNN model, namely AlexNet, to assess cWGAN-GP's discriminative capabilities of the generated samples and their alignment with the real thermal images, which resulted in an overall accuracy of 98.41%. Therefore, these proposed approaches offer a promising solution to address the lack of public datasets containing induction motor thermal images representing different health states. By leveraging these models, it will be feasible to enhance induction motor condition monitoring systems and improve the process of fault diagnosis.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2023-10-09
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	11 December 2023
Date of Acceptance:	12 June 2022
Last Modified:	11 Dec 2023 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/164661

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