Augmented classification for electrical coil winding defects

Abstract

A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy.

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Item Type:	Article
Authors/Creators:	Farnsworth, M. Tiwari, D. Zhang, Z. Jewell, G.W. Tiwari, A.
Copyright, Publisher and Additional Information:	© 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Electrical machines; Machine learning; Computer vision; Manufacturing; Coil winding
Dates:	Published: April 2022 Published (online): 23 January 2022 Accepted: 4 January 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:	Funder Grant number Engineering and Physical Sciences Research Council EP/S018034/1
Depositing User:	Symplectic Sheffield
Date Deposited:	07 Feb 2022 13:44
Last Modified:	30 Nov 2022 14:40
Status:	Published
Publisher:	Springer Nature
Refereed:	Yes
Identification Number:	10.1007/s00170-022-08671-w
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:183307

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Augmented classification for electrical coil winding defects

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