Augmented classification for electrical coil winding defects
Augmented classification for electrical coil winding defects
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DOI：
10.1007/s00170-022-08671-w
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发表时间：
2022-01-23
影响因子：
3.4
通讯作者：
Tiwari, Ashutosh
中科院分区：
文献类型：
--
作者：
Farnsworth, Michael;Tiwari, Divya;Tiwari, Ashutosh
近几十年来，一场绿色革命加速发展，人们希望通过采用更绿色的电气替代品来取代现有的运输电力解决方案。与此同时，制造业的数字化使流程的跟踪和可追溯性以及故障检测和分类的改进取得了进展。本文探讨了电机制造和识别故障模式在这个生命周期中所面临的挑战，通过演示最先进的机器视觉方法的分类电线圈绕组缺陷。我们展示了如何使用最新的生成对抗网络来增强这些模型的训练，以进一步提高它们在这一具有挑战性的任务中的准确性。我们的方法利用预处理和降维来提高标准卷积神经网络（CNN）模型的性能，从而显着提高准确性。
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.