Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot

Chen, Chong, Wang, Tao, Lu, Kaijie, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Cheng, Lianglun 2024. Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot. Engineering Applications of Artificial Intelligence 138 (PartA) , 109315. 10.1016/j.engappai.2024.109315 Item availability restricted.

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Abstract

The safe operation of Industrial robots is a major concern in intelligent manufacturing. Accurate compound fault diagnosis is essential to the safe operation of industrial robots, while it is challenging to achieve since the compound fault samples are hard to be collected. Generative adversarial network (GAN) is a useful tool for addressing the data imbalance issue. However, the computation efficiency of GAN in addressing the data imbalance issue has not been investigated. Hence, this study proposes a lightweight GAN named compact convolutional Transformers-GAN (CCT-GAN) to alleviate the data imbalance issue in compound fault diagnosis modelling. Firstly, the feedback current signals collected from the industrial robot are transformed into time-frequency images via continuous wavelet transformation (CWT). Secondly, CCT-GAN is designed to achieve high-quality fake data generation and compound fault diagnosis modelling without large computational costs. Thirdly, the relation between a single fault and the compound fault is considered in the compound fault diagnosis modelling via multi-hot representation to alleviate the data imbalance issue. An experimental study based on the real-world compound fault dataset of industrial robots reveals that the proposed CCT-GAN shows merits in compound fault diagnosis modelling in comparison with the prevailing algorithms. The results indicate that CCT-GAN can performance of compound fault diagnosis when only 100 data samples from each compound fault category are available.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0952-1976
Date of First Compliant Deposit:	16 September 2024
Date of Acceptance:	10 September 2024
Last Modified:	07 Nov 2024 21:15
URI:	https://orca.cardiff.ac.uk/id/eprint/172139

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