Model-agnostic meta-learning for fault diagnosis of industrial robots

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dc.contributor.authorLiu, Yuxin
dc.contributor.authorChen, Chong
dc.contributor.authorWang, Tao
dc.contributor.authorCheng, Lianglun
dc.contributor.authorQin, Jian
dc.date.accessioned2023-10-24T15:40:01Z
dc.date.available2023-10-24T15:40:01Z
dc.date.issued2023-10-16
dc.description.abstractThe success of deep learning in the field of fault diagnosis depends on a large number of training data, but it is a challenge to achieve fault diagnosis of multi-axis industrial robots in the case of few-shot. To address this issue, this paper proposes a method called Model-Agnostic Meta-Learning (MAML) for fault diagnosis of industrial robots. Its goal is to train an effective industrial robot fault classifier using minimal training data. Additionally, it can learn to recognize faults in new scenarios with high accuracy based on the training data. Experimental results based on a six-axis industrial robot dataset show that the proposed method is superior to traditional convolutional neural network (CNN) and transfer learning, and that the diagnostic results with the same amount of data in few-shot cases are better than existing intelligent fault diagnosis methods.en_UK
dc.identifier.citationLiu Y, Chen C, Wang T, et al., (2023) Model-agnostic meta-learning for fault diagnosis of industrial robots. In: 2023 28th International Conference on Automation and Computing (ICAC), 30 August - 1 September 2023, Birmingham, UKen_UK
dc.identifier.eisbn979-8-3503-3585-9
dc.identifier.isbn979-8-3503-3586-6
dc.identifier.urihttps://doi.org/10.1109/ICAC57885.2023.10275255
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20437
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectfault diagnosisen_UK
dc.subjectdeep learningen_UK
dc.subjectindustrial robotsen_UK
dc.subjectmeta learningen_UK
dc.titleModel-agnostic meta-learning for fault diagnosis of industrial robotsen_UK
dc.typeConference paperen_UK

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