Fault diagnosis of industrial robot based on dual-module attention convolutional neural network

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dc.contributor.authorLu, Kaijie
dc.contributor.authorChen, Chong
dc.contributor.authorWang, Tao
dc.contributor.authorCheng, Lianglun
dc.contributor.authorQin, Jian
dc.date.accessioned2022-06-16T09:45:45Z
dc.date.available2022-06-16T09:45:45Z
dc.date.issued2022-06-01
dc.description.abstractFault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots.en_UK
dc.identifier.citationLu K, Chen C, Wang T, et al., (2022) Fault diagnosis of industrial robot based on dual-module attention convolutional neural network. Autonomous Intelligent Systems, Volume 2, June 2022, Article number 12en_UK
dc.identifier.issn2730-616X
dc.identifier.urihttps://doi.org/10.1007/s43684-022-00031-5
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18032
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfault diagnosisen_UK
dc.subjectindustrial robotsen_UK
dc.subjectdeep learningen_UK
dc.subjectCNNen_UK
dc.subjectattentionen_UK
dc.titleFault diagnosis of industrial robot based on dual-module attention convolutional neural networken_UK
dc.typeArticleen_UK

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