Spatial attention-based convolutional transformer for bearing remaining useful life prediction

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dc.contributor.authorChen, Chong
dc.contributor.authorWang, Tao
dc.contributor.authorLiu, Ying
dc.contributor.authorCheng, Lianglun
dc.contributor.authorQin, Jian
dc.date.accessioned2022-08-18T15:26:21Z
dc.date.available2022-08-18T15:26:21Z
dc.date.issued2022-08-02
dc.description.abstractThe remaining useful life (RUL) prediction is of significance to the health management of bearings. Recently, deep learning has been widely investigated for bearing RUL prediction due to its great success in sequence learning. However, the improvement of the prediction accuracy of existing deep learning algorithms heavily relies on feature engineering such as handcrafted feature generation and time–frequency transformation, which increase the complexity and difficulty of the actual deployment. In this paper, a novel spatial attention-based convolutional transformer (SAConvFormer) is proposed to establish an accurate bearing RUL prediction model based on raw vibration data without prior knowledge or feature engineering. In this algorithm, firstly, a convolutional neural network enhanced by a spatial attention mechanism is proposed to squeeze the feature maps and extract the local and global features from raw bearing vibration data effectively. Then, the extracted senior features are fed into a transformer network to further explore the sequential patterns relevant to the bearing RUL. An experimental study using the XJTU-SY rolling bearings dataset revealed the merits of the proposed deep learning algorithm in terms of root-mean-square-error (RMSE) and mean-absolute-error (MAE) in comparison with other state-of-the-art algorithms.en_UK
dc.identifier.citationChen C, Wang T, Liu Y, et al., (2022) Spatial attention-based convolutional transformer for bearing remaining useful life prediction. Measurement Science and Technology, Volume 33, Issue 11, November 2022, Article number 114001en_UK
dc.identifier.issn0957-0233
dc.identifier.urihttps://doi.org/10.1088/1361-6501/ac7c5b
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18334
dc.language.isoenen_UK
dc.publisherIOP Publishingen_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectremaining useful lifeen_UK
dc.subjectprognostic and health managementen_UK
dc.subjectdeep learningen_UK
dc.subjectTransformer networken_UK
dc.subjectCNNen_UK
dc.titleSpatial attention-based convolutional transformer for bearing remaining useful life predictionen_UK
dc.typeArticleen_UK

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