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Firstly, a graph convolutional network (GCN) is applied to extract the spatial correlation characteristics and fused with the self-attention mechanism network to obtain the global and local spatial features. Subsequently, a dilated convolutional network (DCN) is integrated into the self-attention mechanism network, to extract the global and multi-step temporal features and mitigate long-term dependency issues. Finally, the extracted spatio-temporal features are used to predict the equipment\u2019s RUL through fully connected layers. The experimental results demonstrate that STCAN outperforms some existing methods in terms of RUL prediction.<\/jats:p>","DOI":"10.1177\/09596518241269642","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T09:10:53Z","timestamp":1723108253000},"page":"315-329","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Remaining useful life prediction for multi-sensor mechanical equipment based on self-attention mechanism network incorporating spatio-temporal convolution"],"prefix":"10.1177","volume":"239","author":[{"given":"Xu","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Beijing, 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