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• W4206773814 abstract "With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches." @default.
• W4206773814 created "2022-01-25" @default.
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• W4206773814 date "2022-12-01" @default.
• W4206773814 modified "2023-10-07" @default.
• W4206773814 title "Electrical-STGCN: An Electrical Spatio-Temporal Graph Convolutional Network for Intelligent Predictive Maintenance" @default.
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• W4206773814 doi "https://doi.org/10.1109/tii.2022.3143148" @default.
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