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- W4385486530 abstract "In recent years, the deep learning methods have been extensively applied in the field of RUL prediction. However, existing methods usually do not enable adaptively weight the importance of multi-sensor data from complex systems. In this paper, a new RUL prediction method based on spatial-temporal attention network (STAnet) is proposed for multi-sensor data. The spatial-temporal attention module in the network can adaptively weight and encode the original signal without any prior knowledge. The feature extraction module can extract the hidden features from the weighted data. The information reinforcement module can weight and decode hidden features while performing information reinforcement, filtering and complementation. Finally using a fully connected layers to map the deep degradation features into specific remaining useful life. Experiments were also conducted on a commonly used dataset, and the results demonstrate its effectiveness and superiority." @default.
- W4385486530 created "2023-08-03" @default.
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- W4385486530 date "2023-01-01" @default.
- W4385486530 modified "2023-09-23" @default.
- W4385486530 title "Remaining Useful Life Prediction of Multi-sensor Data Based on Spatial-Temporal Attention Network" @default.
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- W4385486530 doi "https://doi.org/10.1007/978-981-99-4334-0_105" @default.
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