A novel graph neural network framework, namely, Spatial–temporal Dual-channel Adaptive Graph Convolutional Network (SDAGCN), is proposed for RUL prediction.
Aug 1, 2023 � Existing frameworks usually design complex graph convolutional networks (GCNs) for multi-sensor information fusion to capture shared patterns�...
These embeddings are utilized in graph construction to enhance the nonlinear capabilities of nodes, thereby improving the effectiveness of GNNs in learning�...
Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion ... Authors: Xingwu�...
Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion. Article. Aug�...
Nov 17, 2020 � Accurate remaining useful life (RUL) estimation is crucial for the maintenance of complex systems, e.g. aircraft engines.
Sep 29, 2024 � Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion.
A bearing RUL prediction method based on multi-sensor data fusion and bidirectional-temporal attention convolutional network (Bi-TACN) is proposed in this�...
Missing: Spatial- dual-
Feb 1, 2024 � Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion.
In HAGCN, the hierarchical graph representation layer is proposed for modeling spatial dependencies of sensors and bi-directional long short-term memory network�...