A Node-Level PathGraph-Based Bearing Remaining Useful Life Prediction Method

Graph deep learning-based prognostic methods have been successfully applied in bearing remaining useful life (RUL) prediction, as graph represents spatial and temporal dependencies of signals. However, graph data-driven prediction methods using single-sensor data are still insufficiently studied. And the graph construction is not interpretable, where the physical meaning of edges is unclear. To overcome these limitations, a node-level PathGraph-based bearing RUL prediction method is proposed, where a Chebyshev graph convolutional network (ChebCGN) with bi-directional long short-term memory network (BiLSTM) is designed. The node-level PathGraph is constructed to represent the relationships among the time-discrete signals, where edges denote the chronological order and nodes represent signals. After that, graph feature learning ability of ChebGCN-LSTM is enhanced by inputting different chronological PathGraphs related to bearings’ states. In ChebGCN-LSTM, the BiLSTM captures the temporal information, overcoming the limitation of ChebGCN that ignored global temporal dependencies of signals. The constructed PathGraphs are input to ChebGCN-LSTM simultaneously to realize RUL prediction. Experimental results on case studies verify the effectiveness of the proposed bearing RUL prediction method.