Adaptive Spatio-Temporal Graph Information Fusion for Remaining Useful Life Prediction
Yuxuan Zhang, Yuanxiang Li, Yilin Wang, Yongshen Yang, Xian Wei
Abstract
Accurate remaining useful life (RUL) prediction is of great significance for maintaining the safety and reliability of many industrial systems. In recent years, deep learning based-methods predict the RUL by automatically learning and fusing degradation features from signals and have shown great potential in improving prediction accuracy. However, these methods only focus on capturing degradation information from the sensor signals in the time domain while ignoring the characteristics of different sensors in the spatial domain. Our key motivation is that spatial characteristic is also critical in RUL prediction and cast the RUL prediction problem into a deep spatio-temporal graph fusion problem, which consists of two parts, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , spatial structures learning and spatio-temporal information fusion. We propose a framework, namely, adaptive spatio-temporal graph neural network (ASTGNN) to solve the above problem. In the spatial structures learning part, we propose two spatial graph convolution layers, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ASTGNN-M and ASTGNN-A to learn the spatial structures adaptively from the time-varying signals. In the spatio-temporal information fusion part, we use dilation convolution to alleviate the over-smoothing problem when encountering long sensor signals. Moreover, based on ASTGNN, a model named adaptive spatio-temporal hypergraph neural network (ASTHGNN) is proposed for high-order spatio-temporal feature learning. The performances of ASTGNN-M, ASTGNN-A and ASTHGNN are investigated on the C-MAPSS turbofan engine dataset. Experimental results show that ASTHGNN achieves superior performance compared with the state-of-the-art methods and the proposed methods can effectively learn the graph and hypergraph structures of sensor signals.