Adaptive Spatio-Temporal Graph Convolutional Neural Network for Remaining Useful Life Estimation

Accurate remaining useful life (RUL) estimation is of crucial importance to numerous industrial applications where safety and reliability are among primary concerns. Recently, deep learning based prognostics methods have been emerging as an effective method to improve RUL prediction results. However, these methods, e.g. recurrent neural networks (RNNs), convolutional neural networks (CNNs), only capture temporal information of the sensory data while ignoring intrinsic spatial relations between sensors. To solve this problem, in this work, we propose a framework, namely, adaptive spatio-temporal graph convolutional neural network (ASTGCNN). The proposed framework consists of two parts. In the spatial domain, since the intrinsic graph structure of sensors is not provided in most situations, a dynamic graph neural network is proposed to learn the sensors' spatial relation. In the temporal domain, a stacked dilated ID CNN is utilized to capture long range dependency of input sensor signals. These two parts are integrated in a unified framework and can be trained in an end-to-end manner. The performance of ASTGCNN is investigated on the turbofan engine dataset Experimental results show that the proposed framework can improve the RUL prediction performance of the current deep learning methods, and learn the intrinsic spatial information of sensors.