Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines

Abstract

Abstract Accurate remaining useful life (RUL) estimation is crucial for the maintenance of complex systems, e.g. aircraft engines. Thanks to the popularity of sensors, data-driven methods are widely used to evaluate RULs of systems especially deep learning approaches. Though remarkably capable at non-linear modeling, deep learning-based prognostics techniques lack powerful spatio-temporal learning ability. For instance, convolutional neural networks are restricted to only process grid structures rather than general domains, recurrent neural networks neglect spatial relations between sensors and suffer from long-term dependency learning. To solve these problems, we construct a graph structure on sensor network with Pearson Correlation Coefficients among sensors and propose a method for combining the power of graph convolutional network on spatial learning and sequence learning success of temporal convolutional networks. We conduct the proposed method on aircraft engine dataset provided by NASA. The experimental results demonstrate that the established graph structure is appropriate and the proposed approach can model spatio-temporal dependency accurately as well as improve the performance of RUL estimation.