A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

Abstract

Accurate implementation of remaining useful life (RUL) prediction of equipment is essential for health management and maintenance decisions. Advances in sensing and big data technologies have provided the possibility for research on data-driven life prediction methods. However, the current RUL prediction methods still need to improve in utilizing multi-source information. The present techniques consider more the relationship between the temporal information and RUL prediction among the multi-source information and rarely target the research and exploitation of the potential connection between sensor networks and RUL. Therefore, this paper proposes a spatio-temporal feature extraction network based on the sensor dynamic graph: DST-GT model. The method extracts the unidirectional relationship between sensors from monitoring data to construct a dynamic spatio-temporal graph reflecting the sensor relationship. The DST-GT model uses graph convolution based on message selection (MSGCN) to model the spatial dependencies of sensors, and uses multi-scale gated temporal convolution module (MGTCN) to model the temporal dependencies in sensor state monitoring data. In this paper, graph learning, graph convolution and temporal convolution modules are jointly learned in an end-to-end framework. The results on two widely used datasets and comparisons with other methods demonstrate the accuracy and advancement of DST-GT networks for RUL prediction.