Self-Attention and Multi-Task Based Model for Remaining Useful Life Prediction with Missing Values

Abstract

Remaining useful life (RUL) prediction is recently a hot spot in industrial big data analysis research. It aims at obtaining the health status of the equipment in advance and making intelligent maintenance decisions. However, values missing is a common problem in real industrial applications which severely restricts the performance and application scope of RUL prediction. To deal with this problem, a novel prediction model called self-attention-based multi-task network (SMTN)is proposed. The spatiotemporal feature fusion module utilizes the self-attention mechanism and long short-term memory to fully exploit the information in space and time dimensions, multi-task learning module tries to learn a complete representation from incomplete data by performing the missing values imputation task, and the representation is simultaneously used for RUL prediction. Comparison experiments conducted on the C-MAPSS dataset verified the effectiveness of the proposed SMTN.