Aero-Engine Remaining Useful Life Estimation Based on Multi-Head Networks

Data-driven aero-engine remaining useful life (RUL) estimation is a key technology to monitor engine’s degradation. However, due to the difficulties of extracting the time information from data, the accuracy of data-driven methods remains low. Aiming at the problem, this article proposes a novel multi-head structure to wrap time information into the network structure and training processes. Multi-head networks are designed to take the time sequence information of inputs in the feedforward process. Meanwhile, the time loss function takes full consideration of the time prior information of the outputs (estimated RUL) and directs the backward process of the networks to converge to the real aero-engine operating situation. Experiments on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) aero-engine’s RUL estimation dataset are conducted to validate the effectiveness of the proposed method. The result and comparisons with other state-of-the-art methods show that the proposed multi-head structure and time loss function can improve the accuracy significantly. This suggests that the proposed method is a promising approach in aero-engine degradation evaluation.