Abstract
With the development of deep learning, the potential for its use in remaining useful life (RUL) has substantially increased in recent years due to the powerful data processing capabilities. However, the relationships and interdependencies of operation parameters in non-Euclidean space are ignored utilizing the current deep learning-based methods during the degradation process for engine. To address this challenge, an improved sand cat swarm optimization-assisted Graph SAmple and aggregate and gate recurrent unit (ISCSO-GraphSage-GRU) is proposed to achieve RUL prediction in this work. Firstly, the maximum information coefficient (MIC) is utilized for describing the interdependent relations of measured parameters. Building on this foundation, the constructed graph data is used as input to GraphSage-GRU so as to overcoming the shortcomings of existing deep learning methods. Additionally, this work proposed an improved sand cat swarm optimization (ISCSO) to improve the predicted performance of GraphSage-GRU, including tent mapping in population initialization and a novel adaptive approach enhance the exploration and exploitation of sand cat swarm optimization. The CMAPSS dataset is used to validate the effectiveness and advancedness of ISCSO-GraphSage-GRU, and the experimental results show that the R(2) of the ISCSO-GraphSage-GRU is greater than 0.99, RMSE is less than 6.