A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data.

• Quantitative guidance for graph-structured data preprocessing is proposed. • Deep learning structure based on ARMAGCN and GRU considering the interactions among multisensor signals with a graph structure is developed. • Physics-informed loss function is proposed to train neural networks, specifically for RUL estimation. • Risk eliminations and high accuracy parallelled for data-driven method in RUL estimation. Data-driven models, especially deep learning models, are proposed for remaining useful life (RUL) estimation with multisensor signals. Various treatments to reduce data sensitivity, addressing the difficulty of learning dynamic topologies, and coping with the lack of engineering physics guidance for model training limit the performance of these models and their use. This study proposes a systematic method to estimate RUL with multisensory data under dynamic operating conditions and multiple failure modes. Firstly, ARMA regression is introduced into the graph convolutional network(GCN) model. This allows the information loss in the GCN model following training to be lifted with low computational complexity. Secondly, the physics equations of balancing for economy and security in preventive maintenance policies is introduced in the loss function for training. This involves in a way to impose a higher penalty on delayed predictions, so to focus the neural network training on the control of high-risk situations. Finally, the method is validated on the popular C-MAPSS dataset. Compared with other cutting-edge methods, the proposed method can ensure high-fitting accuracy with strong security. In practice, the controllability and flexibility of deep learning models are enhanced, ensuring the reduction of high-risk, uncertain situations while sacrificing as little accuracy as possible. [ABSTRACT FROM AUTHOR]