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

摘要: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.

关键字:RUL estimation; Graph convolutional network; Multisensor data; Data preprocessing; Physics -informed machine learning

ISSN号:0951-8320

卷、期、页:卷: 237

发表日期:2023-09-01

影响因子:0.000000

期刊分区(SCI为中科院分区):一区

收录情况:SCI(科学引文索引印刷版),EI(工程索引),SCIE(科学引文索引网络版)

发表期刊名称:RELIABILITY ENGINEERING & SYSTEM SAFETY

参与作者:恩里克.齐奥,杨喆

通讯作者:何宇轩,彭世亮,杨兆铭

第一作者:苏怀,林益帆,张劲军

论文类型:期刊论文

论文概要:何宇轩,苏怀,恩里克.齐奥,彭世亮,林益帆,杨兆铭,杨喆,张劲军,A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data,RELIABILITY ENGINEERING & SYSTEM SAFETY,2023,卷: 237

论文题目:A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data

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