Liquid Neural Networks Model Turbofan Degradation with Improved Interpretability液体神经网络实现涡轮发动机退化监测，可解释性显著提升
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Researchers Weizhi Nie et al. propose a liquid neural network model for aircraft engine health monitoring on the C-MAPSS benchmark, using a disentangled latent-state architecture that separates health degradation from operating-condition variation.
The full model improves sensor forecasting RMSE from 0.2438 (GRU baseline) to 0.2266, with the largest gains on multi-condition subsets FD002 and FD004, achieving an average state-speed Spearman correlation of 0.5960.
The model uses remaining useful life, monotonic risk, and latent-consistency losses to supervise the degradation component, while condition-prediction and decorrelation losses prevent operating-condition leakage and produce a clearer temporal degradation axis.