液态潜在状态动力学用于可解释的涡扇退化建模
Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling
AI总结：
提出液态神经网络作为潜在动力学模型，通过分解潜在状态为退化与工况分量，在C-MAPSS上实现可解释的退化建模，传感器预测RMSE从0.2438降至0.2266。
摘要：
用于预测的多变量时间序列模型通常通过点预测精度来评估，但其内部状态很少揭示连贯的退化过程。我们研究液态神经网络作为潜在动力学模型，用于C-MAPSS基准上的飞机发动机健康监测。所提出的模型将历史窗口编码为潜在状态，通过液态转移模型演化该状态，并解码未来的传感器观测。为了将健康演化与工况变化分离，潜在状态被分解为退化和工况分量。剩余寿命、单调风险和潜在一致性损失监督退化分量，而工况预测和去相关损失阻止工况泄漏。在FD001--FD004上，完全解耦模型将整体传感器预测RMSE从GRU基线的0.2438降低到0.2266，在多工况子集FD002和FD004上增益最大。学习到的退化状态也形成了更清晰的时间退化轴，平均状态速度斯皮尔曼相关性达到0.5960。直接剩余寿命回归在GRU基线上仍然更强，表明所提出的表示目前作为退化动力学的可解释世界模型比作为校准的寿命回归器更有效。这些结果表明，液态潜在动力学可以桥接预测性维护预测和可检查的健康状态建模。
英文摘要：
Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent degradation process. We study liquid neural networks as latent dynamics models for aircraft engine health monitoring on the C-MAPSS benchmark. The proposed model encodes a history window into a latent state, evolves that state with a liquid transition model, and decodes future sensor observations. To separate health evolution from operating-condition variation, the latent state is factorized into degradation and condition components. Remaining useful life, monotonic risk, and latent-consistency losses supervise the degradation component, while condition prediction and decorrelation losses discourage operating-condition leakage. Across FD001--FD004, the full disentangled model improves overall sensor forecasting RMSE from 0.2438 for a GRU baseline to 0.2266, with the largest gains on the multi-condition subsets FD002 and FD004. The learned degradation state also forms a clearer temporal degradation axis, reaching an average state-speed Spearman correlation of 0.5960. Direct remaining-useful-life regression remains stronger for the GRU baseline, indicating that the proposed representation is currently more effective as an interpretable world model for degradation dynamics than as a calibrated lifetime regressor. These results suggest that liquid latent dynamics can bridge predictive maintenance forecasting and inspectable health-state modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
