AI RESEARCH
Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations
arXiv CS.AI
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ArXi:2606.01894v1 Announce Type: new Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance. However, real-world deployment is challenging due to the irregular nature of sensor observations, characterized by asynchronous sampling, burst missingness, and temporal jitter. Compounding this issue, purely data-driven models often generate physically implausible degradation trajectories that violate the irreversible nature of damage accumulation.