Leading research on a probabilistic predictive maintenance framework combining hidden degradation-state modeling with maintenance decision optimization under partial observability. The work targets uncertainty-aware health-state inference, calibrated risk estimation, and policy computation to improve long-term cost and reliability in real-world predictive maintenance scenarios.