Remaining Useful Life Prediction

Remaining Useful Life (RUL) Prediction focuses on estimating how much useful operating time is left before a component, subsystem, or asset reaches a failure threshold. In aerospace and defense, this is applied to engines, critical components, and other high‑value equipment using rich operational and condition-monitoring data instead of fixed time or cycle-based maintenance intervals. The goal is to transition from scheduled or overly conservative maintenance to condition-based and predictive maintenance strategies. AI techniques ingest multichannel sensor data, usage profiles, and environmental conditions to model equipment degradation and forecast RUL with high accuracy. This enables maintenance teams to plan interventions just in time, avoid unexpected failures, and better manage spares and logistics. For aerospace and defense organizations, accurate RUL prediction directly improves safety, asset availability, mission readiness, and lifecycle cost control across fleets of complex, expensive assets.