Aerospace Defense Asset Life Prediction
This AI solution uses advanced machine learning and graph neural networks to predict remaining useful life and failure risks for aerospace and defense components, platforms, and fleets. By turning multi-sensor, maintenance, and operational data into accurate life forecasts, it enables condition-based maintenance, higher mission readiness, and better reliability-by-design. Organizations reduce unscheduled downtime, optimize sustainment spending, and extend asset life while maintaining safety and performance thresholds.
The Problem
“Predict fleet RUL and failure risk from telemetry + maintenance history”
Organizations face these key challenges:
Unscheduled maintenance orders and mission aborts due to late failure detection
High parts spend and AOG time from conservative time-based maintenance
Siloed data: sensor streams, logs, and maintenance records aren’t aligned to asset configuration
Low trust in predictions because models lack calibration, explainability, and audit trails