Aerospace Structural Life Intelligence
This AI solution uses AI models to predict structural behavior, degradation, and remaining useful life of aerospace and defense components, from aero‑engines to airframes and mission‑critical hardware. By combining graph neural networks, multichannel sensor analytics, and physics-informed learning, it enables earlier fault detection, smarter maintenance scheduling, and optimized material and design choices—reducing unplanned downtime, extending asset life, and lowering total lifecycle costs.
The Problem
“Predict structural degradation and RUL for aerospace assets from sensor + physics data”
Organizations face these key challenges:
Unexpected removals and AOG events despite scheduled inspections
Conservative life limits causing early part retirement and high spares cost
Large volumes of sensor/flight data with weak linkage to actionable maintenance decisions
Difficulty transferring models across fleets, variants, and operating conditions
Impact When Solved
Technologies
Technologies commonly used in Aerospace Structural Life Intelligence implementations: