Across 47 monitored component classes — engine, hydraulics, avionics, and airframe systems.
Unplanned Aircraft-on-Ground events reduced from 19 per year to fewer than 2 — at an average cost of £160k per incident.
Average lead-time before a predicted fault — sufficient for pre-emptive maintenance scheduling without flight disruption.
Full deployment across the entire MRO facility — from discovery through integration, validation, and EASA compliance sign-off.
The problem. The fix.
Reactive maintenance was grounding aircraft and breaking budgets
The client's MRO facility was operating on a traditional scheduled maintenance model — replacing components on calendar intervals regardless of actual wear state. This led to two compounding problems: over-maintenance of healthy components (wasted cost) and under-detection of in-flight degradation (catastrophic risk).
Unplanned AOG events were averaging 19 per year. Each grounding cost between £120k and £280k in direct costs, with knock-on penalties, crew displacement, and passenger disruption adding further losses. Insurance premiums were rising. Regulator scrutiny was increasing.
A previous attempt with a off-the-shelf predictive tool achieved only 58% accuracy — too low to trust for flight-safety decisions — and was decommissioned after six months.
Sensor-fusion AI with digital twin and agentic decision layer
TechProf designed a three-layer autonomous system. The first layer ingests real-time telemetry from 847 distributed sensors per aircraft — vibration, thermal, pressure, electrical load, and acoustic — at 200Hz sampling frequency.
The second layer runs a neural ensemble model trained on 12 years of historical maintenance logs, fault records, and EASA incident data. The ensemble cross-validates predictions across seven independent model families before issuing a confidence score.
The third layer is an agentic decision engine: when confidence exceeds threshold, it autonomously raises a work order in the client's MRO system, books the appropriate hangar slot, pre-positions parts, and notifies the engineering team — requiring zero human intervention for routine pre-emptions.
18 weeks, zero
production disruption.
Sensor mapping, historical data ingestion, EASA compliance review, data quality assessment across 12 years of maintenance records.
Neural ensemble training, backtesting against 3 years of known AOG events, achieving 94.2% accuracy before advancing to shadow deployment.
System ran in parallel with existing process — predictions logged but not actioned. 14 AOG-avoidance opportunities correctly identified with zero false positives in the first 30 days.
Full production cutover, MRO system integration, agentic work-order automation activated, EASA AMC20-3 compliance validated, engineering team trained.
"The first week live, the system predicted a hydraulic servo degradation on a 737 with 17 hours' warning. Manual inspection confirmed it. That one event alone justified the entire programme cost. We haven't had an unplanned AOG since."
Ready to eliminate
unplanned groundings?
We'll scope a predictive maintenance programme for your fleet in a 2-hour discovery session — at no cost.