In recent years, global aviation has suffered multi-billion dollar losses due to unscheduled downtime (IATA, 2023). Most engine failures (80%) occur gradually—EGT Margin drops 50-100 cycles before the event. Standard Part-145 methods check engines on a calendar basis, not by condition. We build AI systems for predictive aircraft maintenance that use ACARS and QAR data to predict failure 20-30 flights ahead. Over 5 years, we have delivered 12 projects for fleets ranging from 10 to 100 aircraft—each with CAMO approval and integration into the AMP.
Each prevented AOG saves approximately $150,000 in direct costs, and reducing AOG by 20% for a fleet of 50 aircraft cuts annual costs by $2M. The system works with data from all major engine types: CFM LEAP, GE GEnx, Rolls-Royce Trent—and supports all QAR formats (A321neo, B787, B737NG).
Why AI for Predictive Aircraft Maintenance?
The shift from scheduled to condition-based maintenance is approved by EASA Part-CAMO as an alternative methodology. ML models provide prioritization—which component to check first. The main problems we solve:
- RUL (Remaining Useful Life) prediction: instead of rigid replacement intervals, accurate remaining life estimation.
- Early degradation detection: EGT Margin drop, vibration increase, thermal regime changes.
- AOG reduction: alerts 24-48 hours before the aircraft arrives at base—engineers prepare spare parts and tools.
How We Build RUL Models?
Feature engineering is the foundation of a quality prediction. For each flight, we extract aggregated features from the cruise segment from QAR:
def extract_flight_features(qar_data, flight_phase='cruise'): cruise_data = qar_data[qar_data['phase'] == flight_phase] return { 'egt_mean_cruise': cruise_data['egt'].mean(), 'egt_p95_cruise': cruise_data['egt'].quantile(0.95), 'egt_trend_in_flight': np.polyfit(range(len(cruise_data)), cruise_data['egt'], 1)[0], 'n1_vib_max': cruise_data['n1_vibration'].max(), 'n2_vib_rms': np.sqrt(np.mean(cruise_data['n2_vibration']**2)), 'specific_fuel_consumption': cruise_data['ff'].mean() / cruise_data['thrust'].mean(), 'egt_takeoff_peak': qar_data[qar_data['phase'] == 'takeoff']['egt'].max() } For RUL prediction, we use the Temporal Fusion Transformer from the PyTorch Forecasting library. The flight sequence is treated as a time series, target is the number of cycles until scheduled replacement or failure:
from pytorch_forecasting import TemporalFusionTransformer tft_rul_model = TemporalFusionTransformer.from_dataset( dataset, learning_rate=1e-3, lstm_layers=2, hidden_size=64, output_size=7 ) Our TFT model is 1.3 times more accurate than standard LSTMs in lead time accuracy. Additionally, our system reduces AOG by 20-30%, which is 2-3 times better than traditional scheduled maintenance approaches.
Step-by-step RUL model building process:
- Collect and clean QAR data for the last 2+ years.
- Develop per-flight features (see example above).
- Train TFT model with hyperparameter tuning.
- Validate on historical data with no look-ahead bias.
- Deploy model to production with drift monitoring.
How Long Does Implementation Take?
| Stage | Duration | Content |
|---|---|---|
| Data Analysis | 2-3 weeks | Study QAR/ACARS history, data structure, detect anomalies |
| Feature Engineering | 2 weeks | Develop per-flight features for selected ATA Chapters |
| Model Development | 4-6 weeks | Train TFT, hyperparameter tuning, backtest on historical data |
| MRO Integration | 4-6 weeks | API to AMOS/TRAX/RAMCO, create work orders from alerts |
| CAMO Documentation | 4-8 weeks | Develop AMP amendment, methodology description, validation |
What Is Included in the Work?
- Analysis of QAR/ACARS data for the fleet history (minimum 2 years)
- Building and validating RUL models for engines, APU, landing gear
- Developing dashboards with alerts for line station engineers
- Integration with MRO systems via REST API
- Preparing a full documentation package for CAMO approval
- Staff training (2 days) and technical support for 6 months
- Access to API and real-time dashboards
Comparison of Approaches
| Criterion | Scheduled Maintenance | Predictive Maintenance (our approach) |
|---|---|---|
| Inspection frequency | Fixed intervals per schedule | Condition-based (ML alerts) |
| Probability of missing a failure | High | Low (FN rate < 2%) |
| Maintenance cost | Higher due to unnecessary replacements | Lower by 15-25% |
| Impact on AOG | Frequent unscheduled downtime | AOG reduction by 20-30% |
Regulatory Requirements and Team Experience
Our team has 10+ years in aviation ML and MLOps. We are ISO 9001:2015 certified and have successfully passed CAMO audits for three major airlines. We guarantee correct documentation and full support during approval. Our models are validated on data from over 1000 real flights.
Quality metrics for RUL predictions:
- True Positive Rate: >95%
- False Negative Rate: <2%
- Lead Time Accuracy: ±5 flights
- Precision per component (ATA Chapter 71-80): >90%
How to Start?
Contact us—we will assess your infrastructure and prepare a commercial proposal. Basic solution timeline: 6-8 weeks, full RUL system: 5-7 months. Pricing is individual. Request a consultation today.