AI for Aerospace: From Predictive Maintenance to Flight Control

AI Products for the Aerospace Industry

The cost of a model error in aerospace isn't metrics — it's lives and hundreds of millions of dollars. Data is catastrophically scarce: an aircraft accumulates thousands of flight hours before a failure, and tests are not reproducible. This very contradiction — small data with stringent reliability requirements — defines the architecture of our AI solutions. We develop AI systems that are certifiable to DO-178C and operate under real-world conditions.

How AI Addresses the Small Data Problem in Predictive Maintenance?

The CFM56 aircraft engine is equipped with 250+ sensors. A single flight generates 1.5 GB of ACARS/QAR data. GE Aviation Digital Twin processes data from 40,000+ engines in real time. The key challenge is predicting RUL (Remaining Useful Life) of components. The NASA C-MAPSS dataset is the standard benchmark. Best results: Temporal Convolutional Network (TCN) and Transformer architectures, RMSE ~12–18 cycles on the FD001 test subset. In practice, this yields significant cost savings through timely component replacement.

The small data problem is solved via Physics-Informed Neural Networks (PINNs) and transfer learning: the model is trained on simulation data (NPSS), then fine-tuned on real engine readings using MAML. PINNs achieve 20% lower RMSE than pure TCNs with the same data volume. Anomaly detection in flight data: a normal flight differs from a dangerous pattern. VAE or Isolation Forest are trained on normal QAR traces.

Why Surrogate Models Accelerate CFD Optimization 600x?

CFD simulation of a wing: 6–12 hours on ANSYS Fluent. For multi-objective optimization, thousands of iterations are needed. A surrogate model (Gaussian Process or Neural Network) is trained on 200–500 CFD runs and then used instead of full simulation. Speed: 50 ms vs. 8 hours — a 600x speedup.

Here's how we build a surrogate model:

1. Collect 200-500 CFD runs on a Latin hypercube.
2. Train a Gaussian Process with Matérn kernel.
3. Optimize using Expected Improvement (EI).
4. Validate on a holdout set (R² > 0.95).

Bayesian Optimization with a GP surrogate finds the Pareto front in 300–500 iterations vs. 50,000+ for Grid Search. Frameworks: BoTorch (PyTorch-based), Dragonfly, scikit-optimize. The result: 15–25% mass reduction without loss of strength. The budget for such a module is determined individually based on geometry complexity.

Computer Vision for Non-Destructive Testing

An ultrasonic C-scan or thermographic image of a fuselage panel: a defect (delamination, crack) appears as a local intensity anomaly. Automation of manual analysis, which takes 4–8 hours per panel.

We use YOLOv8 or Mask R-CNN for defect detection, trained on datasets with synthetic augmentation (FEA thermal field simulation + Gaussian noise). In practice: precision 0.89, recall 0.91 on A320 CFRP panels. Additionally: 3D reconstruction from photographs (photogrammetry) for hangar inspection — the point cloud is compared to the CAD model.

Onboard AI Systems

FPGA-based inference for real-time monitoring of the ARINC 429 bus. Requirements: latency < 1 ms, determinism, DO-178C certification (Level A). Models: quantized to INT8, exported to ONNX → TensorRT for NVIDIA Jetson Xavier AGX.

For unmanned systems: depth estimation from stereo or monocular cameras, obstacle detection (YOLO), path planning (RRT* with ML heuristic). Stack: ROS 2 + PyTorch + TensorRT on NVIDIA Jetson.

Certification and Explainability

DO-178C/DO-254 — standards for avionics software and hardware. AI components require ARP 4761 safety assessment. EASA has published AI Roadmap 2.0 — a framework for certifying ML systems in aviation.

Explainability: SHAP values for tabular models (PdM), Grad-CAM for CNNs in NDT. Regulators demand not only accuracy but also the ability to explain the model's decision.

What's Included

• Documentation: model card, safety assessment report, verification plan.
• Deliverables: trained models, monitoring dashboards, code repository.
• Training: knowledge transfer to the client's team for operation and retraining.
• Support: assistance during certification, 12-month warranty.

Development Timeline

From 10 to 24 months — depends on the AI system type, certification requirements, and data availability. NDT module without onboard use: 4–6 months. Onboard system with DO-178C certification: 18–24 months.

If you want to discuss a project, we will assess the task and propose a solution. Request a preliminary evaluation of your project. Get an expert consultation on AI in aerospace.

Physics-Informed Neural Networks — introduction to the method. DO-178C — avionics software certification standard.