Drone Health Guard is an AI-powered predictive maintenance system designed to revolutionize drone fleet management.
Instead of waiting for failures, our system uses machine learning on drone sensor data to predict faults before they occur — improving safety, reliability, and cost efficiency.
Every year, 15% of commercial drones fail during operations, costing businesses an average of $4,200 per incident and several hours of downtime.
Drone Health Guard transforms this reactive maintenance model into a proactive AI-driven approach.
- 74% accuracy in detecting and classifying four distinct fault types.
- Predicts failures 30+ minutes before they occur.
- Provides fault type, severity assessment, and maintenance recommendations.
- Potential savings: $2,000+ per drone annually.
Drone failures cost businesses millions.
- 15% of commercial drones fail annually
- Average repair + downtime cost: $4,200 + 8 hours
- Safety risks in logistics, inspection, and urban operations
- Current maintenance is reactive, not predictive
Drone Health Guard leverages AI to:
- Detect fault types and severity in real time
- Generate actionable maintenance alerts
- Reduce downtime and prevent in-flight failures
- Improve operational safety and reliability
- Source: Real operational drone data (with induced faults)
- Size: 70 flight recordings
- Sensors: Controller, stabilizer, and drone system readings
- Fault Classes: 4 distinct fault types (F0–F3) with varying severity levels
- Data Preprocessing – Clean and normalize multi-sensor readings
- Feature Extraction – Derived temporal and statistical features
- Model Training – Machine learning classifiers for fault prediction
- Evaluation – Accuracy, precision, recall, F1 score
- Deployment – Integrated with a dashboard or API (optional extension)
- Python 3.x
- NumPy, Pandas, Scikit-learn
- Matplotlib, Seaborn
- IBM watsonx.ai (for model training and explainability)
- Google Colab / Jupyter Notebook
# Clone repository
git clone https://github.com/LUTHFI007/DroneFaultDetection-Hackathon.git