Predictive maintenance in aviation has emerged as a key strategy for minimizing unexpected component failures, enhancing operational reliability, and reducing costs. Traditional maintenance schedules rely on fixed intervals, often leading to over-maintenance or failures between inspections. Machine learning (ML) offers the ability to analyze historical and real-time data to predict potential failures, allowing for dynamic and efficient maintenance strategies.
Objectives:
- Develop ML models to predict component failures and optimize maintenance schedules for aviation systems.
- Identify key failure patterns from historical maintenance and operational data.
- Evaluate the cost-effectiveness of predictive maintenance strategies over traditional methods.
- Create actionable insights to improve maintenance decision-making.
Research Questions:
- How can ML models forecast potential failures for critical aviation components?
- What are the most impactful operational and environmental factors contributing to component degradation?
- How does predictive maintenance compare to current practices in terms of cost and reliability?
Tasks:
- Data Collection: Use open datasets or create synthetic data reflecting maintenance trends.
- Data Preprocessing: Handle missing values, normalize data, and conduct exploratory analysis to identify trends.
- Feature Engineering: Identify influential features & develop derived features.
- Model Development: Train supervised ML models (e.g., random forest, neural networks) for failure prediction. Use unsupervised learning for anomaly detection in maintenance patterns.
- Testing: Use historical data for model training and recent data for validation.
- Evaluation Metrics: Validate model performance using metrics like precision, recall, and F1 score.
This study will optimize maintenance workflows, reduce operational downtime, and ensure higher reliability in aviation systems, contributing to safer and more efficient air travel.
Time duration: 6 Months
Contact:
Dr. Thomas Feuerle, t.feuerle@tu-braunschweig.de
Parth Purohit, parth-yogeshbhai.purohit@tu-braunschweig.de