Objectives:
1. Develop ML models to predict component failures with sufficient lead time to minimize TAT impact.
2. Identify critical operational, environmental, and historical factors contributing to component degradation.
3. Integrate real-time anomaly detection to capture emerging issues during turnaround.
4. Compare predictive maintenance strategies to current practices in terms of cost, TAT, and reliability.
5. Deploy the predictive maintenance model into an OCC GUI for operational use.
Tasks:
➢ Data Collection: Gather open aviation datasets or create synthetic datasets reflecting in-cabin component failures, maintenance logs, turnaround events, and operational parameters.
➢ Data Preprocessing: Clean data, impute missing values, normalize, and conduct exploratory analysis to identify high-impact TAT delay factors.
➢ Feature Engineering: Time-to-failure indicators, Turnaround-phase failure likelihood features, Environmental and operational stress metrics
➢ Model Development: Supervised learning (Random Forest, Gradient Boosting, Neural Networks) for failure prediction, Unsupervised learning (Isolation Forest, Autoencoders) for anomaly detection during turnaround
➢ Simulation & Testing: Use historical and simulated turnaround data for training; validate with recent datasets
➢ Evaluation Metrics: Technical: Precision, Recall, F1-score, RMSE for time-to-failure predictions, Operational: Reduction in TAT delays, % of preemptive maintenance completed within turnaround window
➢ OCC GUI Integration: Build a dashboard with query capability (e.g., "Show high-risk aircraft in the next 24 hrs"), Include visual alerts, risk scores, and recommended actions, Ensure OCC staff can interact with the model for "what-if" scenarios
Time duration: 6 Months
Contact:
Parth Purohit, parth-yogeshbhai.purohit@tu-braunschweig.de
Dr. Thomas Feuerle, t.feuerle@tu-braunschweig.de