Building Predictive Maintenance Systems for Aircraft Using Machine Learning

Short summary

A practical overview of applying machine learning to aircraft predictive maintenance, covering data sources (engine sensors, flight recorders, maintenance logs), model selection (Random Forest, XGBoost, LSTM, Transformers), and engineering challenges like class imbalance and model drift. The article emphasizes that data quality and explainability are critical, and that human-certified personnel remain responsible for all maintenance decisions. It also touches on emerging research areas including federated learning, edge AI, and digital twins.

• •ML models estimate component degradation before failure using sensor and maintenance data
• •Key challenges include class imbalance, explainability (SHAP/LIME), and model drift
• •Human review and certified personnel remain mandatory for all maintenance actions

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