How Machine Learning Is Reshaping Predictive Maintenance in Aviation

Aircraft generate vast amounts of operational data that machine learning systems can analyze to predict component failures before they occur. Predictive maintenance models draw on sources such as engine sensors, flight data recorders, and maintenance logs to identify early signs of degradation. Algorithms like Random Forest, LSTM, and Transformer-based models are commonly used, with model selection depending on the specific task and dataset. Key engineering challenges include poor data quality, class imbalance due to rare failure events, and model drift as operating conditions evolve over time. Despite automation, certified maintenance personnel retain full responsibility for inspections, repairs, and clearing aircraft for service.