Fire Control Radar Fault Prediction with Real-Flight Data

Unexpected failures of avionics equipment critically affect flight safety, operational availability, and maintenance costs. To address these issues, Condition-Based Maintenance Plus (CBM+) has emerged as a strategy to optimize maintenance timing based on equipment condition rather than fixed schedules. However, while aviation research has largely focused on engines and structures, studies on avionics systems remain limited, often relying on simulations. This study proposes a novel data-driven approach to predict avionics equipment failures using actual aircraft operational data. Maneuver-related sequences were analyzed to investigate correlations between flight patterns and equipment faults, and a two-stage framework was developed. In the feature extraction stage, a CNN-LSTM encoder compresses 10 s maneuver sequences into compact yet informative representations. In the fault prediction stage, AI models classify failures of the Fire Control Radar based on these features. Experiments with real flight data validated the effectiveness of the method, showing that the CNN-LSTM encoder preserved essential maneuver information, while the combination of Standard Scaling and Multi-Layer Perceptron achieved the best performance, with a maximum Fault Recall of 98%. These findings demonstrate the feasibility of practical CBM+ implementation for avionics equipment using only flight data, providing a promising solution to improve maintenance efficiency and aviation safety.