Mission profile clustering for usage-based health modeling of flight control actuators applied to a fleet of advanced jet trainers

This work introduces a mission profile clustering pipeline aimed at supporting usage-based health modeling of electro hydraulic flight control actuators employed in a fleet of Advanced Jet Trainer (AJT) aircraft. The study is part of a broader, high-level, modular Prognostics and Health Management (PHM) framework developed to predict Unscheduled Removals (URs) of the AJT horizontal tail flight control actuators. Operating in an industrial setting, this PHM effort specifically addresses the challenge of extracting prognostic information from a legacy fleet already in service, leveraging existing operational data to improve asset availability. The overall project leverages an extensive real-world dataset that spans over ten years and more than 25000 flight hours accumulated by a fleet of as many as 20 aircraft. This paper specifically focuses on the Flight Clustering Module within the Data Processing layer of the PHM framework, which serves as a critical enabler for future feature projections. Through an in-depth analysis of the underlying principles and a detailed overview of the main system interfaces, this work proposes a practical solution for categorizing and classifying mission profiles while highlighting the challenges of working with real operational data. After a pre-processing pipeline, developed to standardize and align time-series flight data, the clean trends are then clustered via a Self-Organizing Map (SOM). In this work, a systematic SOM hyperparameter tuning pipeline is also introduced. The tuning routine employs a grid search strategy to optimize the SOM hyperparameters by jointly evaluating the topographic error, the quantization error, and the percentage of grid utilization. The result of the application of the trained SOM on the dataset is a set of Clustered Mission Types (CMTs), each linked to specific statistical distributions of actuator usage increments. These clusters are integrated into the broader PHM framework to simulate future aircraft behavior and estimate component degradation. Placed in an operational industrial environment, this methodology effectively connects mission-specific usage patterns with predictive health modeling, improving the modeling ability of PHM systems, and laying the foundation for smarter usage-based maintenance planning in aviation operations.