Integration of Machine Learning and Probabilistic Methods for Structural Life Assessment in Helicopter Digital Twins

Traditional safe-life methodologies for rotorcraft structural components often result in overly conservative life estimates, increasing maintenance costs and reducing aircraft availability. This study explores the integration of digital twin concepts with probabilistic modeling and machine learning to enhance structural life assessment, demonstrated through a practical case involving the Royal Canadian Air Force CH-146 Griffon helicopter. A probabilistic fatigue model determines a fatigue life distribution by incorporating material variability and uncertain operational loads inferred directly from flight data. Unlike conventional approaches, this method dynamically estimates load spectra, including uncertainty instead of relying on conservative assumptions. Monte Carlo simulations are used to quantify structural risk and assess the impact of load and material uncertainties. Sensitivity analyses highlight these uncertainties’ contributions to failure probability. The proposed approach provides probabilistic life predictions, supporting risk-based maintenance strategies to potentially optimize operational efficiency. The long-term goal is to develop an adaptive digital twin model that continuously updates with new operational flight data, enhancing predictive accuracy for helicopter fleet management.