From Dampers Estimated Loads to In-Service Degradation Correlations

This paper presents an original method that takes advantage of existing large in-service flight data, damper load Machine Learning models as well as the inventory of degraded dampers (elastomeric part), to link the estimated loads and operational conditions to damper degradation cases. The Machine Learning models are trained on flight test campaigns data, and then applied on in-service helicopter data to estimate damper loads as a function of flight parameters. The estimated load history is then used as an input to generate engineering load indicators. These latter, jointly with operational and usage data, are correlated with the reported dampers' degradation observations. Finally, an explainability mechanism is investigated to better understand the Machine Learning models inferences, opening perspectives towards precise damper degradation root causes identification. The obtained results are promising, showing that the occurrence of damper degradation correlates with load history and helicopter operations.