Probabilistic mission identification of aircraft engine usage using nonparametric density estimation techniques

Fatigue life prediction of aircraft gas turbine engine rotating components requires estimates of the applied stress values throughout the life of the component. These values may vary considerably from flight-to-flight, and are highly dependent upon the mission type. However, engine flight data recorders currently do not have the capability to identify the mission type, so an automated mission identification method would greatly improve remaining life predictions. In this paper, a method is presented for predicting the most likely mission type for a given flight history. It is based on volume integration of the joint probability densities that are common to both the flight history and a standard mission. An analytical framework is presented, including a brief description of the adaptive kernel method used to estimate the probability densities of the flight history and standard mission. The effectiveness of the method is illustrated using rainflow stress data associated with actual flight histories. The results can be used to improve fatigue and fracture risk predictions of military gas turbine engines