In this paper we present a neural network-based approach to predict engine stall events 2 seconds prior to their occurrence. The objective of this effort is to use engine and aircraft data to investigate stall events that have occurred on a modern gas turbine engine and to use that data to develop methods to predict stall events probabilistically. Engine data included pre-stall event data for multiple stall events, take-off trend data for the flights when the stalls occurred, and flight data for dozens of flights. The results indicate that neural network-based approach is able predict 99 out of 100 the stall events with a false alarm rate of 12%. The insights gained through this research can be used for developing an elaborate probabilistic model for predicting stall events.

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