This paper considers the application of neural networks for jet engine diagnostics. Aircraft engine trending and diagnostics provide engine managers and fleet managers with critical information on the health of their engines and assist in identifying potential failures before they occur. The key to a trending system is its ability to model critical engine parameters accurately and then using the difference between the actual and modeled parameters to predict engine malfunction. A backpropagation neural network provides a powerful tool for modeling these parameters. Flight performance data from the F-16 F-100 engine was gathered over a four month period from 90 engines. Five separate, but identical in architecture, networks were implemented in software to model five key parameters of the engine using data from engines known to be good. The trained network then was tested against engine data unseen during training by the network and known to have corrected component failures during the period covered by the data. Comparing the difference between the network modeled parameter and the actual parameter, a measure of engine health was determined. In one case, for example, this difference averaged 26.8% (of the total range covered by the data) for the eight flights prior to the component replacement. After the component was replaced, the difference averaged 5.4% over the fourteen subsequent flights. This result suggests that neural networks may provide a basis for predictive assessment of engine performance. Extensions of this initial study will involve expanding the training data set, determining more precisely the cause and relationships between performance and repair actions, and exploring alternative architectures.
Guy Denney, Guy Denney,
"F16 jet engine trending and diagnostics with neural networks", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152545; https://doi.org/10.1117/12.152545