Diagnostics of a small jet engine-neural networks approach

This paper presents the design procedure and application of a nested neural network for diagnostics of a small jet engine. Such a diagnostics technique is based on the performance analysis while the performance model was developed with TURBOMATCH, the Cranfield University's gas turbine simulation code. To validate this model, an outdoor test was conducted to run the small engine. Areas examined in this paper are performance validation of the engine, neural network design, training data generation, and networks training procedures. The assumptions, measured parameters selection and the results obtained are presented and discussed. The results obtained show the good prospects for the use of NN's for detection of existing faults, isolation of faults and quantification of fault levels.