Sina S. Tayarani-Bathaie, Zakieh Sadough, and Khashayar Khorasani
Dynamic neural networks, Fault diagnosis, Gas turbine engines
In this paper, a neural network-based fault detection scheme is presented to detect faults in a highly nonlinear dynamic system corresponding to an aircraft jet engine. Toward this end, a dynamic neural network (DNN) is developed to learn the dynamics of the jet engine. The DNN is constructed based on a dynamic multilayer perceptron network which uses IRR filters to generate dynamics between the input and output of the system. The dynamic neural network that is described in this paper is developed to detect component faults that may occur in a dual spool turbo fan engine. Various simulations are carried out to demonstrate the performance of our proposed fault diagnosis scheme.
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