Dynamic neural network-based fault diagnosis of gas turbine engines

In this paper, a neural network-based fault detection and isolation (FIDI) scheme is presented to detect and isolate faults in a highly nonlinear dynamics of an aircraft jet engine. Towards this end, dynamic neural networks (DNN) are first developed to learn the input-output map of the jet engine. The DNN is constructed based on a multi-layer perceptron network which uses an IIR (infinite impulse response) filter to generate dynamics between the input and output of a neuron, and consequently of the entire neural network. The trained dynamic neural network is then utilized to detect and isolate component faults that may occur in a dual spool turbo fan engine. The fault detection and isolation schemes consist of multiple DNNs or parallel bank of filters, corresponding to various operating modes of the healthy and faulty engine conditions. Using the residuals that are generated by measuring the difference of each network output and the measured engine output various criteria are established for accomplishing the fault diagnosis task, that is addressing the problem of fault detection and isolation of the system components. A number of simulation studies are carried out to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme. (C) 2013 Elsevier B.V. All rights reserved.