Fault diagnosis of gas turbine engines by using dynamic neural networks

The goal of this paper is to present an innovative methodology for performing fault detection in gas turbine engines by utilizing dynamic neural networks. The proposed neural network architecture selected belongs to the class of locally recurrent globally feed-forward networks. The envisaged network is structurally similar to a feed-forward multi-layer perceptron with the difference that the employed processing units are not static and possess dynamic characteristics. The developed and constructed dynamic neural network architecture is then used to perform fault detection of anomalies in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate and verify the advantages and capabilities of our proposed neural network diagnosis methodology.