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Fault diagnosis of gas turbine engines by using dynamic neural networks

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4 Author(s)
Mohammadi, R. ; Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada ; Naderi, E. ; Khorasani, K. ; Hashtrudi-Zad, S.

This paper presents a novel methodology for fault diagnosis in gas turbine engines based on the concept of dynamic neural networks. The neural network structure belongs to the class of locally recurrent globally feed-forward networks. The architecture of the network is similar to the feed-forward multi-layer perceptron with the difference that the processing units include dynamic characteristics. The dynamic neural network is used for fault detection in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate the advantages of our proposed neural network diagnosis methodology.

Published in:

Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on

Date of Conference:

7-10 Aug. 2011