A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fault diagnosis of gas turbine engines by using dynamic neural networks
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.
Fault diagnosis of gas turbine engines by using dynamic neural networks
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.
Fault diagnosis of gas turbine engines by using dynamic neural networks
Mohammadi, R. (author) / Naderi, E. (author) / Khorasani, K. (author) / Hashtrudi-Zad, S. (author)
2011
4 Seiten, 14 Quellen
Conference paper
English
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