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Gas Turbine Fault Detection and Isolation Using MLP Artificial Neural Network

[+] Author Affiliations
Gustavo R. Matuck, João Roberto Barbosa, Cleverson Bringhenti

ITA – Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil

Isaias Lima

UNIFEI – Universidade Federal de Itajubá, Itajubá, MG, Brazil

Paper No. GT2007-27987, pp. 803-811; 9 pages
doi:10.1115/GT2007-27987
From:
  • ASME Turbo Expo 2007: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2007
  • Montreal, Canada, May 14–17, 2007
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4790-X | eISBN: 0-7918-3796-3
  • Copyright © 2007 by ASME

abstract

This work deals with a nonlinear model, based on a particular form of artificial neural networks, ANN, for application to gas turbines fault diagnosis. The traditional multi-layer perceptron (MLP) is used, with error back-propagation and different activation functions. The application of the model is illustrated using test data from a gas turbine simulation computer program. A specially developed computer program is used to simulate the engine in operation, generating all needed engine data for both baseline and deteriorated engine. A test case using a turboshaft engine is used to demonstrate the capacity of this ANN to identify faults that may occur during engine operation.

Copyright © 2007 by ASME

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