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Author Nozari, H.A. ♦ Banadaki, H.D. ♦ Shoorehdeli, M.A. ♦ Simani, S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Turbines ♦ Neurons ♦ Fault detection ♦ Predictive models ♦ Training ♦ Torque ♦ Temperature measurement ♦ Nonlinear predictor model ♦ Fault detection and isolation ♦ Neural network ♦ industrial gas turbine ♦ Multi-layer perceptron ♦ System identification
Abstract This study proposed a model based fault detection and isolation (FDI) method using multi-layer perceptron (MLP) neural network. Detection and isolation of realistic faults of an industrial gas turbine engine in steady-state conditions is mainly centered. A bank of MLP models which are obtained by nonlinear dynamic system identification is used to generate the residuals, and also simple thresholding is used for the intend of fault detection while another MLP neural network is employed to isolate the faults. The proposed FDI method was tested on a single-shaft industrial gas turbine prototype and it have been evaluated using non-linear simulations based on the real gas turbine data. A brief comparative study with other related works in the literature on this gas turbine benchmark is also provided to show the benefits of proposed FDI method.
ISBN 9781457710780
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-08-16
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 421.44 kB
Page Count 6
Starting Page 26
Ending Page 31

Source: IEEE Xplore Digital Library