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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics & Instrumentation

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

[+] Author and Article Information
H. S. Tan

 Republic of Singapore Air Force, Air Logistics Department, Propulsion Branch, 303 Gombak Drive, 01-44 Singapore 669645

J. Eng. Gas Turbines Power 128(4), 773-782 (Oct 17, 2005) (10 pages) doi:10.1115/1.2179465 History: Received October 04, 2004; Revised October 17, 2005

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Data distribution of two module faults

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Figure 2

The structure of Fourier neural network

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Figure 3

The structure of GSLN

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Figure 4

Distribution of training and testing data

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Figure 5

Networks’ accuracies at different input dimensions

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Figure 6

Fourier neural networks’ accuracies at different integral index N

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Figure 7

(a)–(d) Surface reconstructions by the neural networks

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Figure 8

Noise robustness of various neural networks

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Figure 9

Networks’ accuracies at each epoch

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