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.

Copyright © 2006 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

Data distribution of two module faults

Grahic Jump Location
Figure 2

The structure of Fourier neural network

Grahic Jump Location
Figure 3

The structure of GSLN

Grahic Jump Location
Figure 4

Distribution of training and testing data

Grahic Jump Location
Figure 5

Networks’ accuracies at different input dimensions

Grahic Jump Location
Figure 6

Fourier neural networks’ accuracies at different integral index N

Grahic Jump Location
Figure 7

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

Grahic Jump Location
Figure 8

Noise robustness of various neural networks

Grahic Jump Location
Figure 9

Networks’ accuracies at each epoch



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In