Research Papers: Gas Turbines: Structures and Dynamics

Neural Network Models for Usage Based Remaining Life Computation

[+] Author and Article Information
Girija Parthasarathy1

Vehicle Health Management Laboratory, Honeywell Aerospace, 3660 Technology Drive, Minneapolis, MN 55418girija.parthasarathy@honeywell.com

Sunil Menon, Kurt Richardson, Ahsan Jameel, Dawn McNamee, Tori Desper, Michael Gorelik, Chris Hickenbottom

 Honeywell Aerospace, 111 South, 34th Street, Phoenix, AZ 85034


Corresponding author.

J. Eng. Gas Turbines Power 130(1), 012508 (Jan 11, 2008) (7 pages) doi:10.1115/1.2771248 History: Received June 28, 2006; Revised July 24, 2006; Published January 11, 2008

In engine structural life computations, it is common practice to assign a life of certain number of start-stop cycles based on a standard flight or mission. This is done during design through detailed calculations of stresses and temperatures for a standard flight, and the use of material property and failure models. The limitation of the design phase stress and temperature calculations is that they cannot take into account actual operating temperatures and stresses. This limitation results in either very conservative life estimates and subsequent wastage of good components or in catastrophic damage because of highly aggressive operational conditions, which were not accounted for in design. In order to improve significantly the accuracy of the life prediction, the component temperatures and stresses need to be computed for actual operating conditions. However, thermal and stress models are very detailed and complex, and it could take on the order of a few hours to complete a stress and temperature simulation of critical components for a flight. The objective of this work is to develop dynamic neural network models that would enable us to compute the stresses and temperatures at critical locations, in orders of magnitude less computation time than required by more detailed thermal and stress models. The current paper describes the development of a neural network model and the temperature results achieved in comparison with the original models for Honeywell turbine and compressor components. Given certain inputs such as engine speed and gas temperatures for the flight, the models compute the component critical location temperatures for the same flight in a very small fraction of time it would take the original thermal model to compute.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Model reduction concept

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Figure 2

Neural network schematics (a) neuron and (b) example network

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Figure 3

(a) NNARX model structure and (b) NNOE model structure

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Figure 4

Problem domain and input selection

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Figure 5

Critical location 1 temperatures for different flight profiles (training data)

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Figure 6

Critical location 1 temperatures for different flight profiles (validation data)

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Figure 7

Critical location 2 temperatures for different flight profiles (training data)

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Figure 8

Critical location 2 temperatures for different flight profiles (validation data)



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