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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

[+] Author and Article Information
Donald L. Simon, Jonathan S. Litt

 NASA Glenn Research Center, 21000 Brookpark Road, MS 77-1, Cleveland, OH 44135

Due to the proprietary nature of the data, axis scales have been removed from the figures in this document.

Note: SHP, which is proportional to torque times power turbine speed, is not measured directly. It is calculated as SHP=constant×torque×Np.

J. Eng. Gas Turbines Power 133(7), 071603 (Mar 24, 2011) (8 pages) doi:10.1115/1.4002318 History: Received May 25, 2010; Revised July 28, 2010; Published March 24, 2011; Online March 24, 2011

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

FIGURES IN THIS ARTICLE
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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Steady-state data filter architecture

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Figure 2

Steady-state data filter state transition logic

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Figure 3

Comparison of unfiltered, low pass filtered, and lagged TGT during a thermal transient

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Figure 4

Measured TGT from one engine over one flight

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Figure 5

Comparison of raw and steady-state SHP versus TGT data collected from a single flight

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Figure 6

Example of the steady-state data collected from an individual engine over 5 months

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Figure 7

Example of the steady-state residual data collected from an individual engine over 5 months

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Figure 8

Example of ΔSHP versus TGT with and without LPF

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Figure 9

Example of ΔSHP versus TGT with and without TGT transient filter

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Figure 10

Example of steady-state points collected with anti-ice bleed valve open

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Figure 11

Example of steady-state data points collected with (postulated) inlet barrier filter installation

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Figure 12

Example of steady-state data points collected with a TGT instrumentation bias

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Figure 13

Example of steady-state data points collected with a torque measurement dropout

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Figure 14

Example of steady-state data points collected with an OAT instrumentation bias

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