Research Papers: Gas Turbines: Aircraft Engine

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

[+] Author and Article Information
Donald L. Simon

NASA Glenn Research Center,
21000 Brookpark Road,
Cleveland, OH 44135
e-mail: Donald.L.Simon@nasa.gov

Aidan W. Rinehart

Vantage Partners, LLC,
3000 Aerospace Parkway,
Brook Park, OH 44142
e-mail: Aidan.W.Rinehart@nasa.gov

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received October 6, 2015; final manuscript received November 5, 2015; published online February 17, 2016. Editor: David Wisler.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Eng. Gas Turbines Power 138(7), 071201 (Feb 17, 2016) (11 pages) Paper No: GTP-15-1479; doi: 10.1115/1.4032339 History: Received October 06, 2015; Revised November 05, 2015

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori (MAP) estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors (SSEE) in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate (CCR) for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

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Grahic Jump Location
Fig. 1

Gradual versus rapid performance shifts




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