Record Details

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics
NTRS Full-Text: Click to View  [PDF Size: 448 KB]
Author and Affiliation:
Simon, Donald L.(NASA Glenn Research Center, Cleveland, OH United States);
Rinehart, Aidan W.(Vantage Partners, LLC, Brook Park, OH, United States)
Abstract: 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 estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors 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 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.
Publication Date: Jan 01, 2016
Document ID:
20160001158
(Acquired Jan 29, 2016)
Subject Category: AIRCRAFT PROPULSION AND POWER
Report/Patent Number: NASA/TM-2016-218926, GT2015-43744, E-19182, GRC-E-DAA-TN27315
Document Type: Technical Report
Meeting Information: ASME Turbo Expo 2015; 15-19 Jun. 2015; Montreal, Quebec; Canada
Meeting Sponsor: American Society of Mechanical Engineers; New York, NY, United States
Contract/Grant/Task Num: NNC12BA01B; WBS 533127.02.01.03.02
Financial Sponsor: NASA Glenn Research Center; Cleveland, OH United States
Organization Source: NASA Glenn Research Center; Cleveland, OH United States
Description: 20p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright; Distribution as joint owner in the copyright
NASA Terms: ENGINE MONITORING INSTRUMENTS; GAS TURBINE ENGINES; ESTIMATING; PERFORMANCE PREDICTION; ERRORS; DIAGNOSIS; KALMAN FILTERS; SYSTEMS HEALTH MONITORING; FAULT DETECTION; PROBABILITY THEORY; MONTE CARLO METHOD; CLASSIFICATIONS; COVARIANCE; MEAN SQUARE VALUES; MATRICES (MATHEMATICS)
Other Descriptors: SYSTEMS HEALTH MONITORING; GAS TURBINE ENGINES; DIAGNOSIS
› Back to Top
Facebook icon, External Link to NASA STI page on Facebook Twitter icon, External Link to NASA STI on Twitter YouTube icon, External Link to NASA STI Channel on YouTube RSS icon, External Link to New NASA STI RSS Feed
Find Similar Records
 
NASA Logo, External Link

NASA Official: Gerald Steeman

Sponsored By: NASA Scientific and Technical Information Program

Site Curator: STI Support Services

Last Modified: January 29, 2016

Privacy Policy & Important Notices Disclaimers, Copyright, Terms of Use Freedom of Information Act USA.gov NASA.gov NASA OCIO Free Adobe PDF Reader Free MS Word Viewer