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Markov Nonlinear System Estimation for Engine Performance Tracking

Peng Wang,

Department of Mechanical and
Aerospace Applied science,
Instance Western Reserve University,
Cleveland, OH 44106
e-mail: pxw206@case.edu

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Robert X. Gao

Fellow ASME
Department of Mechanical and
Aerospace Applied science,
Instance Western Reserve University,
Cleveland, OH 44106
electronic mail: Robert.Gao@case.edu

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Peng Wang

Section of Mechanical and
Aerospace Technology,
Case Western Reserve University,
Cleveland, OH 44106
e-mail: pxw206@case.edu

Robert Ten. Gao

Young man ASME
Department of Mechanical and
Aerospace Engineering,
Case Western Reserve Academy,
Cleveland, OH 44106
electronic mail: Robert.Gao@case.edu

Contributed by the Shipping Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GEqually TURBINES AND POWER. Manuscript received June 20, 2015; final manuscript received January 31, 2016; published online March 22, 2016. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power. Sep 2016, 138(ix): 091201 (10 pages)

Published Online: March 22, 2016

This paper presents a joint country and parameter estimation method for aircraft engine functioning degradation tracking. Contrast to previously reported techniques on state interpretation that view parameters in the state evolution model as constants, the method presented in this paper treats parameters equally fourth dimension-varying variables to business relationship for varying degradation rates at dissimilar stages of engine performance. Transition of deposition stages and estimation of parameters are performed by particle filtering (PF) under the Bayesian inference framework. To address the sample impoverishment problem due to discrete resampling, which is inherent to PF, a continuous resampling strategy has been proposed, with the goal to improve estimation accuracy of PF. The algorithm has shown to exist able to observe abrupt mistake inception based on the residuals betwixt the estimated results from the state evolution model and actual measurements. The developed technique is evaluated using data generated from a turbofan engine model. Simulation of engine output parameters over a serial of flights with both nominal degradation and precipitous mistake types has been conducted, and fault within 1% for performance tracking and degradation prediction has been shown. This demonstrates the effectiveness of the developed technique in mistake detection and degradation tracking in aircraft engines.

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Source: https://asmedigitalcollection.asme.org/gasturbinespower/article/138/9/091201/374198/Markov-Nonlinear-System-Estimation-for-Engine