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Research Papers: Gas Turbines: Aircraft Engine

Markov Nonlinear System Estimation for Engine Performance Tracking

[+] Author and Article Information
Peng Wang

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

Robert X. Gao

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
Case Western Reserve University,
Cleveland, OH 44106
e-mail: Robert.Gao@case.edu

1Corresponding author.

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS 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 138(9), 091201 (Mar 22, 2016) (10 pages) Paper No: GTP-15-1211; doi: 10.1115/1.4032680 History: Received June 20, 2015; Revised January 31, 2016

This paper presents a joint state and parameter estimation method for aircraft engine performance degradation tracking. Contrast to previously reported techniques on state estimation that view parameters in the state evolution model as constants, the method presented in this paper treats parameters as time-varying variables to account for varying degradation rates at different stages of engine operation. Transition of degradation 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 be able to detect abrupt fault inception based on the residuals between 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 series of flights with both nominal degradation and abrupt fault types has been conducted, and error within 1% for performance tracking and degradation prediction has been shown. This demonstrates the effectiveness of the developed technique in fault detection and degradation tracking in aircraft engines.

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Figures

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Fig. 2

PF integrated with TV filter for engine performance tracking

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Fig. 3

Initial sampling, weight update: (a) standard SIR and (b) local search importance resampling; degeneracy problem and sample impoverishment problem in SIR

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Fig. 4

Jointed PF and TV filter for engine performance tracking

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Fig. 5

Computed health parameter for normal engine deterioration

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Fig. 6

Health parameter estimation and 80-step-ahead prediction for normal deterioration

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Fig. 7

Health parameter estimation and 40-step-ahead prediction for normal deterioration

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Fig. 8

Evolution of distribution of parameters in Eq. (29), with left and right figures correspond to the case study shown in Figs. 6 and 7, respectively

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Fig. 9

RUL prediction for normal degradation

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Fig. 10

PF + TV filter for fault degradation estimation and prediction

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Fig. 11

The probability distribution of corrected step change xTV

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Fig. 12

Evolution of distribution of parameters for fault deterioration

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Fig. 13

Mode estimation and transition

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Fig. 14

Performance comparison between the PF and EKF for (left) normal degradation and (right) faulty degradation

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