Estimating in-flight engine performance variations using Kalman filter concepts

Luppold, R.; Román, Jesús Ángel; Gallops, G. W.; Kerr, L. J.

doi:10.2514/6.1989-2584

Cited by 121 publications

(79 citation statements)

References 5 publications

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“…erver designing process. The coordinate (4) ansformation, the measurement matrix (5) rdinates, Equation (3) can be described nˆn is symmetric positive definite. This represents a convex optimization problem, which can be handled by standard LMI software.…”

Section: Proofmentioning

confidence: 99%

“…erver designing process. The coordinate (4) ansformation, the measurement matrix (5) rdinates, Equation (3) can be described (6) the form of where ∈ is added with modeling uncertainties as , , ) t x u (7) tion matrix.…”

Section: Proofmentioning

confidence: 99%

“…Results have shown a range of degradation cases can be faithfully detected and estimated with suitable accuracy and quick diagnosis speed by the described observer. HPT efficiency h 4 LPT efficiency h 5 LPC flow capacity h 6 HPC flow capacity h 7 HPT flow capacity h 8 LPT flow capacity W f Fuel flow rate A 8 Nozzle area P 25 HPC inlet pressure T 25 HPC inlet temperature P 3 Combustor inlet pressure T 3 Combustor inlet temperature EGT Outlet temperature of 1st stage inlet guide vane of LPT…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…server designing process. The coordinate (4) transformation, the measurement matrix (5) ordinates, Equation (3) can be described niques, health parameters is required to ed system is created:…”

Section: Proofmentioning

confidence: 99%

“…A well-developed model-based strategy for health monitoring systems is the Kalman filter (KF) strategy. Luppold et al [4] proposed an algorithm based on a KF concept to estimate in-flight engine performance variations. Simon et al [5] applied the constrained KF approach, along with constraint tuning on the basis of measurement residuals, to estimate engine In order to estimate health parameters via observer techniques, health parameters is required to be considered as state variables, thus the following augmented system is created: …”

Section: Introductionmentioning

confidence: 99%

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Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

et al. 2016

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Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components' performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.

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Section: Proofmentioning

confidence: 99%

Section: Proofmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

et al. 2016

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Aircraft Propulsion Health Management

Volponi

Wood

2011

System Health Management

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A Model‐Based Approach for Gas Turbine Engine Performance Optimal Estimation

Lü¹,

Lv²,

Huang³

et al. 2013

Asian Journal of Control

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Kalman filters (KF) are widely used in model‐based fault diagnosis designs for aircraft engine health management purposes. Nevertheless, health parameter estimation based on KF in the self‐tuning on‐board real‐time model (STORM) cannot always achieve sufficient health monitoring due to the interrelation of different types of sensors and the number of sensors usually being less than the number of health parameters on the aircraft engine. In order to mitigate this problem, one must accomplish two things: the sensor measurements for fault diagnosis should be analyzed and determined and a health parameter vector in STORM of lower dimension should be acquired. In this paper, a measurement vector for STORM is selected by the concept of sensor condition number and the sensor similarity is used for validation. A transformation matrix for the health parameter vector is introduced, and quantum particle swarm optimization (QPSO) is employed to produce a health parameter subset with appropriate dimensions to enable KF based estimation. Simulations on this method are carried out on a turbofan engine, and the results show that the proposed method for health parameters estimation is efficient.

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Estimating in-flight engine performance variations using Kalman filter concepts

Cited by 121 publications

References 5 publications

Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

Aircraft Propulsion Health Management

A Model‐Based Approach for Gas Turbine Engine Performance Optimal Estimation

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