An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring

Lü, Feng; Ju, Hong-Fei; Huang, Jinquan

doi:10.1016/j.ast.2016.08.008

Cited by 71 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kalman filter employs the system's dynamics model and sequential measurements to estimate the state variables of dynamic systems which are better than the estimate obtained by using measurement alone. The schematic representation of performance-based condition monitoring of gas turbine engine using Kalman filter is shown in Figure 7 [82]. Simon et al [84] states that the discrete linear time-invariant system can be presented by…”

Section: Fault Detection and Isolation (Fdi) Approachesmentioning

confidence: 99%

“…To address the effect of the undetermined problem, a tuning parameter that is a linear subset of the original health parameter is introduced using an optimal transformation matrix [95]. In another study, this tuning is extended to state estimation of the non-linear dynamic system known as undetermined EKF [82].…”

Section: Fault Detection and Isolation (Fdi) Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

Tahan¹,

Tsoutsanis

Muhammad³

et al. 2017

Applied Energy

337

158

View full text Add to dashboard Cite

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a highquality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring.

show abstract

Section: Fault Detection and Isolation (Fdi) Approachesmentioning

confidence: 99%

Section: Fault Detection and Isolation (Fdi) Approachesmentioning

confidence: 99%

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

Tahan¹,

Tsoutsanis

Muhammad³

et al. 2017

Applied Energy

337

158

View full text Add to dashboard Cite

show abstract

“…S. Yepifanov implemented the Levenberg-Marquardt method [16] as well as Singular Value Decomposition and ε-structuration [17,18]. A. Volponi et al applied the Kalman filter [19], and this approach is followed and developed in many papers [20][21][22][23][24], whose authors improved stability of the algorithm and its applicability to a non-linear engine model. X. Chang et al applied an alternative method based on the non-linear filtration (sliding mode observer) [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

View full text Add to dashboard Cite

Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

show abstract

“…The control problem and diagnosis problem of smallrange fluctuation near the steady state point are the current research hotspots [5][6][7]. It not only needs to improve the control algorithm, the design methods of the observer, and the estimation method of model uncertainty [8,9] but also needs to improve the model to expand the application range of the linear model. The purpose of developing the application range of the linear model is to make the model more accurate within a wide range of state parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The parameter uncertain model can be gotten by the calculation of programming problems in equations (9) and (10). Equations (9) and (10) are solvable, which means optimization problem has a nonempty solution set. This problem is proved in Ref.…”

mentioning

confidence: 99%

Study on Identification Method for Parameter Uncertainty Model of Aero Engine

Bai

Shuai

Wang

2019

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

The linear model of an aero engine is effective in a small range of the neighborhood of equilibrium points. According to this problem, the identification method for the parameter uncertain linear model of the aero engine was proposed. The identification problem is solved by calculating nonlinear programming. Considering the parameter uncertainty of the model is the critical point of this research during the optimization process. A parameter uncertain model of an aero engine can be obtained, which has large use range. This method is used for DGEN380 aero engine. The two parameters, VDD and VE, are defined for describing error range. Compared with experimental data, the uncertain model of DGEN 380 can simulate the real state of DGEN380 within 1% error range when ΔPLA < 22%. Compared with another conventional method of identification (recursive least squares), the parameter uncertain model, established by the method of this research, has a broad application area through parameter uncertainty of the model.

show abstract

An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring

Cited by 71 publications

References 38 publications

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Study on Identification Method for Parameter Uncertainty Model of Aero Engine

Contact Info

Product

Resources

About