Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

Ying, Yulong; Cao, Yunpeng; Li, Shuying; Li, Jingchao

doi:10.1155/2015/940757

Cited by 24 publications

(29 citation statements)

References 24 publications

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“…Brief of QPSO. Particle Swarm Optimization (PSO) [20] was first proposed by Eberhart and Kennedy in the United States in 1995. It is a population-based evolutionary algorithm that simulates the bird flocking process and believes that information sharing among individuals in a population can provide evolutionary advantages, and cooperation as well as competition among individuals can solve the optimization problem.…”

Section: Optimized Gas Turbine Power Calculation Methodsmentioning

confidence: 99%

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

Zhao

et al. 2018

Mathematical Problems in Engineering

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The installed positions of three domestic turbo-shaft engines mounted on a certain type of ship-borne helicopter interfere with the intake air flow of the engines, resulting in a decline of engine performance after initial installation. Due to the difference of load and adjustment method under the bench and installed conditions, it is necessary to study the change in gas turbine power rather than output shaft power of the engine before and after installation to evaluate the engine initial installed power loss. In this paper, quantum-behaved particle swarm optimization (QPSO) is applied to optimize the calculation of gas turbine power at different steady states based on the component-level aerodynamic thermal model of gas generator. Then, extreme learning machine (ELM) is adopted for regressive identification of the established gas generator state assessment model based on data mining and the identification model is applied to engine installed condition. Finally, statistical analysis of engine initial installed gas turbine power loss at three installed positions is carried out, respectively. Results show that the values of engine initial installed gas turbine power loss at three installed positions all conform to the normal distribution, the mean values are 1.658%, 9.828%, and 5.089%, respectively, and a confidence interval with 95% confidence level of the mean values are (1.388%, 1.928%), (9.178%, 10.478%) and (4.308%, 5.870%), which can provide references for determining the power monitoring thresholds after engine installation.

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Section: Optimized Gas Turbine Power Calculation Methodsmentioning

confidence: 99%

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

Zhao

et al. 2018

Mathematical Problems in Engineering

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“…[1][2][3][4][5] However, these important performance and health status information cannot be directly measured and therefore do not be easily monitored. During gas turbine operations, the deviation of component performance parameters can be indicated by the deviation of gas-path measurements and such a deviation may be due to varying ambient conditions or/and operating conditions or/and engine component degradations.…”

Section: Methodsmentioning

confidence: 99%

Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis

Ying

Cao

et al. 2016

Advances in Mechanical Engineering

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Gas-path analysis method has been widespread applied to gas turbine engine health control and has become one of the key techniques in favor of condition-oriented maintenance strategy. Theoretically, gas-path analysis method (especially nonlinear gas-path analysis) can easily quantify gas-path component degradations. However, when the number of components within engine is large which highly expands the dimension of fault coefficient matrix, maybe leading to strong smearing effect (i.e. predicted degradations are located almost in all component health parameters, although some of them are not really degraded), the degraded components may not be accurately identified. In order to improve the robustness of gas turbine gas-path fault diagnosis, a hybrid gas-path analysis approach integrating gray relation algorithm into gas-path analysis method has been proposed in this study. The gray relation algorithm and gas-path analysis method approach includes two steps. First, the faulty components are recognized and isolated by gray relation algorithm, which deeply reduces the dimension of fault coefficient matrix, and second, the magnitudes of detected component faults are quantified by the gas-path analysis. The fault classification analyses and case studies have shown that the confidence level of the fault classification can reach more than 95%, when single and multiple components are degraded, and the predicted degradations are almost same as that of implanted fault patterns.

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“…For the same compressor, the value of D is constant and equations (3) and (4) can be converted into equations (5) and (6)…”

Section: Basic Theorymentioning

confidence: 99%

A component map adaptation method for compressor modeling and diagnosis

Ying

Wang

et al. 2018

Advances in Mechanical Engineering

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The accuracy of model-based gas turbine performance prediction, diagnosis, and prognosis is mainly determined by the precision of compressor thermodynamic model. This article proposes a novel component map adaptation method for gas turbine compressor modeling and gas-path diagnosis. First, compressor characteristic maps are generated through stage-stacking calculation. And the generated compressor characteristic maps are expressed in the form of polynomial functions by partial least squares regression. After integrating the fitting polynomial functions into the gas turbine performance model, the adaptation of characteristic maps in the performance model by gas-path measurements from the target engine can be made easily, through tuning the fitting polynomial coefficients. Application and analysis have illustrated that the proposed approach can effectively improve the accuracy of the gas turbine thermodynamic model during offdesign performance calculation. The proposed approach exhibits a great potential to tune compressor characteristic maps for both compressor performance modeling diagnosis, with full consideration of their highly nonlinearity.

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Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

Cited by 24 publications

References 24 publications

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis

A component map adaptation method for compressor modeling and diagnosis

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