Gas Turbine Modeling for Diagnosis and Control

Larsson, Emil; Åslund, Jan; Frisk, Erik; Eriksson, Lars

doi:10.1115/1.4026598

Cited by 15 publications

(14 citation statements)

References 16 publications

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“…Here, in order to eliminate the inconvenience stated above, the health parameters for gas path components can be redefined using component similar performance parameters (e.g., corrected mass flow rate /  for compressor and turbine), where the same treatment can be found in the treatment of characteristic maps for compressor and turbine [5].…”

Section: Improved Definition For Engine Health Parametersmentioning

confidence: 99%

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Li¹

2018

IJPE

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Gas turbine engines always run during poor working conditions that have high temperatures, high pressures, and high mechanical and thermal stress. Thus, the performance of the gas path components gradually degrades, leading to serious faults. So, the health status of the engine gas path components provides essential information for users and operators. Here, a new gas path analysis approach has been developed to predict gas turbine engine health status by using gas path measurements. The developed approach has been tested in seven test cases where the degradation of a model gas turbine engine similar to a three-shaft marine engine has been analyzed. The case studies have shown that the approach can accurately and quickly detect, isolate, and quantify the degradation of major engine gas path components with the existence of measurement noise. The test cases have also shown that the time cost by the approach is short enough for its potential application of online health monitoring.

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Section: Improved Definition For Engine Health Parametersmentioning

confidence: 99%

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Li¹

2018

IJPE

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“…More detailed information about gas turbine performance modeling were described in Ying et al 20 and Larsson et al 21 …”

Section: Component Health Parametermentioning

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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“…Condition-monitoring plays an increasingly important role for technical processes in order to improve reliability, availability, maintenance and lifetime of equipment [3]. The detection of engine performance and gas path performance analysis are considered to be effective approaches to enhance the reliability and safety of the turbofan engine [4,5]. Additionally, the detection systems can also be used for the scheme of on-condition maintenance for the purpose of decreasing the maintenance costs [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

Lü

Huang

2018

Energies

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Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components' performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg-Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

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Gas Turbine Modeling for Diagnosis and Control

Cited by 15 publications

References 16 publications

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

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

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

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