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

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yulong Ying ◽  
Yunpeng Cao ◽  
Shuying Li ◽  
Jingchao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Health Status ◽  
Gas Turbine ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Measurement Noise ◽  
Swarm Optimization ◽  
Turbine Engine ◽  
Steady State Model

In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA) method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA) has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.

scholarly journals Two-Dimensional Steady-State Boundary Shape Inversion of CGM-SPSO Algorithm on Temperature Information

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Shoubin Wang ◽  
Huangchao Jia ◽  
Xiaogang Sun ◽  
Li Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Optimization Algorithm ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Two Dimensional ◽  
Swarm Optimization

Addressing the problem of two-dimensional steady-state thermal boundary recognition, a hybrid algorithm of conjugate gradient method and social particle swarm optimization (CGM-SPSO) algorithm is proposed. The global search ability of particle swarm optimization algorithm and local search ability of gradient algorithm are effectively combined, which overcomes the shortcoming that the conjugate gradient method tends to converge to the local solution and relies heavily on the initial approximation of the iterative process. The hybrid algorithm also avoids the problem that the particle swarm optimization algorithm requires a large number of iterative steps and a lot of time. The experimental results show that the proposed algorithm is feasible and effective in solving the problem of two-dimensional steady-state thermal boundary shape.

scholarly journals A Novel Gas Turbine Engine Health Status Estimation Method Using Quantum-Behaved Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xinyi Yang ◽  
Wei Shen ◽  
Shan Pang ◽  
Benwei Li ◽  
Keyi Jiang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Health Status ◽  
Gas Turbine ◽  
Estimation Method ◽  
Particle Swarm ◽  
Gas Turbine Engine ◽  
Performance Model ◽  
Estimation Accuracy ◽  
Swarm Optimization ◽  
Turbine Engine

Accurate gas turbine engine health status estimation is very important for engine applications and aircraft flight safety. Due to the fact that there are many to-be-estimated parameters, engine health status estimation is a very difficult optimization problem. Traditional gas path analysis (GPA) methods are based on the linearized thermodynamic engine performance model, and the estimation accuracy is not satisfactory on conditions that the nonlinearity of the engine model is significant. To solve this problem, a novel gas turbine engine health status estimation method has been developed. The method estimates degraded engine component parameters using quantum-behaved particle swarm optimization (QPSO) algorithm. And the engine health indices are calculated using these estimated component parameters. The new method was applied to turbine fan engine health status estimation and is compared with the other three representative methods. Results show that although the developed method is slower in computation speed than GPA methods it succeeds in estimating engine health status with the highest accuracy in all test cases and is proven to be a very suitable tool for off-line engine health status estimation.

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