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.

Fuzzy-Based Gas Turbine Engine Fuel Controller Design Using Particle Swarm Optimization

2011 ◽  
Vol 110-116 ◽  
pp. 3215-3222 ◽  
Author(s):  
M. Montazeri-Gh ◽  
E. Mohammadi ◽  
S. Jafari
Keyword(s):  
Particle Swarm Optimization ◽  
Gas Turbine ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Gas Turbine Engine ◽  
Time Response ◽  
Rule Base ◽  
Engine Fuel ◽  
Swarm Optimization ◽  
Turbine Engine

This paper presents the application of Particle Swarm Optimization (PSO) algorithm for optimization of the Gas Turbine Engine (GTE) fuel control system. In this study, the Wiener model for GTE as a block structure model is firstly developed. This representation is an appropriate model for controller tuning. Subsequently, based on the nonlinear GTE nature, a Fuzzy Logic Controller (FLC) with an initial rule base is designed for the engine fuel system. Then, the initial FLC is tuned by PSO with emphasis on the engine safety and time response. In this study, the optimization process is performed in two stages during which the Data Base (DB) and the Rule Base (RB) of the initial FLC are tuned sequentially. The results obtained from the simulation show the ability of the approach to achieve an acceptable time response and to attain a safe operation by limiting the turbine rotor acceleration.

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 Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

A Highly Robust Thrust Estimation Method with Dissimilar Redundancy Framework for Gas Turbine Engine

2021 ◽  
Author(s):  
Hang Zhao ◽  
Zengbu Liao ◽  
Jinxin Liu ◽  
Ming Li ◽  
Wei Liu ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Estimation Method ◽  
Gas Turbine Engine ◽  
Turbine Engine

Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

2021 ◽  
Author(s):  
Hongmei Xu ◽  
Juan Liu ◽  
Kun Wang ◽  
Songtao Kong ◽  
Yong Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Properties ◽  
Measurement Errors ◽  
Fuzzy Inference ◽  
Quantum Particle ◽  
Estimation Method ◽  
Particle Swarm ◽  
Temperature Dependent ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization

2014 ◽  
Vol 1051 ◽  
pp. 1004-1008 ◽  
Author(s):  
Gui Fang Guo ◽  
Lin Shui ◽  
Xiao Lan Wu ◽  
Bing Gang Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Battery Management System ◽  
Battery Management ◽  
Swarm Optimization ◽  
Cycle Number ◽  
Soc Estimation ◽  
Discharge Operation

State of charge (SOC) is very important parameter for monitoring the battery charge and discharge operation and estimating the drive distance of electric vehicle. Especially, with the cycle number increasing, the precision estimation of SOC for battery management system is still not well resolved. Therefore, in this study, aim at accurate sampling of voltage, current and temperature signals based on LTC6803-3 chip, the paper proposed a support vector machine (SVM) optimized by particle swarm optimization (PSO) to improve SOC estimation accuracy. The results demonstrate that the proposed PSO-SVM model has good forecasting performance.

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