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

• Published in: Mathematical problems in engineering Vol. 2015; no. 2015; pp. 1 - 12
• Main Authors: Li, Jing-chao, Li, Shuying, Cao, Yunpeng, Ying, Yulong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Degradation; Diagnostic systems; Efficiency; Engine noise; Fault detection; Gas path analysis; Gas turbine engines; Health; Marine engines; Mathematical models; Mathematical problems; Methods; Noise levels; Noise measurement; Noise monitoring; Nonlinearity; Optimization algorithms; Particle swarm optimization; Performance degradation; Steady state models; China
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2015/940757

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.