Development and Application of Turbine Engine Health Diagnosis Model

• Main Authors: Yueh-ChenTsai, 蔡岳辰
• Other Authors: Yueh-Heng Li
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/hu9hkw

碩士 === 國立成功大學 === 航空太空工程學系碩士在職專班 === 106 === In general, engine wear is inevitable, even though it gets worse in engine performance over time. There are several mechanisms caused the degradation and potential failures of gas turbine engine, such as accumulation of dirt, oxidation, foreign object damage, worn bearings or seals, excessive blade end clearance, burning or warped turbine blades or blades, blocked fuel nozzles, cracked and warped burners, or cracked rotor disks and blades. In the past, the Gas-path Analysis Method based on mathematical models has been the main method for traditional monitoring engine health and fault diagnosis. However, the existing monitoring and diagnostic software cannot be effectively used in daily maintenance due to various limitations of engine performance parameters and modeling methods. Consequently, this study used data driven method through engine's actual flight or ground data, to reverse modeling of the engine health state and to improve the diagnosis technology, and to confirm that the algorithm model provides a quicker and more effective solution. The data used for model training and testing were NASA's public 90,000-pound thrust commercial engine data set, and a diagnostic system with health classification capabilities was developed. Eventually, this methodology is expected to promote and apply to other turbo machines.