期刊
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
卷 23, 期 3, 页码 677-685出版社
KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-008-1120-3
关键词
Defect diagnostics; Gas turbine engine; Hybrid method; Module system; Off-design condition
资金
- Ministry of Commerce, Industry and Energy (MOCIE)
- Korea Aerospace Research institute (KARI)
- Korea Evaluation Institute of Industrial Technology (KEIT) [F0003003-2009-31] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
A hybrid method of an artificial neural network (ANN) and a support vector machine (SVM) has been used for a health monitoring algorithm of a gas turbine engine. The method has the advantage of reducing teaming data and converging time without any loss of estimation accuracy, because the SVM classifies the defect location and reduces the teaming data range. In off-design condition, however, the operation region of the engine becomes wide and the nonlinearity of teaming data increases considerably. Therefore, an improved hybrid method with the module system and the advanced SVM has been suggested to solve the problems. The module system divides the whole operating region into reasonably small-sized sections, and the advanced SVM has two steps of the classification. The proposed algorithm has been proven to reliably and effectively diagnose the Simultaneous defects of the triple components as well as the defects of the single and dual components of the gas turbine engine in off-design condition.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据