An improved hybrid modeling method based on extreme learning machine for gas turbine engine
作者： Maojun Xu , Jian Wang , Jinxin Liu , Ming Li , Jia Geng
DOI: 10.1016/J.AST.2020.106333
关键词:
摘要: Engine model plays a critical role in many applications of gas turbine engine, especially for fault tolerant control and gas-path prognostic and health management. However, traditional pure physics-based modeling method has difficulty in dealing with engine-to-model mismatch, sensor measurement uncertainty, etc., leading to a big limitation in real applications. In this paper, a novel improved hybrid modeling (IHM) method is proposed to accurately model a dual spool turbofan gas turbine engine. The proposed IHM method is …
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