An improved hybrid modeling method based on extreme learning machine for gas turbine engine

Abstract 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 innovatively formulated by combining a nonlinear physics-based model (PBM), a series of residual learning models (RLMs) and an input selection module for each RLM. The PBM is constructed based on component level method while the RLMs are constructed based on extreme learning machine (ELM). Particularly, the parameters applied to train the RLMs are selected independently in a customized way based on the input selection module. The performance of the proposed method is illustrated through a series of simulations considering engine-to-model mismatch, sensor measurement uncertainty and verified through a group of ground test data. The results demonstrate that the proposed IHM method can improve modeling accuracy effectively compared with PBM. In addition, through incorporating input selection module, the RLMs in IHM have better performance than using traditional input selection way. Meanwhile, when compared to other networks, the ELM-based RLM guarantees faster training speed while achieving better residual learning effect, and shows more effectiveness and superiority.