Health Parameters Estimation Based on Hybrid Model For Turbo-shaft Engine

摘要：

A hybrid model is proposed for health parameter estimation of turbo-shaft engine. The hybrid model is composed of the physical model based on Kalman filter and the empirical model based on neural networks. Gaussian mixture model (GMM) is used to cluster the flight data, on-board neural network training is realized in real-time. GMM not only reduces the data storage capacity, but also accelerates speed of neural network training after the end of the flight. Updated neural network model makes the Kalman filter expanding operation range. Simulation results show that the hybrid model estimate health parameters in flight envelope with satisfactory accuracy.

关键词： turbo-shaft engine health parameters estimation kalman filter neural network gaussian mixture model (gmm)

作者: HUANG Jinquan SHE Yunfeng LU Feng

作者单位: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China

会议类型: 国际会议

会议名称: The 3rd International Symposium on Jet Propulsion and Power Engineering(第三届喷气推进与动力工程国际会议 ISJPPE)

会议地点: 南京

会议语种:英文

页码: 602-606

在线出版日期: 2010-09-13（万方平台首次上网日期，不代表论文的发表时间）