@inproceedings{bfd8730d810341cebd02d80e0169f0a4,
title = "Improved AIUKF based aeroengine component deterioration analysis",
abstract = "Aeroengines are complex aerodynamic thermal systems that operate in harsh environments for long periods of time. To ensure engine reliability and stability, accurate and effective prediction of engine deterioration is critical. At present, most of the filtering methods for predicting the degree of deterioration of engine components have shortcomings such as large initial state error and limited estimation accuracy. This paper proposes an improved adaptive iteration Unscented Kalman Filter (AIUKF) algorithm, combining forgetting factor with AIUKF to predict the degree of engine deterioration. The simulation results show that the method can simulate the engine deterioration process with high precision, effectively predict the deterioration degree of each component. This method has strong ability to predict nonlinear systems, and improves the accuracy of predicting engine deterioration.",
keywords = "Adaptive iteration, Component deterioration, Unscented Kalman Filter",
author = "Linfeng Gou and Yawen Shen and Xianyi Zeng and Wenxin Shao and Zihan Zhou",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866106",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5161--5166",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
}