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基于风扇退化的涡扇发动机寿命衰减双参数评估方法研究
郭庆,李印龙,张渝舜
中国民航大学 航空工程学院,天津 300300
摘要:
由于涡扇发动机不同单元体之间存在耦合性,采用单一性能退化参数预测发动机剩余寿命明显是不全面的。本文根据风扇故障导致涡扇发动机退化机理,引入Frank Copula函数描述二元性能参数之间的相关性,并且采用二元非线性Wiener过程来构建性能退化模型,然后基于MCMC(Markov Chain Monte Carlo)方法进行模型参数估计,实现涡扇发动机剩余寿命预测。最终,通过涡扇发动机的仿真数据集来验证该方法的适用性。证明基于Copula函数的二元非线性Wiener过程建模为发动机剩余寿命预测提供了理论基础和技术支持。
关键词:  涡扇发动机  相关失效  Copula函数  二元非线性Wiener  MCMC方法
DOI:10.13675/j.cnki.tjjs.2204055
分类号:V267
基金项目:中国民航大学研究生科技创新基金(2020YJS014);南京航空航天大学中央高校基本科研业务费(NJ2022021)。
A Two-Parameter Evaluation Method of Turbofan Engine Life Decay Based on Fan Degradation
GUO Qing, LI Yin-long, ZHANG Yu-shun
College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China
Abstract:
Due to the coupling between different units of turbofan engines, it is obvious that the use of a single performance degradation parameter to predict the remaining engine life is not comprehensive. Based on the degradation mechanism of turbofan engine caused by fan failure, Frank Copula function is introduced to describe the correlation between binary performance parameters, and a binary nonlinear Wiener process is used to construct a performance degradation model, and then the model parameters are estimated based on MCMC (Markov Chain Monte Carlo) method to achieve the remaining life prediction of turbofan engine. Finally, the applicability of the method is verified by the simulation data set of turbofan engine. It is demonstrated that the binary nonlinear Wiener process modelling based on Copula function provides the theoretical basis and technical support for the remaining engine life prediction.
Key words:  Turbofan engine  Relevant failure  Copula function  Binary nonlinear Wiener  MCMC method