Fault diagnosis using particle filter for MEA typical components

The Journal of Engineering

Hui Yannian¹  Li Hongliang²  Qu Jianglei³ 

[1] Technology Research Institute of COMAC, more electric systems department , Beijing , People'Beijing Aeronautical Science &s Republic of China

关键词: electric aircraft;    SIR particle filtering state estimation;    prognostic health management;    utilisation;    electrical power;    environmental impact;    modern aerospace engineering;    aircraft weight;    fault diagnosis;    operation cost;    particle filter;    MEA typical components;    developing trend;   

DOI  :  10.1049/joe.2018.0028

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

More electric aircraft (MEA) is a developing trend in modern aerospace engineering aiming for a reduction of the aircraft weight, operation cost and environmental impact through putting more emphasis on the utilisation of electrical power. It has many advantages, but also increases the complexity of the aircraft. Therefore, the requirements of prognostic and health management for MEA are needed. The method that using sequential importance re-sampling (SIR) particle filtering state estimation and smoothed residual to diagnose fault for typical components is discussed. The simulation results show that this method can locate faults accurately and quickly.

【 授权许可】

CC BY