Open AccessFault diagnosis using particle filter for MEA typical components
Author(s) -
Hongliang Li,
Yannian Hui,
Jianglei Qu,
Haocheng Sun
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.0028
Subject(s) - particle filter , aerospace , residual , reliability engineering , computer science , fault (geology) , sampling (signal processing) , state (computer science) , filter (signal processing) , reduction (mathematics) , state of health , power (physics) , automotive engineering , engineering , aerospace engineering , algorithm , mathematics , geometry , seismology , geology , computer vision , physics , battery (electricity) , quantum mechanics
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.