Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing  

作　　者：Yujia WANG Rui KANG Ying CHEN 

机构地区：[1]School of Reliability and Systems Engineering,Beihang University,Beijing 100083,China

出　　处：《Chinese Journal of Aeronautics》2021年第1期252-265,共14页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.61503014 and 61573043)。

摘　　要：The reliability of an Engine Electronic Controller(EEC)attracts attention,which has a critical impact on aircraft engine safety.Reliability assessment is an important part of the design phase.However,the complex composition of EEC and the characteristic of the Phased-Mission System(PMS)lead to the difficulty of assessment.This paper puts forward an advanced approach,considering the complex products and uncertain mission profiles to evaluate the Mean Time Between Failures(MTBF)in the design phase.The failure mechanisms of complex components are deduced by Bayesian Deep Learning(BDL)intelligent algorithm.And copious samples of reliability simulation are solved by cloud computing technology.Based on the result of BDL and cloud computing,simulations are conducted with the Physics of Failure(Po F)theory and Failure Behavior Model(FBM).This reliability assessment approach can evaluate MTBF of electronic products without reference to physical tests.Finally,an EEC is applied to verify the effectiveness and accuracy of the method.

关 键 词：Engine electronic controllers Cloud computing Bayesian deep learning UNCERTAINTY Reliability assessment 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V233.7[自动化与计算机技术—控制科学与工程]