Research On Online Reliability Estimation And Residual Life Prediction Method For On-board Electronic Equipments

Aircraft power supply system usually consists of the main power source, the secondary powersupply, emergency power supply and auxiliary power supply. In fact, switch mode power supply（SMPS） is used as secondary power supply which support the direct current for electronicequipment, while accumulator is used to start the engine and Auxiliary Power Unit（APU）as theemergency or auxiliary power supply when the generator fails. The performance of SMPS and batterywill directly affect the safety of aircraft in the air. Therefore, it is essential to accurately estimate thereliability and remaining useful life prediction of aircraft SMPS and battery which will guarantee thesystem without malfunction and complete the tasks. The achievements in this paper play a great rolein theory research and engineering application.This paper mainly focuses on the research of reliability estimation and remaining useful lifeprediction of aircraft secondary power supply, such as SMPS and battery. The research contentcontains the following three aspects,（1）The equivalent failure models of key components of power electronic circuits are studied.Analyzing the effect of power electronic circuits during the degradation of each component, therelative change of ripple coefficients is definied as the circuit-level failure characteristic parameters.（2）The research on reliability estimation based on Wiener progress has been conducted.. Inorder to overcome the difficulties of less available data and low prediction accuracy for the individual,the method based on bootstrap is proposed which establishes the individual Wiener progress andadopts the Bayesian framework to combine prior information of history data and posterior distribution.Thereby, it can realize online reliability estimation of individual accurately and precisely. Todemonstrate the approach, the method without prior information is used to compare the estimationresults. Furthermore, the data from BUCK circuit and Lithium-ion battery are used to illustrate thefeasibility and applicability.（3）Amethod of online residual life prediction based on nonlinear degradation model is studied.To illustrate the nonlinear degradation characteristic of the product, the nonlinear degradation modelbased on diffusion progress is developed. Drift parameter of the degradation model is determined asstate variable while the performance degradation data is regarded as measurement variable.Afterwards, the suitable state space model is developed. Meanwhile, the state variable and the driftparameter are updated by kalman filter algorithm. Moreover, the residual life is calculated in terms ofits distribution function. Finally, the degradation of the BUCK circuit is analyzed by the proposedmethod compared with the linear model, the results show that the efficiency and accuracy of themethod used in the paper.