Open AccessA Modeling Method of Stochastic Parameters’ Inverse Gauss Process Considering Measurement Error under Accelerated Degradation Test
Author(s) -
Xiaoping Li,
Zhenyu Wu,
Dejun Cui,
Baining Guo,
Lijie Zhang
Publication year - 2019
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2019/9752920
Subject(s) - monte carlo method , unobservable , reliability (semiconductor) , acceleration , inverse , degradation (telecommunications) , inverse problem , process (computing) , constant (computer programming) , accelerated life testing , gauss , stochastic process , computer science , observational error , mathematics , algorithm , mathematical optimization , statistics , econometrics , physics , power (physics) , telecommunications , mathematical analysis , geometry , classical mechanics , quantum mechanics , weibull distribution , programming language , operating system
To solve the problem that the individual differences and the measurement errors affect the accuracy of life estimation in accelerated degradation test, the inverse Gauss process with stochastic parameters is applied in the accelerated degradation test with the consideration of the influence of individual differences, and the analysis of measurement uncertainty is carried out. An inverse Gauss accelerated degradation model considering both individual differences and measurement errors is established. In the maximum likelihood estimation of parameters, Genetic Algorithm and Monte Carlo integral are used to solve the problems caused by complex integral and the unobservable measurement errors in the calculation process. Finally, the proposed method is verified by the Monte Carlo simulation under the constant accelerated stress and step accelerated stress and the illustrative example of electrical connectors under the constant acceleration stress, respectively. The results show that the modeling tool is useful for improving the accuracy of the life prediction and the reliability evaluation.