Statistical Bayesian method for reliability evaluation based on ADT data

2017 2nd International Conference on Reliability Engineering

Lu, Dawei^1 ; Wang, Lizhi^2 ; Sun, Yusheng^1 ; Wang, Xiaohong^1

School of Reliability and System Engineering, Beihang University, China^1

Unmanned System Institute, Beihang University, China^2

关键词: Accelerated degradation testing (ADT);    Acceleration models;    Estimation parameters;    Normal operating conditions;    Parameter inference;    Posterior distributions;    Reliability Evaluation;    Stochastic process model;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/351/1/012008/pdf
DOI  :  10.1088/1757-899X/351/1/012008

来源: IOP

【 摘 要 】

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products' reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen; Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values; Third, the lifetime and reliability values are estimated on the basis of the estimation parameters; Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.