摘要
A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product's performance.However,in some cases,the measurement process may exert extra stress on products being measured.To obtain trustful results in such a situation,a new degradation model was derived.Then,by fusing the prior information of product and its own on-line degradation data,the real-time reliability was evaluated on the basis of Bayesian formula.To make the proposed method more practical,a procedure based on expectation maximization (EM) algorithm was presented to estimate the unknown parameters.Finally,the performance of the proposed method was illustrated by a simulation study.The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results,if the damaged measurement process is involved.
A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data. Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product＇s performance. However, in some cases, the measurement process may exert extra stress on products being measured. To obtain trustful results in such a situation, a new degradation model was derived. Then, by fusing the prior information of product and its own on-line degradation data, the real-time reliability was evaluated on the basis of Bayesian formula. To make the proposed method more practical, a procedure based on expectation maximization （EM） algorithm was presented to estimate the unknown parameters. Finally, the performance of the proposed method was illustrated by a simulation study. The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results, if the damaged measurement process is involved.
基金
Project(60904002)supported by the National Natural Science Foundation of China
