CSADT life prediction based on DAD using time series method

For long lifetime and high reliability products, it is difficult to obtain failure time data in a short time period. Hence, Accelerated Degradation Testing (ADT) is presented to deal with the cases that no failure time data could be obtained but degradation data of the primary parameter of the product are available. At present, there are mainly two ways to predict product life and reliability by ADT: one is based on degradation path, that is, product life prediction is obtained by prediction of each sample degradation path; the other is based on Degradation Amount Distribution (DAD), that is, product life prediction is obtained by prediction of all samples DAD parameters. Most previous works use deterministic model to represent the degradation path or parameters of DAD. However, long-term life prediction must take into account the stochastic and periodic nature of environmental variables. A few literatures study ADT life prediction using time series method for its excellent capable of stochastic and periodic information mining. However, life predictions using time series method in present literatures are all based on degradation path. Due to several special advantages of life prediction based on DAD, such as it can be used in random failure threshold situation, which is common situation in practice, it is important to study ADT life prediction based on DAD using time series method.