Life-prediction of the CSADT based on BP algorithm of ANN

One of the main problems encountered when we study the reliability of high-quality, long-life products by traditional means is that it is difficult to obtain adequate failure data in the life testing, for example, cost and time are always limited. For many of these products, an underlying degradation process is the root cause of failures. In order to get useful information in a short time, accelerated degradation testing (ADT) is frequently used. Using multi-stress in the ADT, we not only reduce time and cost of the testing, and increase efficiency, but we also simulate the actual environmental conditions more accurately and obtain more credible results. However, to achieve these results, the accelerated model must be established. Unfortunately, it is often quite difficult to give a certain physical or chemical model and quantify the degradation process, because the failure mechanisms of different stresses are various. In this paper, a new model is developed to predict the life of items in the constant stress accelerated degradation testing (CSADT) based on Back-Propagation (BP) Algorithm of Artificial Neural Network (BPANN). With this BPANN model, unlike other degradation analysis methods, this acceleration model avoids complicated calculations. It provides a new approach to the life-prediction of the ADT.