Life and reliability forecasting of the CSADT using Support Vector Machines

Abstract

Accelerated Degradation Testing (ADT) is now adopted frequently to verify the reliability and life of high-reliable, long-life product. But ADT data analysis methods are still deficiency. Due to the excellent capable of little sample learning and nonlinear mapping, SVM prediction model is widely used in many fields. In this paper, a new degradation prediction method based on Support Vector Machines (SVM) is proposed and developed to predict time-to-failure of product. This prediction method is also compared with BPANN and regression methods to validate its effectiveness. Moreover, Constant Stress ADT is studied and ADT data are divided into several sets of performance degradation under different stress levels. Using SVM prediction method, all degradation processes are predicted to failure and lifetimes are obtained easily, then life and reliability under normal condition are evaluated by accelerated model. Simulation case demonstrates that the life and reliability prediction for CSADT based on SVM is reasonable and validity