MODELING AND RELIABILITY PREDICTION FOR THE STEP-STRESS DEGRADATION MEASUREMENTS USING NEURAL NETWORKS METHODOLOGY

Abstract

Rapidly evolving accelerated degradation testing (ADT) has provided an efficient channel for reliability assessment without the labor of conventional life testing that observations are complete or incomplete data. In step-stress loading, a specimen is subjected to successively higher level stress until an appropriate termination time. All specimens go through the same specified pattern of stress levels and testing time. A step-stress degradation testing (SSDT) is corresponding to collect the degradation measurements over time from a step-stress life testing before any specimen fail. The advantages of SSDT may shorten the testing time and reduce the test units compared to ADT. Conventional statistical approach for modeling the step-stress degradation process may be complicated. In this paper, we address a nonparametric approach by applying the methodology of neural networks to calibrate the step-stress degradation measurements and conducting the reliability prediction at use condition. A light emitting diode (LED) example is given to illustrate the implementation procedure.