Modeling and reliability prediction for the step-stress degradation measurements using neural networks methodology

Dong Shang Chang, Chih Chou Chiu, Shwu Tzy Jiang

研究成果: 雜誌貢獻期刊論文同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)277-288
頁數12
期刊International Journal of Reliability, Quality and Safety Engineering
6
發行號3
DOIs
出版狀態已出版 - 1999

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