NDLI: Life and reliability forecasting of the CSADT using Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Shuzhen Li Xiaoyang Li Tongmin Jiang
Copyright Year	2010
Description	Author affiliation: Dept. of System Engineering, Beihang University, China (Shuzhen Li; Xiaoyang Li; Tongmin Jiang)
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
Starting Page	1
Ending Page	6
File Size	389721
Page Count	6
File Format	PDF
ISBN	9781424451029
ISSN	0149144X
DOI	10.1109/RAMS.2010.5447978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-01-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Degradation Life estimation Artificial neural networks Life testing Predictive models Stress Acceleration Support vector machine classification Data engineering SVM Accelerated degradation testing life prediction reliability
Content Type	Text
Resource Type	Article

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