NDLI: A prediction method of life and reliability for CSALT using Grey RBF neural networks

Content Provider	IEEE Xplore Digital Library
Author	Shuzhen Li Xiaoyang Li Tongmin Jiang
Copyright Year	2009
Description	Author affiliation: Department of System Engineering, Beihang University, Beijing, China (Shuzhen Li; Xiaoyang Li; Tongmin Jiang)
Abstract	There are two problems of the traditional life and reliability estimation methods of Accelerated Life Test (ALT): one is the difficulty to establish the accelerated model and another is the complex computing of multiple likelihood equations. In this paper, we proposed a new prediction method of life and reliability for the constant stress accelerated life test using Grey RBF Neural Network. The accelerated stress levels and reliability are used as the training input vectors, while well-regulated failure data operated by Grey Accumulated Generate Operation (AGO) principle as training target vectors. Then RBF neural net is established and trained. Eventually, the failure data under normal stress can be predicted by putting the normal stress levels and the reliability into the model, and reliability curves can be drawn if life distribution is known. A simulation case is conducted and results are compared to that of BP algorithm, which demonstrates the validation of this model.
Starting Page	699
Ending Page	703
File Size	211970
Page Count	5
File Format	PDF
ISBN	9781424436712
DOI	10.1109/ICIEEM.2009.5344500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Life testing Prediction methods Accelerated life testing Reliability theory Predictive models Neural network Stress Equations BP Reliability prediction Neural networks Life estimation RBF Grey system theory Acceleration Mathematical model
Content Type	Text
Resource Type	Article

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