U-Optimal Accelerated Life Test Scheme Considering Right Censored Data

Di Cao, Juan Wang, Liangqing Feng

2022

Abstract

The purpose of accelerated life test is to promote more failure units of test products in a shorter time, so as to predict the reliability of products under normal conditions based on the failure data collected under accelerated conditions. In view of the right censored data, some statistical optimality is considered. In the framework of generalized linear model, the semi-parametric Cox proportional hazard model is used to obtain the accelerated life test scheme under the U- and D-optimal criteria. From the perspective of prediction variance, the fitting effect is best when the shape parameter 𝛼 is 1.5 in Weibull distribution. The uncertainty of model parameters is evaluated by Monte Carlo technique to verify the feasibility of the test scheme.

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Paper Citation


in Harvard Style

Cao D., Wang J. and Feng L. (2022). U-Optimal Accelerated Life Test Scheme Considering Right Censored Data. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 530-533. DOI: 10.5220/0011751400003607


in Bibtex Style

@conference{icpdi22,
author={Di Cao and Juan Wang and Liangqing Feng},
title={U-Optimal Accelerated Life Test Scheme Considering Right Censored Data},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={530-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011751400003607},
isbn={978-989-758-620-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - U-Optimal Accelerated Life Test Scheme Considering Right Censored Data
SN - 978-989-758-620-0
AU - Cao D.
AU - Wang J.
AU - Feng L.
PY - 2022
SP - 530
EP - 533
DO - 10.5220/0011751400003607
PB - SciTePress