Reliability Assessment of Heavily Censored Data Based on E-Bayesian Estimation

Tianyu Liu (), Lulu Zhang, Guang Jin and Zhengqiang Pan
Additional contact information
Tianyu Liu: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Lulu Zhang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Guang Jin: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Zhengqiang Pan: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2022, vol. 10, issue 22, 1-14

Abstract: The classic E-Bayesian estimation methods can only derive point estimation of the reliability parameters. In this paper, an improved E-Bayesian estimation method is proposed to evaluate product reliability under heavily censored data, which can achieve both point and confidence interval estimation for the reliability parameters. Firstly, by analyzing the concavity & convexity and function characteristics of the Weibull distribution, the value of product failure probability is limited to a certain range. Secondly, an improved weighted least squares method is utilized to construct the confidence interval estimation model of reliability parameters. Simulation results show that the proposed approach can significantly improve the calculation speed and estimation accuracy with just very few robustness reductions. Finally, a real-world case study of the sun gear transmission mechanism is used to validate the effectiveness of the presented method.

Keywords: E-Bayesian estimation method; confidence interval; heavily censored data; sun gear (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/22/4216/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/22/4216/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:22:p:4216-:d:969924

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().