A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test-Reference-Cited by-同舟云学术

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A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test

Published:2024-02 Issue:4 Volume:10 Page:e26429
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Li Yaqiu^ORCID,Zhou Qijie,Fan Ye,Pan Guangze^ORCID,Dai Zongbei,Lei Baimao

Funder

Ministry of Industry and Information Technology of the People's Republic of China

Department of Science and Technology of Guangdong Province

Guangzhou Municipal Science and Technology Bureau

Basic and Applied Basic Research Foundation of Guangdong Province

Guangzhou Municipal Science and Technology Program key projects

Publisher

Elsevier BV

Reference26 articles.

1. Accelerated degradation testing with long-term memory effects;Si;Ieee T Reliab,2020

2. A theoretical framework for accelerated testing;LuValle,2000

3. Development of accelerated reliability test cycle for electric drive system based on vehicle operating data;Wang;Eng. Fail. Anal.,2022

4. Reliability estimation from two types of accelerated testing data considering measurement error;Ma;Reliab. Eng. Syst. Saf.,2020

5. Accelerated aging effect on high temperature vulcanized silicone rubber composites under DC voltage with controlled environmental conditions;Mehmood;Eng. Fail. Anal.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multidimensional Perspective to Data Preprocessing for Model Cognition Verity;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2024-05-14

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