A stochastic model for RUL prediction of subsea pipeline subject to corrosion-fatigue degradation
Author:
Publisher
Elsevier BV
Subject
Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering
Reference66 articles.
1. Predictive deep learning for pitting corrosion modeling in buried transmission pipelines;Akhlaghi;Process Saf. Environ. Prot.,2023
2. Fracture Mechanics: Fundamentals and Applications;Anderson,2017
3. Developing a dynamic model for pitting and corrosion-fatigue damage of subsea pipelines;Arzaghi;Ocean Eng.,2018
4. Stochastic process corrosion growth models for pipeline reliability;Bazán;Corros. Sci.,2013
5. Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study;Cai;Comput. Ind. Eng.,2021
Cited by 12 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. Remaining useful life prediction based on multi-stage Wiener process and Bayesian information criterion;Computers & Industrial Engineering;2024-10
2. An interpretable machine learning-based pitting corrosion depth prediction model for steel drinking water pipelines;Process Safety and Environmental Protection;2024-10
3. Towards proactive corrosion management: A predictive modeling approach in pipeline industrial applications;Process Safety and Environmental Protection;2024-10
4. Reliability analysis of multi-component systems subjected to dependent degradation processes and random shocks in dynamic environments;Process Safety and Environmental Protection;2024-10
5. Machine learning-aided risk-based inspection strategy for hydrogen technologies;Process Safety and Environmental Protection;2024-09
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3