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Collaborative Research: Maintenance Planning for Complex Systems in Dynamic Environments

协作研究：动态环境中复杂系统的维护规划

基本信息

批准号：
1728321
负责人：
Qianmei Feng
金额：
$ 34.81万
依托单位：
University of Houston
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728321&HistoricalAwards=false
关键词：
Collaborative Research Maintenance Planning Complex

项目摘要

Equipment failures in capital-intensive industries, such as oil and gas exploration, aerospace, and power generation, may threaten human lives and have significant environmental and economic impact. Many of these equipment failures can be traced to poor equipment maintenance. One criticism of existing maintenance planning is that the existing predictive failure models do not accurately reflect degradation resulting from multiple causes in dynamic environments. This project addresses the need for better planning models and analysis to enhance equipment reliability in capital-intensive industries. The PIs have established collaborations with industrial partners to ensure the relevance of their research. Educational opportunities for students, including outreach to underrepresented minorities, is also supported by the award. This award will support research on a general and systematic methodology for effective reliability modeling and maintenance planning for critical complex systems in capital-intensive industries. To successfully accomplish the goal, four specific objectives will be achieved: (1) developing a broad class of general stochastic models that can integrally handle the complexities of degradation processes under dynamic environments; (2) constructing general stochastic-modulated multi-dimensional stochastic processes to model the multiple dependent degradation processes under dynamic environments; (3) creating a general mixture degradation framework to capture heterogeneities at both population and unit-levels; and (4) developing a unified maintenance decision-making framework that jointly integrates long-term maintenance planning and short-term dynamic decisions. Successful development of these models will lead to fundamentally new perspectives on the application of reliability and maintenance optimization for complex engineered systems.

石油和天然气勘探、航空航天和发电等资本密集型行业的设备故障可能威胁人类生命，并对环境和经济产生重大影响。这些设备中的许多故障都可以追溯到设备维护不善。对现有维修计划的一个批评是，现有的预测故障模型不能准确地反映动态环境中多种原因造成的退化。该项目解决了对更好的规划模型和分析的需求，以提高资本密集型行业的设备可靠性。私人投资机构与工业伙伴建立了合作关系，以确保其研究的相关性。该奖项还支持为学生提供教育机会，包括接触代表人数不足的少数群体。该奖项将支持对资本密集型行业中关键复杂系统的有效可靠性建模和维护计划的一般和系统方法的研究。为了成功地实现这一目标，将实现四个具体目标：(1)开发一类广泛的通用随机模型，能够完整地处理动态环境下退化过程的复杂性；(2)构建通用随机调制多维随机过程，以模拟动态环境下的多个相关退化过程；(3)创建一个通用的混合退化框架，以捕捉种群和单元水平的异质性；以及(4)建立一个联合集成长期维修计划和短期动态决策的统一维修决策框架。这些模型的成功开发将为复杂工程系统的可靠性和维护优化的应用带来全新的视角。