Collaborative Research: Maintenance Planning for Complex Systems in Dynamic Environments

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，并产生重大的环境和经济影响。 许多设备故障可以追溯到设备维护不善。 现有的维修规划的一个批评是，现有的预测故障模型不准确地反映退化所造成的多种原因在动态环境中。 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。 PI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，包括向代表性不足的少数群体开展外联活动。 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规划的一般和系统方法的研究。 为了成功地实现这一目标，我们将实现以下四个具体目标：（1）建立一个能够完整地处理动态环境下退化过程复杂性的广义随机模型：（2）构造广义随机调制的多维随机过程来模拟动态环境下的多个相关退化过程;（3）建立一个通用的混合物退化框架，以捕获总体和单元级别的异质性;（4）开发一个统一的维护决策框架，该框架将长期维护规划和短期动态决策结合起来。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应用带来全新的视角。

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.