Collaborative Research: Adaptive Maintenance Planning Based on Evolving Residual Life Distributions

协作研究：基于演化剩余寿命分布的自适应维护规划

• 批准号: 0856192
• 负责人: Nagi Gebraeel
• 金额: $ 17.45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0856192&HistoricalAwards=false
• 关键词: Collaborative; Research; Adaptive; Maintenance; Planning

This grant provides funding for the development of broadly applicable analytical and statistical tools that determine adaptive maintenance policies for complex systems that deteriorate over time. In contrast to existing techniques, these new mathematical models will link low-level, sensor-based condition data with high-level maintenance decision making. In particular, the techniques will determine how often condition-based data should be collected, when repairs or replacements of critical components should take place, and when spare parts should be acquired in anticipation of impending failures. The resulting policies will be adaptive in nature, meaning that they will revise the timing of maintenance actions based on observed data. For individual components, Bayesian statistical techniques will be developed to model degradation patterns and the evolving residual life distribution of the component. At the system level, environmental data will be used to develop versatile stochastic failure models to estimate the system's residual life distribution. For both cases, Markov decision process models that effectively convert these residual life distributions into cost-optimal, adaptive maintenance policies will be analyzed. Laboratory experiments will be performed to assess the applicability of the techniques to real problems and to validate the models. If successful, this research will improve the way that firms translate vast quantities of condition monitoring data into maintenance decisions. Determining the optimal timing of data collection, repairs and replacements, and spare parts ordering is vital to the maintenance of engineering systems including manufacturing systems, aging infrastructure, aviation systems, and many others. Performing the right type of maintenance activity at the right time will reduce maintenance costs while improving safety.

这笔拨款为开发广泛适用的分析和统计工具提供了资金，这些工具可以为随着时间的推移而恶化的复杂系统确定适应性维护策略。与现有技术相比，这些新的数学模型将低级的、基于传感器的状态数据与高级维护决策联系起来。特别是，这些技术将决定应多久收集一次基于状态的数据，何时应进行关键部件的修理或更换，以及何时应在预期即将发生故障时获取备件。由此产生的策略本质上是自适应的，这意味着它们将根据观察到的数据修改维护操作的时间。对于单个组件，将开发贝叶斯统计技术来模拟组件的退化模式和不断变化的剩余寿命分布。在系统层面，环境数据将用于开发多功能随机失效模型，以估计系统的剩余寿命分布。对于这两种情况，将分析有效地将这些剩余寿命分布转换为成本最优、自适应维护策略的马尔可夫决策过程模型。将进行实验室实验，以评估技术对实际问题的适用性，并验证模型。如果成功，这项研究将改善企业将大量状态监测数据转化为维护决策的方式。确定数据收集、维修和更换以及备件订购的最佳时间对于包括制造系统、老化基础设施、航空系统和许多其他系统在内的工程系统的维护至关重要。在正确的时间执行正确类型的维护活动将降低维护成本，同时提高安全性。