Collaborative Research: Multi-Level Data Fusion for Real-Time Prognostic Health Management of Hierarchical Systems

该奖项的研究目标是为分层工程系统开发一种新的实时性能和健康管理（PHM）方法。这种新的方法是基于从系统的不同层次上的组件和子系统收集的数据的融合。第一个努力是建立一个离线模型系统，数学集成从单独的组件级的生存模型，其中每一个代表的影响因素，如环境和压力，对一个组件的故障危险。离线模型系统，然后与在线系统性能测量，制定一个状态空间模型，描述了系统的性能退化，作为一个观察到的常规不可观察的危险状态演变。基于状态空间模型，将统计过程控制和非线性滤波技术相结合，发展在线监测和故障诊断方法。此外，根据监测和故障诊断的结论，进行维修计划和资源分配的实时优化决策。这些方法将通过行业支持者提供的真实的数据、案例研究和测试平台进行验证和实施。如果成功的话，本研究的结果将通过为分层工程系统的实时PHM过程贡献新的概念、标准和算法来推进最先进的方法。所开发的方法可应用于旨在满足关键任务行业（如运输，能源，基础设施和制造业）中某些高度复杂和高级功能需求的系统。研究结果的传播将大大改善对此类系统故障的理解和预测。因此，可以预期改进的实时PHM实践，实现增加的系统可用性和降低的维护成本。此外，这种合作研究的跨学科性质将使学生受益，使他们接触到新的课程模块和研究机会，涉及学习和应用可靠性，数据挖掘和统计方面的先进方法。

The research objective of this award is to develop a new real-time prognostics and health management (PHM) methodology for hierarchical engineering systems. This new methodology is based on the fusion of data collected from components and subsystems on different levels of a system. The first effort is to establish an offline model system mathematically integrated from separate component-level survival models, each of which represents the impacts of factors, such as environments and stress, on a component's failure hazard. The offline model system is then incorporated with the online system performance measurements to formulate a state space model that describes the system performance degradation as an observation of conventionally unobservable hazard state evolvement. Based on the state space model, methodologies of online monitoring and prognostics will be developed by combining statistical process control and nonlinear filtering techniques. In addition, real-time decision of maintenance scheduling and resource allocation will be optimally conducted according to the monitoring and prognostics conclusions. These methodologies will be validated and implemented with the real data, case studies and testbed provided by industry supporters. If successful, the results of this research will advance the state-of-the-art methodologies by contributing new concepts, criteria and algorithms to the course of real-time PHM of hierarchical engineering systems. The developed methodology can be applied to systems that are designed to meet certain highly complex and advanced functional demands in mission-critical industries, such as transportation, energy, infrastructure, and manufacturing. The dissemination of the research results will significantly improve the understanding and prediction of failures of such systems. Consequently, improved real-time PHM practice can be expected, achieving increased system availability and reduced maintenance cost. In addition, the interdisciplinary nature of this collaborative research will benefit students by exposing them to new course modules and research opportunities that involve learning and applying advanced methodologies in reliability, data mining and statistics.