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.
