I/UCRC FRP: Collaborative Research on Event-based Analytics for Enhanced Prognostics Design in a Big Data Environment

拟议的工作旨在研究基于事件的建模，以处理高维和异构的数据环境，以增强自适应控制的数据收集和快速维护决策的自动化设计，将数据分析应用到软件开发过程中。基于事件的方法进行了探讨，其潜力，以减少时间冗余，同时保持机器的动态。基于事件的方法还没有被完全探索用于具有连续信号输入（诸如传感器测量）的自动化应用。所提出的方法代表了数据建模预测系统设计的范式转变，并有可能帮助解决大数据在数量，速度和种类方面的基本问题。结果将使用从电动汽车车队收集的各种数据进行验证。拟议工作的成果有可能对制造业在生物学和健康监测领域产生重大影响。由此产生的方法有可能创建更有效的系统，可以更快地适应和应对关键问题。这项工作得到了行业咨询委员会以及该中心各个行业成员的支持，并有可能通过扩展到事件驱动建模，大数据减少和挖掘领域来扩展中心的产品组合，以提高工业效率。该中心将让研究生和本科生参与这项工作。

The proposed work seeks to investigate event-based modeling to deal with high-dimensional and heterogeneous data environments in order to enhance prognostics design with adaptive control of data collection and rapid maintenance decision-making to apply data analytics to the software development process. The event-based approach is explored given its potential to reduce temporal redundancy while preserving the machine dynamics. Event-based approaches have not been fully explored for prognostics applications with continuous signal inputs, such as sensor measurements. The proposed approach represents a paradigm shift in data modeling prognostic system design and holds the potential to help address the fundamental issues of big data in the areas of volume, velocity and variety. Results will be validated using various data collected from a fleet of electric vehicles. The outcomes of the proposed work have the potential for significant impact in the manufacturing sector in the area of prognostics and health monitoring. The resulting approach has the potential to create more efficient systems that can more rapidly adapt and respond to critical issues. The work is supported by the Industry Advisory Board as well as individual industry members of the center and has the potential to extend the centers portfolio through expansion into the area of event-driven modeling, big data reduction and mining for improved industrial efficiency. The center will involve graduate students and undergraduates in the work.