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，以处理高维和异类数据环境，以便通过自适应控制数据收集和快速维护决策来增强预测设计，以便将数据分析应用于软件开发过程。鉴于基于事件的方法在保持机器动力学的同时减少时间冗余的潜力，本文对其进行了探讨。基于事件的方法还没有被充分探索用于具有连续信号输入的预测应用，例如传感器测量。拟议的方法代表了数据建模预测系统设计的范式转变，并具有帮助解决大数据在数量、速度和多样性领域的基本问题的潜力。结果将使用从一支电动汽车车队收集的各种数据进行验证。拟议工作的结果有可能在预测和健康监测领域对制造业产生重大影响。由此产生的方法有可能创建更有效的系统，以便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个别行业成员的支持，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大中心的投资组合，以提高工业效率。该中心将邀请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.