EAGER/Cybermanufacturing Systems: Fleet-Sourced Cyber Manufacturing Applications for Improved Transparency and Resilience of Manufacturing Assets and Systems

EAGER/网络制造系统：源自车队的网络制造应用程序，可提高制造资产和系统的透明度和弹性

• 批准号: 1550433
• 负责人: Jay Lee
• 金额: $ 10万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550433&HistoricalAwards=false
• 关键词: EAGER; Cybermanufacturing; Systems; Fleet; Sourced

Internet-enabled services (such as cloud-based and mobile applications) have been influential through almost all economic sectors, such as retail, music, transportation, and healthcare, which have proven the benefit of performing analytics on historical data from a networked system. However, compared to existing Internet-enabled industries, manufacturing assets are less connected and less accessible in real-time. As a result, current manufacturing enterprises make decisions following a top-down approach: from overall equipment effectiveness to assignment of production requirements, without considering the condition of machines. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research to develop the concepts and theory for next-generation advanced cybermanufacturing systems that are networked and interoperable through analytics on the fleet-sourced data. Cybermanufacturing systems will enable a bottom-up real-time decision support manufacturing strategy by taking into account asset health conditions predicted based on historical asset data. Eventually, mobile applications will be developed for portable access of the actionable information. Since such analytics can be performed on data collected from existing asset condition monitoring systems with moderate levels of add-on sensor installment, manufacturing industries in almost every sector will benefit from the results of this research. Consequently, this research will inject speed into the development of U.S. economy and benefit the society by increasing the efficiency and productivity of manufacturing enterprises. This research requires knowledge and expertise from a variety of disciplines including manufacturing, mechanical engineering, computer science, and control theory. The interdisciplinary methodology will facilitate the creativity and healthy growth of the involved areas and draw interest from younger generation to impact science, technology, engineering and mathematics education.The new cybermanufacturing methodology will deepen the research on fleet-sourced prognostics, which overcomes several drawbacks of conventional prognostics and health management approaches, including lack of generality and reconfigurability, lack of robustness against changing regimes, and sometimes insufficient accuracy. A fleet is referred to as a group of assets similar in working conditions (make and model, ambient conditions, and health status). Research gaps in conducting fleet-sourced prognostics exist in the quantification of asset similarity, clustering fleets, validation of such fleets, and dynamically changing the clustering scheme when regimes change. The research team will leverage existing fleet-level peer-to-peer prognostics approaches to develop a reconfigurable platform with capabilities to reduce the dimensionality from fleet-sourced data, devise a risk assessment methodology to provide real-time predictive actionable information, and eventually incorporate such functions into the developed mobile applications.

支持互联网的服务（例如基于云的应用程序和移动应用程序）对零售、音乐、交通和医疗保健等几乎所有经济领域都产生了影响，这已经证明了对网络系统的历史数据进行分析的好处。然而，与现有的互联网行业相比，制造资产的互联性和实时访问性较差。因此，目前的制造企业都是采用自上而下的方式进行决策：从设备整体效率到生产需求分配，而没有考虑机器的状况。这项早期概念探索性研究资助 (EAGER) 奖项支持基础研究，以开发下一代先进网络制造系统的概念和理论，这些系统通过对车队来源数据的分析进行联网和互操作。网络制造系统将通过考虑基于历史资产数据预测的资产健康状况，实现自下而上的实时决策支持制造策略。最终，将开发移动应用程序以方便地访问可操作信息。由于此类分析可以对从具有中等水平附加传感器安装的现有资产状态监测系统收集的数据进行，因此几乎每个行业的制造业都将从这项研究结果中受益。因此，这项研究将通过提高制造企业的效率和生产力，为美国经济的发展注入速度，造福社会。这项研究需要来自制造、机械工程、计算机科学和控制理论等多个学科的知识和专业知识。跨学科方法将促进所涉及领域的创造力和健康发展，并引起年轻一代对影响科学、技术、工程和数学教育的兴趣。新的网络制造方法将深化对车队预测的研究，克服了现有技术的一些缺点。传统的预测和健康管理方法，包括缺乏通用性和可重构性、缺乏针对不断变化的制度的稳健性，有时准确性不足。车队是指工作条件（品牌和型号、环境条件和健康状况）相似的一组资产。进行基于车队的预测的研究差距存在于资产相似性的量化、车队聚类、此类车队的验证以及在政权变化时动态改变聚类方案方面。研究团队将利用现有的车队级点对点预测方法来开发一个可重新配置的平台，该平台具有降低车队来源数据维度的能力，设计一种风险评估方法来提供实时预测性可操作信息，并最终将将此类功能集成到开发的移动应用程序中。