I/UCRC: Collaborative Research on Coupled Models for Prognostics and Health Management

I/UCRC：预测与健康管理耦合模型的合作研究

• 批准号: 1230840
• 负责人: Jay Lee
• 金额: $ 5.7万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230840&HistoricalAwards=false
• 关键词: UCRC; Collaborative; Research; Coupled; Models

The proposed research seeks to provide a comprehensive framework for how to collect and assess degradation data over the product lifecycle from design to usage phases. The proposed framework will enhance both data-driven modeling and physics-based modeling techniques outlined in previous prognostic research. By using the coupled model scheme, the requirements for data quality and complete product design information can be reduced. The scheme can be validated on a critical rotary machinery component, such as a bearing, as well as for more complex components and structures, such an electrical vehicle battery. The proposed work on developing a combined data driven and physics driven model for fault prognostication has the potential to yield significant cost reduction in manufacturing systems and processes. 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 center?s portfolio. The center has actively involved minority and female students in its research through Research Experiences for Teachers and Undergraduates (RET and REU) projects and will continue to do so through the proposed effort.

拟议的研究旨在提供一个全面的框架，用于如何收集和评估从设计到使用阶段的产品生命周期中的降解数据。所提出的框架将增强先前预测研究中概述的数据驱动建模和基于物理的建模技​​术。通过使用耦合模型方案，可以降低对数据质量和完整产品设计信息的要求。该方案可以在关键的旋转机械部件（例如轴承）以及更复杂的部件和结构（例如电动汽车电池）上进行验证。所提议的开发用于故障预测的数据驱动和物理驱动组合模型的工作有可能显着降低制造系统和工艺的成本。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，并且有潜力扩展该中心的产品组合。 该中心通过教师和本科生研究经验（RET 和 REU）项目积极让少数族裔和女学生参与其研究，并将继续通过拟议的努力这样做。