SGER: Exploratory Research in Maintenance Science

SGER：维护科学的探索性研究

• 批准号: 9527953
• 负责人: Frederick Ling
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-01 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9527953&HistoricalAwards=false
• 关键词: SGER; Exploratory; Research; Maintenance; Science

9527953 Ling This Small Grant for Exploratory Research will investigate a new view of process equipment maintenance and reliability as part of the life cycle modeling of manufactured products. The path to accomplish this is to move maintenance of equipment and processes from responsive corrective repairs, to intelligent prevention using experience and scheduled service, finally to the goal of a science based and sensor enhanced way of doing predictive maintenance. The focus area is the electronics industry, where these investigators have established the contacts needed to get empirical data to back up their theory and methodology. The exploratory research objectives are: to judiciously assemble operating and maintenance data, to identify promising methods for modeling equipment degradation that use both stochastic and mechanistic methods, and to include degradation modeling of process equipment in the life cycle modeling of manufactured products. The models, backed up by data, will lead to a methodology that can identify equipment components that are critical because either they have precursors that give long range predictions of their degradation or they deteriorate so quickly they need sensor based monitoring. This grant will attempt to establish a science and engineering base for maintenance, that will lead greater availability and productivity, lower costs for energy and maintenance, and improved product quality and safety. ***

小行星9527953 这项探索性研究的小额赠款将调查过程设备维护和可靠性的新观点，作为制造产品生命周期建模的一部分。 实现这一目标的途径是将设备和流程的维护从响应式纠正性维修转移到使用经验和计划服务的智能预防，最终实现基于科学和传感器增强的预测性维护方式的目标。 重点领域是电子行业，这些研究人员已经建立了必要的联系，以获得经验数据来支持他们的理论和方法。 探索性的研究目标是：明智地组装操作和维护数据，以确定有前途的方法建模设备退化，使用随机和机械的方法，并包括过程设备的退化建模在制造产品的生命周期建模。 这些由数据支持的模型将产生一种方法，可以识别关键的设备组件，因为它们要么具有对其退化进行长期预测的前体，要么它们恶化得如此之快，以至于需要基于传感器的监测。 这笔赠款将试图建立一个科学和工程基础的维护，这将导致更大的可用性和生产力，降低成本的能源和维护，并提高产品质量和安全。 ***