A Systematic Methodology for Data Validation and Verification for Prognostics Applications

该提案寻求为辛辛那提大学网站(LEAD)、密苏里州科技大学网站和密歇根大学网站进行的智能维护系统研究中心提供资金。基础研究的资金申请由NSF批准的请愿书授权，NSF 10-507。征集邀请研究所/大学合作研究中心提交支持行业定义的基础研究的建议。拟议的研究重点是控制和评估系统健康状况预测应用程序中使用的数据质量的方法，并指示何时需要维护。该提案构思良好，组织良好，研究的目的和目的也得到了很好的阐述。明确了两年内要完成的任务，以及哪些合作机构将开展这项工作。这项拟议的研究回答了工业合作伙伴提出的一个重要研究问题。对于许多公司来说，在实际执行预测之前确保数据集的质量，可以避免由于低质量的数据集而对冗余的预测分析进行不必要的投资。有保证的数据质量将改善预测结果，从而做出更好的维护决策并显著节省成本。IMS中心积极参与少数族裔和女性研究生，并为教师和本科生(RET和REU)项目提供了一些研究经验。

This proposal seeks funding for the Center for Intelligent Maintenance Systems studies conducted by the University of Cincinnati site (lead), the Missouri University of Science and Technology site and the University of Michigan site. Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-507. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research. The proposed research focuses on methods for controlling and evaluating the quality of data used in prognostic applications of system health and indicating when maintenance is needed. The proposal is well conceived, well organized, and the goals and objectives of the research are presented well. The tasks to be accomplished over the two years are clearly outlined, as well as which of the cooperating institutions will carry out the work. The proposed research answers a significant research question raised by the industrial partners. For many companies assuring the quality of datasets before actually performing prognostics can avoid unnecessary investment in redundant prognostics analysis due to poor quality datasets. Assured data quality will improve prognostics results, which leads to better maintenance decisions and significant cost saving. The IMS Center has actively involved minority and female graduate students and has provided a number of Research Experience for Teachers and Undergraduates (RET and REU) projects.