A Systematic Methodology for Data Validation and Verification for Prognostics Applications

该提案为辛辛那提大学、密苏里科技大学和密歇根大学进行的智能维护系统研究中心寻求资金支持。基础研究的资金申请由NSF批准的征求授权，NSF 10-507。本次招标邀请I/ ucrc提交支持行业定义基础研究的提案。提出的研究重点是控制和评估系统健康预测应用中使用的数据质量的方法，并指出何时需要维护。该提案构思良好，组织良好，研究的目标和目的被很好地提出。明确概述了两年内要完成的任务，以及哪些合作机构将执行这项工作。提出的研究回答了工业合作伙伴提出的一个重要的研究问题。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数据集质量差而对冗余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，从而带来更好的维护决策和显著的成本节约。IMS中心积极参与少数民族和女性研究生，并为教师和本科生（RET和REU）提供了一些研究经验项目。

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.