A Systematic Methodology for Data Validation and Verification for Prognostics Applications

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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.