期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 65, 期 7, 页码 5872-5881出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2777383
关键词
Detectability; diagnosis; maximum mean discrepancy (MMD); prognostics and health management (PHM); trendability
资金
- National Science Foundation Industry/University Cooperative Research Center for Intelligent Maintenance Systems, University of Cincinnati, Cincinnati, OH, USA
As more and more data become available for machine prognostic analysis in the big data environment, effective data suitability assessment methods become highly desired to help locate data with sufficient quality for analysis. Driven by this purpose, this paper proposes a novel and systematic methodology for data suitability assessment based on the needs of prognostics and health management (PHM). In this study, the data suitability for PHM is assessed from the aspects of detectability, diagnosability, and trendability, which correspond to the three major tasks of PHM: fault detection, fault diagnosis, and degradation assessment. The proposed methodology is mainly built upon the recent research studies on maximum mean discrepancy in the field of machine learning, which include a family of test statistics that are used to test the difference between two data distributions. The effectiveness of the proposed methodology is demonstrated in diverse industrial applications, which include semiconductors, boring tool degradation, and sensorless drive diagnosis. The results in the case studies indicate that the proposed methodology can be a promising tool to evaluate whether the data under study or the extracted feature set is suitable for PHM tasks.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据