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http://dx.doi.org/10.7734/COSEIK.2018.31.1.63

Feature Extraction for Bearing Prognostics using Weighted Correlation Coefficient  

Kim, Seokgoo (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Lime, Chaeyoung (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Choi, Joo-Ho (School of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.31, no.1, 2018 , pp. 63-69 More about this Journal
Abstract
Bearing is an essential component in many rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management(PHM), in which the key issue is to establish a proper feature reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics because it tends to increase abruptly at the end of life. This paper proposes a new feature extraction method using the envelope analysis and weighted sum with correlation coefficient. The developed method is demonstrated using the IMS bearing data given by NASA Ames Prognostics Data Repository. Results by the proposed feature are compared with those by conventional approach.
Keywords
bearing; prognostics and health management(PHM); feature extraction; correlation coefficient;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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