IEEE Transactions on Industrial Electronics, Vol. 63, No. 5, May 2016

A Hybrid Feature Selection Scheme for Reducing Diagnostic Performance Deterioration Caused by Outliers in Data-Driven Diagnostics

Myeongsu Kanga, Md. Rashedul Islamb, Jaeyoung Kimb, Jong-Myon Kimc, and Michael Pechta,
a CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
b School of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
c Department of IT Convergence, University of Ulsan, Ulsan 44610, Korea


In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlierinsensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.

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