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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