PROJECT TITLE :

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

ABSTRACT:

In apply, outliers, defined as data points that are distant from the opposite agglomerated knowledge points in the same class, will seriously degrade diagnostic performance. To scale back diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature choice (OIHFS) methodology is developed to assess feature subset quality. Moreover, a new feature evaluation metric is formed because the ratio of the intraclass compactness to the interclass separability estimated by understanding the link between information points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling component bearings beneath various conditions.


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