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

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 5, 2016, Pages 3299-3310

  Kang, Myeongsu ^a,b   Islam, Md Rashedul ^a   Kim, Jaeyoung ^a   Kim, Jong Myon ^a   Pecht, Michael ^b  

^a University of Ulsan   (South Korea)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

Data driven diagnostics; outlier detection; outlier insensitive hybrid feature selection (OIHFS); rolling element bearings

Indexed keywords

BEARINGS (MACHINE PARTS); DETERIORATION; FAULT DETECTION; ROLLER BEARINGS; STATISTICS;

DATA DRIVEN; DIAGNOSTIC PERFORMANCE; FAULT DIAGNOSIS APPLICATIONS; FEATURE EVALUATION; FEATURE SUBSET; HYBRID FEATURE SELECTIONS; OUTLIER DETECTION; ROLLING ELEMENT BEARING;

FEATURE EXTRACTION;

EID: 84963934380     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2016.2527623     Document Type: Article

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