Effect of number of features on classification of roller bearing faults using SVM and PSVM

Expert Systems with Applications

Volumn 38, Issue 4, 2011, Pages 4088-4096

  Sugumaran, V ^a   Ramachandran, K I ^b  

^a SRM INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

^b AMRITA SCHOOL OF ENGINEERING   (India)

Author keywords

Fault diagnosis; Histogram feature; Number of features; PSVM; Statistical feature; SVM

Indexed keywords

FAULT DIAGNOSIS; HISTOGRAM FEATURE; NUMBER OF FEATURES; PSVM; STATISTICAL FEATURES; SVM;

BEARINGS (STRUCTURAL); CONDITION MONITORING; GRAPHIC METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAINTENANCE; MATHEMATICAL OPERATORS; PROBLEM SOLVING; ROLLER BEARINGS; STATISTICAL METHODS; TIME DOMAIN ANALYSIS;

FEATURE EXTRACTION;

EID: 78650687670     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.09.072     Document Type: Article

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