An approach for bearing fault diagnosis based on PCA and multiple classifier fusion

Proceedings - 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2011

Volumn 1, Issue , 2011, Pages 321-325

  Xia, Min ^a   Kong, Fanrang ^a   Hu, Fei ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

fault diagnosis; multiple classifier fusion; PCA; rolling bearing

Indexed keywords

BEARING FAULT DIAGNOSIS; CLASSIFIER FUSION; CORRELATION MEASURES; DATA DIMENSIONS; DECISION FUSION; DECISION-FUSION ALGORITHMS; FINAL DECISION; HIGH EFFICIENCY; INDIVIDUAL CLASSIFIERS; MULTIPLE CLASSIFIER FUSION; MULTIPLE CLASSIFIERS; OPTIMAL SEQUENCE; PCA; PRE-TREATMENT; PRINCIPAL COMPONENTS; PRINCIPLE COMPONENT; REAL APPLICATIONS; RECOGNITION METHODS; ROLLING BEARING; ROLLING BEARINGS; SELECTION PROCESS; SENSITIVE FEATURES; STATISTICAL FEATURES; TIME AND FREQUENCY DOMAINS; WORKING CONDITIONS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); DATA COMPRESSION; FREQUENCY DOMAIN ANALYSIS; INFORMATION FUSION; INFORMATION TECHNOLOGY;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80054753579     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITAIC.2011.6030215     Document Type: Conference Paper

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