Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis

IEEE Transactions on Power Electronics

Volumn 30, Issue 5, 2015, Pages 2786-2797

  Kang, Myeongsu ^a   Kim, Jaeyoung ^a   Kim, Jong Myon ^a   Tan, Andy C C ^b   Kim, Eric Y ^c   Choi, Byeong Keun ^d  

^a University of Ulsan   (South Korea)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^c School of Chemistry Physics and Mechanical Engineering Faculty of Science and Engineering Queensland University of Technology ^*  (Australia)

^d Gyeongsang National University   (South Korea)

Author keywords

Acoustic emission (AE); fault diagnosis of lowspeed bearings; genetic algorithm (GA); individually trained multicategory support vector machines; kernel discriminative feature analysis

Indexed keywords

ACOUSTIC EMISSION TESTING; CLASSIFICATION (OF INFORMATION); FACE RECOGNITION; IMAGE RETRIEVAL; SPEED; SUPPORT VECTOR MACHINES; VECTORS; WAVELET ANALYSIS;

CLASSIFICATION ABILITY; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; DISCRIMINATIVE FEATURES; LOW SPEED BEARING; MULTICATEGORY; REVOLUTIONS PER MINUTES; STATE OF THE ART;

FAULT DETECTION;

EID: 84920116176     PISSN: 08858993     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPEL.2014.2358494     Document Type: Article

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