Dual features functional support vector machines for fault detection of rechargeable batteries

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 39, Issue 4, 2009, Pages 480-485

  Park, Jong I ^a   Baek, Seung H ^b   Jeong, Myong K ^c   Bae, Suk J ^a  

^a Hanyang University   (South Korea)

^b University of Tennessee Knoxville   (United States)

^c the State University of New Jersey   (United States)

Author keywords

Attribute sampling plans; Data mining; Feature selection; Process monitoring; SSecondary rechargeable battery; Support vector machine

Indexed keywords

ATTRIBUTE SAMPLING PLANS; CLASSIFICATION ERRORS; COMPUTATION TIME; CONVENTIONAL APPROACH; DEGRADATION PROFILES; EARLY DETECTION; ERROR RATE; FEATURE SELECTION; MANUFACTURING PROCESS; ONLINE IMPLEMENTATION; OPTIMAL NUMBER; PERFORMANCE DEGRADATION; QUALITY CHARACTERISTIC; RECHARGEABLE BATTERY; REMAINING CAPACITY; SAMPLE SIZES; SAMPLING PLANS; SEARCH ALGORITHMS; SECOND DERIVATIVES; SSECONDARY RECHARGEABLE BATTERY; TEST CYCLES; TEST DURATION; TIME VARYING; TIME-DEPENDENT;

DEGRADATION; ERROR DETECTION; FAULT DETECTION; GEARS; IMAGE RETRIEVAL; INDUSTRIAL ENGINEERING; INFORMATION MANAGEMENT; LEARNING ALGORITHMS; MINING; MULTILAYER NEURAL NETWORKS; PROCESS CONTROL; PROCESS MONITORING; PRODUCTION ENGINEERING; SAMPLING; SECONDARY BATTERIES; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 67649289964     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2009.2014642     Document Type: Article

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