On damage diagnosis for a wind turbine blade using pattern recognition

Journal of Sound and Vibration

Volumn 333, Issue 6, 2014, Pages 1833-1850

  Dervilis, N ^a   Choi, M ^c   Taylor, S G ^b   Barthorpe, R J ^a   Park, G ^b,d   Farrar, C R ^b   Worden, K ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b LOS ALAMOS NATIONAL LABORATORY   (United States)

^c Chonbuk National University   (South Korea)

^d Chonnam National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOASSOCIATIVE NEURAL NETWORKS; FULL-SCALE FATIGUE TEST; LOS ALAMOS NATIONAL LABORATORY; NATIONAL RENEWABLE ENERGY LABORATORY; NATIONAL WIND TECHNOLOGY CENTERS; PATTERN RECOGNITION METHOD; RADIAL BASIS FUNCTIONS; STRUCTURAL HEALTH MONITORING (SHM);

CHEMICAL DETECTION; DAMAGE DETECTION; ELECTRIC UTILITIES; FATIGUE TESTING; LEARNING ALGORITHMS; NEURAL NETWORKS; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; STRUCTURAL HEALTH MONITORING; VIBRATION ANALYSIS; WIND POWER;

WIND TURBINES;

EID: 84891747988     PISSN: 0022460X     EISSN: 10958568     Source Type: Journal    
DOI: 10.1016/j.jsv.2013.11.015     Document Type: Article

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