Damage assessment for wind turbine blades based on a multivariate statistical approach

Garcia Cava, David and Tcherniak, Dmitri and Trendafilova, Irina; (2015) Damage assessment for wind turbine blades based on a multivariate statistical approach. In: Journal of Physics Conference Series. Journal of Physics: Conference Series, 628 . Institute of Physics Publishing, BEL.

[thumbnail of Garcia-etal-Proc-J-Phys-2015-Damage-assessment-wind-turbine-blades-multivariate-statistical-approach]
Text (Garcia-etal-Proc-J-Phys-2015-Damage-assessment-wind-turbine-blades-multivariate-statistical-approach)
Accepted Author Manuscript

Download (14MB)| Preview


    This paper presents a vibration based structural health monitoring methodology for damage assessment on wind turbine blades made of composite laminates. Normally, wind turbine blades are manufactured by two half shells made by composite laminates which are glued together. This connection must be carefully controlled due to its high probability to disbond which might result in collapse of the whole structure. The delamination between both parts must be monitored not only for detection but also for localisation and severity determination. This investigation consists in a real time monitoring methodology which is based on singular spectrum analysis (SSA) for damage and delamination detection. SSA is able to decompose the vibratory response in a certain number of components based on their covariance distribution. These components, known as Principal Components (PCs), contain information about of the oscillatory patterns of the vibratory response. The PCs are used to create a new space where the data can be projected for better visualization and interpretation. The method suggested is applied herein for a wind turbine blade where the free-vibration responses were recorded and processed by the methodology. Damage for different scenarios viz diferent sizes and locations was introduced on the blade. The results demonstrate a clear damage detection and localization for all damage scenarios and for the different sizes.

    ORCID iDs

    Garcia Cava, David ORCID logoORCID: https://orcid.org/0000-0002-3841-6824, Tcherniak, Dmitri and Trendafilova, Irina ORCID logoORCID: https://orcid.org/0000-0003-1121-7718;