Structural Health Monitoring of Wind Turbine Blades Using Statistical Pattern Recognition

Wind turbine blades meet extremely complex loading cycles due to the random nature of wind conditions on their installation sites. Even though, when today available monitoring techniques have arrived at a technical level where it is possible to monitor the blades during operation, this fact still presents a series of technical and logistical challenges to be overcome when turbines are placed offshore. Additionally, offshore environment is tremendously severe and place high demands not only on the strength of the turbine blade structural design but also in the need of reliable blade monitoring systems. In this account, this paper presents an automated method for the assessment of the structural integrity of wind turbine blades. The purpose of this publication is to develop and present a methodology in order to tackle some of the challenges and problems to be solved before a full system can be proved capable of detecting critical damage in blades. The proposed system is based on an active sensor network where feature extraction, sensor data fusion and statistical baseline modelling are synergically evaluated for the purpose of damage detection within the context of pattern recognition-based structural health monitoring (SHM). Delamination fracture, one of the major damage modes in laminated composite materials, is introduced in several steps into the experimental specimen so that the proposed methodology can be evaluated. At the end, it is shown how damage and its development can be determined with the help of the proposed methodology.