Acoustic Emission Condition Monitoring Of Wind Turbine Rotor Blades:

Laboratory Certification Testing To Large Scale In-Service Deployment


To appear in the proceedings of European Wind Energy Conference - EWEC 2003

A G Dutton, M J Blanch, P Vionis, D Lekou, D R V van Delft, P A Joosse, A Anastassopoulos, D Kouroussis, T Kossivas, T. P. Philippidis, T. T. Assimakopoulou, G Fernando, C Doyle, A Proust


Wind turbines experience long term fluctuating variable amplitude fatigue loads with occasional large amplitude stochastic peak loads. The fatigue loads are usually characterised by a mean load with a fixed amplitude oscillation superimposed; the stochastic peak loads by modelled peak loads, such as the fifty year gust.

A methodology for wind turbine blade monitoring using acoustic emission (AE) detection of damage processes in the structure has been developed by the AEGIS consortium, supported by the European Commission. The methodology has been developed separately for the peak load events and the more usual operational fatigue loading. It can be applied as an enhancement to the conventional blade certification test and has the potential to be adapted to large-scale field application of the techniques on operational wind turbines.

Wind turbine blade certification tests are carried out to validate design and production. AE monitoring during all stages of a test can both locate and characterise damage processes in blades, starting with non-audible signals occurring due to damage propagation at relatively low loads. Characteristic results are presented of AE activity during peak loading events and fatigue blade tests to failure in the laboratory.

The transfer of the techniques to operating wind turbines is speculative, but the results presented indicate the kind of results which could be obtained from monitoring in-service machines. In particular, a dedicated pattern recognition software has been developed which could identify differences from turbine to turbine and help target preventative maintenance. Validation of the software from laboratory tests on blades is presented. Finally, the requirements for successful deployment of AE condition monitoring in the field are discussed.