A morphological filtering-based strain data processing method for biaxial fatigue testing of wind turbine blades

Abstract

Biaxial fatigue testing is an effective way to verify the performance of large wind turbine blades. The test process will generate a large amount of transient strain data, which needs to be peak detection to control the loading system and provided it to third-party organizations for type certification. Peak detection is challenging due to the long test cycle and severe signal noise pollution. The objective of this article is to propose a strain data processing method based on morphological filtering. It is found that morphological filtering + three-point smoothing has a better filtering effect. In addition, a peak detection algorithm is designed and proved its effectiveness. In order to validate the proposed method, a principle prototype of biaxial fatigue testing is built for testing. The results show that the method can not only effectively filter out noise, but also accurately and quickly detect the strain peaks, improve the efficiency of damage calculation and effectively control the test process. The method can also be used in the practical engineering field to process strain data generated during fatigue testing of wind turbine blades and improve overall testing efficiency.