Tendon damage identification of 10 MW floating wind turbine based on CMS-CNN
XU Zifei1,2, YANG Yang2,3, LI Chun1,4, MIAO Weipao1, ZHANG Wanfu1, JIN Jiangtao1, WANG Xinyu1
1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China；
2. Department of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool L3 3AF, UK；
3. Faculty of Maritime and Transportation, Ningbo University， Ningbo 315211， China；
4. Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering， Shanghai 200093， China
Abstract：A novel end-to-end Diagnosis model named Continuous-Multi-Scale Convolutional Neural Network (CMS-CNN) has been proposed to improve the rate of identification of structural damage on the damaged tendons in the float wind turbine platform. The effectiveness of the proposed CMS-CNN is examined by a 10MW float wind turbine model. The damaged locations and damaged degrees of the tendons in the 10MW float wind turbine are diagnosed by the proposed method. The results show that: The model considered more information by the continuous multi-scale coarse-grained procedure has better performance than the traditional multi-scale based model. The CMS-CNN model, using sway acceleration as the inputs, is more reliable than the model using the other accredited information. The CMS-CNN model can locate the damaged positions in the initial stages of damage evolution and diagnose both the locations and degrees of recessive damage of the tendons.
许子非1,2，杨阳2,3，李春1,4，缪维跑1，张万福1，金江涛1，王鑫雨1. 基于续尺度卷积网络的10MW漂浮式风力机筋腱损伤识别[J]. 振动与冲击, 2022, 41(3): 183-189.
XU Zifei1,2, YANG Yang2,3, LI Chun1,4, MIAO Weipao1, ZHANG Wanfu1, JIN Jiangtao1, WANG Xinyu1. Tendon damage identification of 10 MW floating wind turbine based on CMS-CNN. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(3): 183-189.
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