资源描述：
《基于vmd能量熵和bp神经网络风电叶片缺陷研究-中国测试》由会员上传分享，免费在线阅读，更多相关内容在工程资料-天天文库。
1、第 44 卷第 9 期中国测试Vol.44 No.92018 年 9 月CHINA MEASUREMENT & TESTSeptember, 2018doi:10.11857/j.issn.1674-5124.2018.09.021基于VMD能量熵和BP神经网络风电叶片缺陷研究张鹏林1，徐旭2，杨超1，董拴涛1(1. 兰州理工大学材料科学与工程学院，甘肃兰州 730050; 2. 兰州兰石检测技术有限公司，甘肃兰州 730314)摘 要:针对叶片在服役过程中缺陷特征提取困难，提出一种基于变分模态能量熵结合BP神经网络的叶片缺陷诊断方法。首先对声发射信
2、号进行变分模态分解，通过方差贡献率筛选不同缺陷的主要模态分量，之后求取不同缺陷主要模态分量的能量熵构造不同缺陷的特征向量。为验证特征向量选取的准确性，将不同缺陷能量熵向量输入BP神经网络进行缺陷模式识别。结果表明：缺陷识别正确率高达90%，表明变分模态能量熵结合BP神经网络的叶片缺陷诊断方法能够实现叶片早期缺陷识别，具有一定的应用价值。关键词:叶片缺陷; 变分模态分解; 能量熵; BP神经网络中图分类号：TM315 文献标志码:A 文章编号:1674–5124(2018)0
3、9–0115–06ResearchonthefaultofthewindturbinebasedonvariationalmodeenergyentropyandBPneuralnetworkZHANG Penglin1, XU Xu2, YANG Chao1, DONG Shuantao1(1. School of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou 730050, China;2. Lanzhou LS Testing T
4、echnology Co., Ltd., Lanzhou 730314, China)Abstract: Given the difficulty to extract the defect features of blades during service, a diagnosis method ofblade defects based on variational mode decomposition (VMD) energy entropy and BP neural network isproposed in this paper. First
5、ly, the acoustic emission signal originated from blade was decomposed by VMD,and the intrinsic mode functions (IMF) containing main feature information were selected through the variancecontribution rate. Then, the energy entropy of IMF of different defects is obtained to constru
6、ct the eigenvectorof different defects. Finally, in order to verify the accuracy of the eigenvector selected, the energy entropyvector of different defects was input to BP neural network to achieve defect mode recognition. The resultsshow that the accuracy of defect recognition i
7、s higher than 90%, and the diagnosis method of blade defect witha combination of VMD energy entropy and BP neural network can realize the blade defect recognition in earlystage, with certain application value.Keywords: fault of blade; VMD; energy entropy; BP neural netwok0引 言纤维断裂
8、、边缘破损等缺陷[1]，在严苛的外界环境和对于风电机组，叶片是获取风能的关键部件
