Defect testing of wind turbine blades based on CEEMDAN energy entropy and SVM
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摘要: 针对目前风电叶片缺陷特征提取的问题，提出了一种基于完全噪声辅助集总经验模态分解（CEEMDAN）和支持向量机（SVM）相结合的叶片缺陷诊断识别方法。通过对采集的声发射信号进行CEEMDAN，借助互相关系数筛选叶片缺陷的主要模态分量，然后构造主要模态分量的能量熵向量。为验证能量熵向量构造的可靠性，对叶片不同缺陷进行能量熵向量的支持向量机模式识别。结果表明，SVM模式识别准确率高达96.7%，说明基于CEEMDAN结合SVM的叶片缺陷识别方法能够实现叶片模拟缺陷的识别，为在役叶片缺陷的识别提供了一定的参考。
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关键词:
- 风电叶片缺陷 /
- 完全噪声辅助集总经验模态分解 /
- 能量熵 /
- 支持向量机
Abstract: Considering the difficulty of the extracting for the fault feature of blade in service, an intelligent recognition method for defects in blade based on Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with support vector machine (SVM) is proposed. First of all, the signals originated from defects of the blade were decomposed by Complete Ensemble Empirical Mode Decomposition with adaptive noise, and the intrinsic mode functions (IMF) containing the main feature information were selected by cross-correlation number. Then, the main Energy Entropy of the intrinsic mode functions was calculated and the vectors of Complete Ensemble Empirical Mode Decomposition with adaptive noise energy entropy were constructed. Finally, in order to verify the reliability of vector of energy entropy, the pattern recognition was carried out by the support vector machine (SVM) for different defects. The results show that the average recognition rate for different faults were as high as 96.7%, and it suggests that the method of Complete ensemble empirical mode decomposition with adaptive noise energy entropy combined with SVM can be supplied well for recognizing and warning in blade during the early process.-
Keywords:
- blade defect /
- CEEMDAN /
- energy entropy /
- SVM
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