基于VMD交叉样本熵的旋翼桨叶故障诊断方法
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TP306+.3;TP181;TN911.72

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国家自然科学基金(51675265)、机械结构力学及控制国家重点实验室自主研究课题(0515K01)、江苏省高校优势学科建设工程(PAPD)项目资助


Faultdiagnosis method of rotor blade based on VMD and cross-sample entropy
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    摘要:

    针对旋翼桨叶常见的故障类型,提出了一种新的故障诊断模型。该模型首先对经无线测控系统采集的加速度信号进行变分模态分解,得到一系列不同频段上的模态分量。随后计算相同模态,不同传感器之间的交叉样本熵,最后将交叉样本熵作为特征向量代入经帝国竞争算法优化的支持向量机中进行故障分类。实验结果表明,基于交叉样本熵的特征具有较高的区分度,采用该模型对不同位置、不同大小的故障进行诊断时,总分类精度为98.67%,证明了提出的故障诊断模型的有效性。

    Abstract:

    A new damage identification method was proposed to identify common damages of rotor blades. Firstly, the method performed a variational mode decomposition (VMD) on the vibration signals collected by wireless measurement and control system, obtaining a series of principal modes in different frequency bands. Then the cross-sample entropy (CSE) was calculated between different sensors in the same frequency band. Finally, the entropy values, which were damage features, were taken input to a SVM optimized by the empire competition algorithm (ECA). By analyzing the experimental data, the results show that the feature based on cross-sample entropy has a high degree of discrimination. Damages with different positions and different sizes were identified using this method, and the total identification accuracy is 98.67%, which proves the effectiveness of the proposed damage identification method.

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吕宏政,陈仁文,张祥,崔雨川.基于VMD交叉样本熵的旋翼桨叶故障诊断方法[J].电子测量技术,2019,42(9):107-111

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  • 在线发布日期: 2021-08-23
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