当水下航行器处于高速航行时就会形成空化噪声，所产生的噪声会严重影响水下航行器的性能和安全。螺旋桨噪声包含着丰富的空化信息，是识别空化状态的有效手段。针对改进排列熵在单尺度下对原信号进行分析，无法有效区分不同空化状态，提出了将改进排列熵与自适应噪声完备经验模态分解（CEEMDAN）相结合的空化噪声特征提取方法。首先，采用自适应噪声完备经验模态分解（CEEMDAN）方法对水下航行器螺旋桨的空化噪声进行分解，提取具有空化特征的固有模态函数（IMF）分量；其次，选取相关系数最高的IMF分量并计算其多尺度改进排列熵（MIPE）；最后，基于多尺度改进排列熵，建立支持向量机的特征分类模型。仿真和实验结果表明，该方法具有更好的可分性。
Abstract
When underwater vehicles are sailing at high speeds, cavitation noise will form, which will seriously affect the performance and safety of underwater vehicles. Propeller noise contains rich cavitation information and is an effective means of identifying cavitation states. A cavitation noise feature extraction method combining improved permutation entropy with adaptive noise complete empirical mode decomposition (CEEMDAN) is proposed to effectively distinguish different cavitation states in analyzing the original signal at a single scale. Firstly, the adaptive noise complete empirical mode decomposition (CEEMDAN) method is used to decompose the cavitation noise of the propeller of an underwater vehicle, and the intrinsic mode function (IMF) components with cavitation characteristics are extracted; Secondly, select the IMF component with the highest correlation coefficient and calculate its multi-scale improved permutation entropy (MIPE); Finally, based on multi-scale improved permutation entropy, a feature classification model for support vector machines is established. The simulation and experimental results show that this method has better separability.
关键词
MIPE /
CEEMDAN /
空化噪声；特征提取
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Key words
MIPE /
CEEMDAN /
Cavitation noise /
feature extraction
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参考文献
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