Feature extraction of cavitation noise based on CEEMDAN multi-scale improved permutation entropy and SVM

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (13) : 190-197.

WU Chenglong, GAO Hanlin, ZHU Dandan, LI Ya’an

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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.

Key words

MIPE / CEEMDAN / Cavitation noise / feature extraction

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WU Chenglong, GAO Hanlin, ZHU Dandan, LI Ya’an. Feature extraction of cavitation noise based on CEEMDAN multi-scale improved permutation entropy and SVM[J]. Journal of Vibration and Shock, 2024, 43(13): 190-197

References

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