A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels

To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This study initially establishes a measurement system for shaft frequency electric fields, utilizing a titanium-based oxide electrode to construct an equivalent dipole source simulating the shaft frequency electric field signals of different types of vessels. Subsequently, a comparative analysis of the time-domain and frequency-domain characteristics of signals after modal decomposition is conducted. A feature extraction method is then proposed that combines the maximum average energy of intrinsic mode functions with arrangement entropy values to achieve discrimination of target signals. Finally, the feasibility of the proposed method is validated through sea trials. The results indicate that the method can successfully screen different types of typical vessels and address the target screening failure caused by slight differences in the characteristic parameters of the shaft frequency electric field signal. The entropy difference has been improved from 0.05 to about 0.2, and the difference rate of the shaft frequency electric field signal has been improved by 75%. This has effectively reduced the false alarm rate of target detection.

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Publication Details

Journal
Applied Sciences
Published
2025-05-29
DOI
https://doi.org/10.3390/app15116143
Citations
1
Primary Topic
Engineering Diagnostics and Reliability
Type
article
Field-Weighted Citation Impact
0.86
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article

A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels

Zhaolong Sun, Runxiang Jiang, Qi Liu, Xiaoguang Ma et al.
1 citations
Applied Sciences
Engineering Diagnostics and Reliability
0.86
article

A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels

Zhaolong Sun, Runxiang Jiang, Qi Liu, Xiaoguang Ma, Xinquan Yue
article en
1 citations

Abstract

To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This study initially establishes a measurement system for shaft frequency electric fields, utilizing a titanium-based oxide electrode to construct an equivalent dipole source simulating the shaft frequency electric field signals of different types of vessels. Subsequently, a comparative analysis of the time-domain and frequency-domain characteristics of signals after modal decomposition is conducted. A feature extraction method is then proposed that combines the maximum average energy of intrinsic mode functions with arrangement entropy values to achieve discrimination of target signals. Finally, the feasibility of the proposed method is validated through sea trials. The results indicate that the method can successfully screen different types of typical vessels and address the target screening failure caused by slight differences in the characteristic parameters of the shaft frequency electric field signal. The entropy difference has been improved from 0.05 to about 0.2, and the difference rate of the shaft frequency electric field signal has been improved by 75%. This has effectively reduced the false alarm rate of target detection.

Applied SciencesVol. 15(11)
Naval University of Engineering (CN)
Openalex Percentile: Top 30%
Engineering Diagnostics and Reliability
0.86
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A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels — Zhaolong Sun, Runxiang Jiang, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS