## 7 Conclusions  
For the feature extraction of different types of vessel shaft frequency electric field signals, this paper proposes a feature extraction method based on the generation mechanism of vessel target shaft frequency electric field signals, which arranges the intrinsic mode function energy and entropy. The feasibility of the method is verified by using the scaled model experiment. The feature extraction effect of different decomposition methods is \begin{table}
\begin{tabular}{c c c c} \hline  & **Vessel A** & **Vessel B** & **Vessel C** \\ \hline Variance of PE values for IMF with Maximum & 4.5134 \(\times\) 10\({}^{-8}\) & 1.3986 \(\times\) 10\({}^{-5}\) & 8.5552 \(\times\) 10\({}^{-8}\) \\ Average Energy & 0.9090–0.9917 & 0.4667–0.4822 & 0.686–0.69 \\ Variance of PE Values & 8.0481 \(\times\) 10\({}^{-9}\) & 4.2267 \(\times\) 10\({}^{-8}\) & 4.6864 \(\times\) 10\({}^{-8}\) \\ Range of PE Value & 0.9993–0.9997 & 0.9980–0.9989 & 0.9941–0.9952 \\ Variance of MPE Values & 1.4947 \(\times\) 10\({}^{-8}\) & 8.09472 \(\times\) 10\({}^{-7}\) & 4.4766 \(\times\) 10\({}^{-7}\) \\ (Time Scale = 42) & 0.9992–0.9998 & 0.9837–0.9878 & 0.9819–0.9854 \\ Variance of MPE Values & 9.9759 \(\times\) 10\({}^{-9}\) & 1.4209 \(\times\) 10\({}^{-6}\) & 5.6725 \(\times\) 10\({}^{-7}\) \\ (Time Scale = 43) & 0.9992–0.9998 & 0.9811–0.9860 & 0.9836–0.9869 \\ \hline \end{tabular}
\end{table} 
Table 4: Feature parameters of electric field signals from different vessels. Figure 17: PE values of IMFs with maximum average energy for different vessels. analyzed from the two perspectives of the time domain and the frequency domain. On this basis, the maximum average energy component of the shaft frequency signal is used to calculate the entropy value to distinguish the non-smooth shaft frequency signals generated by different targets. Finally, the method is verified by using the real vessel data, and the conclusions are as follows:  
1. For the traditional single modal decomposition, the EMD method is more suitable for shaft frequency electric field time-domain signal feature extraction than the three decomposition methods of EWT, EEMD, and ICCEMAND after the comparison of the line spectrum detection methods. 2. Based on the EMD algorithm, the modal decomposition and entropy value algorithm extract the signal features of the vessel's shaft frequency electric field. A test system is built to simulate three different vessel shaft frequency electric field signals, and the PE value is analyzed to decrease significantly with the increase in the order of the IMF component after signal decomposition. To distinguish the shaft frequency E-field signals of different vessels, the IMF components are arranged in descending order of energy, and the PE value of the IMF with maximum energy is calculated. After analysis, it was found that the PE values of the three kinds of shaft frequency electric field signals have apparent differences. 3. Combined with the simulation experiment method, the PE values of the maximum average energy IMF of the three typical vessel shaft frequency electric field signals are used as the characteristic parameter, and 100 samples of shaft frequency electric field signals are taken, respectively, for comparison. The results show that the fluctuation range of the same type of E-field signals is minimal, with similar characteristic parameters. However, the typical parameters of the different kinds of E-field signals differ significantly and are well differentiated. Compared with the PE and MPE values of the original target signals, the PE value of the maximum mean energy IMF as a feature parameter can fully reflect the complexity of the target feature signals and has better discriminability. Therefore, the PE value of the maximum mean energy IMF as a feature parameter has good discriminative ability for different vessel shaft frequency E-field signals. Although the feature extraction of different vessels is realized in this paper, the problem of recognizing different types of vessels is not considered.