# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 4 Experiments and Results
### Ablation Experiments
#### 4.2.2 The Effectiveness of the Improved Coarse-Grained Multi-Scale Feature Extraction Method  
In this paper, an improved coarse-grained multi-scale feature extraction method is proposed. The reconstructed signal is processed by improved coarse-grained processing at different scales. The effectiveness of the improved algorithm is verified by comparing the results of inter-turn short circuit fault diagnosis using the improved multi-scale feature extraction method with those of using the traditional multi-scale feature extraction method. It should be noted that the data preprocessing part adopts the improved VMD decomposition method, and the fault diagnosis part adopts the unimproved Bi-LSTM neural network model. After the decomposed signal is obtained by the improved VMD decomposition method, the decomposed signal is reconstructed. The decomposed signal is reconstructed by kurtosis value, and the decomposed components with more impact information can be screened out. Table 1 shows the kurtosis values of six signals decomposed by VMD from three samples for each of the five faults. In reference [18], the decomposition signal with a kurtosis value greater than 3 is superimposed and reconstructed. The reconstructed signal is subjected to different scales of improved coarse-grained processing to obtain its distribution at different scales of 1-10. For each new set of coarse-grained time series, its time-frequency domain eigenvalues are obtained, and then the average value of the eigenvalues of \(\tau\) time series is calculated. The 19 time-frequency domain eigenvalues under the time scale \(\tau\) can be obtained. There are 14 time domains, which are the maximum value, minimum value, average value, peak-peak value, average value of absolute value, variance, standard deviation, kurtosis, skewness, root mean square, waveform factor, peak factor, pulse factor, and margin factor. There are five frequency domains, which are the center of gravity frequency, mean square frequency, root mean square frequency, frequency variance, and frequency standard deviation. Each scale has 19 features, and there are 190 features in 10 scales. The PCA was used to extract the main features and normalize them by the Map minmax function. Figures 12-14 show the training set accuracy, confusion matrix, and 2D scatter plot obtained by using the improved multi-scale feature extraction method for inter-turn short circuit fault diagnosis. Figure 11: The 2D scatter plot. (**a**) The 2D scatter plot in the training process by the unimproved VMD. (**b**) The 2D scatter plot in the training process by the improved VMD. Figure 12: The accuracy of the training set using the improved multiscale feature extraction. Figure 13: The confusion matrix of using the improved multiscale feature extraction. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Failure Mode** & **Sample Label** & \multicolumn{2}{l}{**Kurtosis Value after VMD**} & & & & \\ \hline  & S1 & 6.24 & 1.52 & 25.71 & 1.53 & 23.65 & 8.96 \\
1 & S2 & 3.18 & 3.38 & 1.52 & 12.87 & 1.51 & 23.94 \\  & S3 & 3.42 & 3.08 & 1.52 & 12.38 & 1.52 & 16.32 \\  & S4 & 3.05 & 3.40 & 1.52 & 12.13 & 1.51 & 29.16 \\
2 & S5 & 2.89 & 1.52 & 22.57 & 1.54 & 24.08 & 13.42 \\  & S6 & 2.98 & 1.53 & 20.10 & 1.54 & 20.91 & 16.10 \\  & S7 & 3.03 & 3.39 & 1.53 & 11.95 & 1.52 & 31.91 \\
3 & S8 & 3.44 & 2.98 & 1.53 & 11.97 & 1.52 & 38.54 \\  & S9 & 3.46 & 2.81 & 1.53 & 11.44 & 1.52 & 30.73 \\  & S10 & 3.57 & 1.53 & 19.02 & 1.55 & 15.55 & 21.01 \\
4 & S11 & 3.18 & 1.53 & 17.95 & 1.55 & 16.87 & 19.15 \\  & S12 & 3.02 & 3.36 & 1.53 & 12.25 & 1.52 & 21.24 \\  & S13 & 5.20 & 1.52 & 52.68 & 9.03 & 1.54 & 21.00 \\
5 & S14 & 6.42 & 1.53 & 48.69 & 9.23 & 1.54 & 20.98 \\  & S15 & 2.38 & 1.53 & 54.71 & 9.05 & 1.54 & 21.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The kurtosis values of five faults after VMD.