# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
## 4 Experiment and Analyses
The training set matrix was input into the SSAE model for feature extraction, and the parameters of the SSAE model were set as described in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Structure** & **Sparse Penalty Factor** & **Sparse Parameter** & **Activation Function** \\ \hline
1000-250-60-15 & 1 & 0.5 & Sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SSAE model parameter settings. Figure 6: MCP nine-state time domain waveform diagram. Figure 7: MCP nine-status normalized amplitude spectra. Fifteen characteristic parameters extracted under the nine MCP states have been randomly selected for display, as shown in Figure 8 below. It can be seen from this figure that the characteristics in the MCP vibration signals extracted with the SSAE can reflect their different statuses. The extracted feature parameters were input into the classifier for classification training and verified through the test set. The verification matrix is shown in Figure 9. As can be seen from Figure 9, after feature extraction via the SSAE, the MCP status recognition accuracy reaches 100%, and the effect is very good. Figure 8: MCP nine-state characteristic parameter curves. Figure 9: SSAE feature confusion matrix. In order to compare the advantages and disadvantages of the proposed method with other methods, two other methods have been selected for comparison. The first method is to extract 16 time domain characteristics of MCP vibration signals under different states, including the peak-to-peak value, variance, kurtosis, skewness, waveform factor, peak factor, margin factor, etc. The 16 features of the training set are input to the classifier for classification training and verified with the test set. The second method is to decompose each vibration signal of the MCP in different statuses into five modal components using VMD, and calculate the fuzzy entropy of the five modal components as its characteristics. The fuzzy entropy of modal components in each state of the training set is input to the classifier for classification training and verified using the test set. The time domain features of nine statuses have been randomly selected for display, as shown in Figure 10. As can be seen from Figure 10, the time domain feature differentiation between different MCP statuses is very poor. The 16 time domain features of the training set were input into the classifier for classification training. The confusion matrix verified with the test set is shown in Figure 10. As can be seen from Figure 11, the overall accuracy of MCP status recognition using this method is only 71.2%, and the effect is not good. The training set was decomposed into VMD: each signal was decomposed into five modal components, and the fuzzy entropy of the five modal components was calculated. The fuzzy entropy trends under nine states are shown in Figure 12, below. It can be seen from Figure 11 that the trend of fuzzy entropy can roughly distinguish between different statuses of the MCP, but the fuzzy entropy trends in the S1 state and S6 state are close. Five fuzzy entropy features in each state of the training set were input to the classifier as features for training. The confusion matrix verified with the test set is shown on then Figure 12. As  
Figure 10: Variation trend of time domain characteristic parameters of an MCP in nine states. can be seen from Figure 13, the overall accuracy of MCP status recognition with this method is 87.7%, and the effect is much better than that of the time domain characteristics method. Figure 6:0 Time domain feature confusion matrix. Figure 6:1 Fuzzy entropy trends of nine states of an MCP. Status recognition of an MCP based on the SSAE, MCP status recognition based on time domain features, and MCP status recognition based on fuzzy entropy features have been compared.