# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
## 4 Experiment and Analyses  
There are many types of faults that can occur in MCPs in actual operation, and the factors that cause these faults are also various. The common faults of MCPs include rotor unbalance, rotor misalignment, impeller cavitation, rolling bearing failure, and mechanical seal failure. At present, most scholars are focusing on different faults of MCPs for fault identification. However, in the actual operation process, it is impossible to replace the impeller immediately after cavitation occurs, and in most cases, the impeller is damaged to a certain extent before replacement. Therefore, this paper mainly identifies the degree of damage caused by MCP impeller cavitation to the impeller, and determines whether to replace the impeller according to the degree of damage. In order to verify the effectiveness of the MCP status recognition method proposed in this paper, an MCP test bench was built. A vibration acceleration sensor was used to collect the vibration signals of the MCP under different degrees of cavitation damage, and status recognition was carried out using the method proposed in this paper. The experimental bench is shown in Figure 4.  
A unidirectional acceleration sensor was arranged in the MCP solute on the test bench. The sampling frequency was 20,000 Hz, and the sampling time was 0.1 s. First, the vibration signal under the normal state (S1) of the MCP was collected. The operating condition was  
Figure 4: MCP test bench diagram. rated and the speed was 2900 r/min. In order to simulate the MCP impeller cavitation, the MCP impeller was drilled and the cavitation degree of the MCP was defined according to the number of holes drilled in the impeller. These were either 1 hole in the impeller (S2), 2 holes in the impeller (S3), 2.5 holes in the impeller (S4), 3 holes in the impeller (S5), 4 holes in the impeller (S6), 5 holes in the impeller (S7), 6 holes in the impeller (S8), or 6 holes in the impeller with side wear of the impeller (S9). The operating conditions remained the same as S1, and the MCP vibration signals under these 8 states were collected successively. A total of 250 sets of vibration signal data were collected in each state. Figure 5 shows the impellers in S3 and S9. The time domain signals under each MCP state are shown in Figure 6, below. From the perspective of vibration amplitude, the vibration amplitude of an MCP under different states does not increase; regarding the trend of the waveform, only the S5 state is abnormal, and the differentiation in other statuses is not large. The normalized amplitude spectra in the frequency domain under each MCP state are shown in Figure 7, below. As can be seen from this figure, each normalized amplitude spectrum of an MCP under different states has its own characteristics, and several states can be identified with the naked eye, which is also the reason why in this paper we chose to extract MCP features from frequency domain signals. A total of 2250 sets of time domain signals were collected under the 9 states of MCP. Since the acquisition time was 0.1 s, the length of each set of time domain signals was 2000 and the data matrix was 2250 \(\times\) 2000. The time domain signal was transformed via fast Fourier transform and normalized, and the data matrix became 2250 \(\times\) 1000. Then, 150 samples in each state were randomly selected as the training set, and the remaining 100 samples were the test set. The training set matrix was 1350 \(\times\) 1000 and the test set matrix was 900 \(\times\) 1000. The specific division results are shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**MCP Status** & **Sample Size** & **Number of Training Sets** & **Number of Test Sets** \\ \hline S1 & 250 & 150 & 100 \\ S2 & 250 & 150 & 100 \\ S3 & 250 & 150 & 100 \\ S4 & 250 & 150 & 100 \\ S5 & 250 & 150 & 100 \\ S6 & 250 & 150 & 100 \\ S7 & 250 & 150 & 100 \\ S8 & 250 & 150 & 100 \\ S9 & 250 & 150 & 100 \\ \hline \end{tabular}
\end{table} 
Table 1: Dataset partitioning. Figure 5: (a) S3 state; (b) S9 state.