# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 5 Result and Discussion  
_Experimental Prediction Result Comparisons and Analysis_  
In order to verify the performance of the MCNN transformer model in predicting shaft seal water pressure, it was compared with the MCNN, transformer, RF, BP and XGBoost prediction models. For the MCNN transformer model, the MCNN method is employed to process various types of input data. Through multi-layer convolution operations, it captures features from the time series data. The extracted time series features are then enhanced and transformed to improve feature representation and the model's generalization ability. Additionally, the MCNN model reconstructs the time series data and converts them into the input format processed by the embedding layer of the Transformer model. In addition, this study compares the hybrid model MCNN-Transformer with single models including MCNN, transformer, Random Forest, CNN, BP, and XGBoost. To assess the prediction performance of these models, four commonly used evaluation indexes, R\({}^{2}\), MAE, MSE, and MAPE, are employed. Moreover, time is considered one of the evaluation criteria for assessing the timeliness of the models. The prediction results are presented in Table 2. Through observing and analyzing the evaluation indicators of the seven models, it is evident that the MCNN transformer model exhibits the highest coefficient of determination (R\({}^{2}\)), while the errors (MAE, MSE, MAPE) between the predicted and actual values are the smallest. In order to visually represent the relationship between the evaluation indicators of each model more intuitively, we have plotted the radar chart of these seven models, as shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IndexModel** & **R\({}^{2}\)** & **MAE** & **MAPE** & **MSE** & **Time(s)** \\ \hline MCNN-Transformer & 0.9624 & 0.0108 & 0.0051 & 0.00018 & 23.93 \\ \hline Transformer & 0.9273 & 0.0147 & 0.0070 & 0.00035 & 7.47 \\ \hline MCNN & 0.9313 & 0.0144 & 0.0069 & 0.00033 & 5.79 \\ \hline CNN & 0.9431 & 0.0131 & 0.0062 & 0.00027 & 4.95 \\ \hline BP & 0.8895 & 0.0188 & 0.0089 & 0.00054 & 3.80 \\ \hline Random Forest & 0.8078 & 0.0247 & 0.0118 & 0.00094 & 0.03 \\ \hline XGBost & 0.8455 & 0.0234 & 0.0111 & 0.00075 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model evaluation. Figure 14: Evaluation indicators comparison of models. By analyzing Table 2 and integrating findings from actual dredging engineering research, we can draw conclusions on how to effectively and appropriately integrate artificial intelligence technology with dredging engineering. 1. It is obvious that the MCNN transformer model achieved the best performance, with the highest coefficient of determination of 0.9624, lowest MAE of 0.0108, MAPE of 0.0051 and MSE of 0.00018. On the contrary, the traditional single neural network model Random Forest (RF) obtains the worst performance, with the lowest coefficient of determination of 0.8078, highest MAE of 0.0247, MAPE of 0.0118 and MSE of 0.00094. 2. Compared to a single transformer model, the hybrid model employs the MCNN model for data convolution before inputting datasets into the transformer model which has indeed enhanced the predictive performance. However, the time required for the MCNN transformer model to predict the shaft end sealing water pressure is 23.93 s, approximately three times longer than that of the Transformer model. 3. For dredging projects, if the pressure sensor of the MPWSS fails during the construction process, to ensure that the mud pump can operate normally and continuously, it is necessary to quickly restore the sensing data of the shaft seal water pressure. The shortest time-consuming model the XGBoost model could be used for prediction. In comparison, this model takes the shortest time to predict, only 0.02 s, but it has a coefficient of determination of 0.8455. However, if the wear of the mud pump is serious during construction, a model with higher accuracy and shorter time consumption is essential.