# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 6 Conclusions  
This article focuses on the challenges posed by the harsh operating conditions of underwater pumps, where pressure sensors are prone to failures. Such failures can result in the MPWSS's inability to maintain the shaft seal water pressure within the desired range, leading to increased wear and tear on the mud pump. Therefore, this paper proposes a new hybrid model, the MCNN transformer model to predict the shaft seal water pressure of mud pumps; it also and uses actual construction data to verify the effectiveness of the model. The specific research steps are as follows:  
First, this section comprehensively explains the working principle of the MPWSS and its intrinsic connection with the mud pump, emphasizing the key role played by the shaft seal water in protecting the mud pump. Subsequently, data cleaning and abnormal data processing were performed on the dredging datasets to improve data quality, thereby enhancing the accuracy and reliability of subsequent analysises. Third, feature extraction is performed on the preprocessed data. At this stage, based on the proposed key index method of MPWSS, the \"cutter-related indices\", \"pump related indices\" and \"mud transport-related indices\" were established, and combined with the stacking model of XGBoost and Random Forest for feature refinement, and determined 25 characteristics that affect shaft seal water pressure. Fourth, the MCNN transformer model is used. Initially, MCNN is used to convolve the input multi-scale time series data. This method can better capture the  
Figure 17: Prediction results under different models. characteristics of the data, and then the processed data are input into the embedding layer of the Transformer model. The hybrid model combines two advanced neural networks to apply the strengths of different algorithms for shaft seal water pressure prediction. In order to verify the superiority and effectiveness of the model, transformer, MCNN, CNN, XGBoost, BP and RF models were proposed and compared, and RMSE, MSE, R\({}^{2}\), MAE and time were compared. The results show that the MCNN transformer model achieves high prediction accuracy, and the prediction index R\({}^{2}\) of this model can reach as high as 0.9624, achieving good prediction performance. However, the disadvantage of this hybrid model is that it requires much time to make high-precision predictions, which takes up to 23.93 s. It is suitable for scenarios that do not have high time requirements but extremely high accuracy requirements. For scenarios where prediction is required, this research recommends the CNN model with slightly lower accuracy but fast prediction or the XGBoost model with the shortest time. The fault sensor replacement model proposed in this paper can effectively avoid the problem of MPWSS sensor failure causing inaccurate sealing water pressure, and has made outstanding contributions to the operation and maintenance of mud pumps, effectively reducing the risk of mud pump failure and maintenance costs. In summary, the mud pump is the core equipment of CSD, with MPWSS playing a vital role in ensuring its normal operation and extending its service life. Hence, achieving an intelligent MPWSS is extremely significant. This study initially proposes the integration of the advanced MCNN transformer model into the MPWSS, which ensures accurate prediction of shaft seal water pressure when the pressure sensor fails. Nonetheless, due to the pivotal role of the MPWSS in the mud pump's operation, it is imperative to incorporate a standby pump into the seal water pump design to avert significant damage to the mud pump in the event of sealing water pump failure. Therefore, future research should focus on devising a control strategy for the swift transition to the standby seal water pump during major seal water pump failures. Furthermore, the role of balanced water in the mud pump sealing water system is of critical importance.