# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
###### Abstract  
The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger's (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger \"Hua An Long\" was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model. mud pump water sealing system; cutter suction dredger; shaft seal water pressure prediction; MCNN-transformer model +
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the pump shaft seal, and to facilitate cooling, lubrication, and sealing, it is important to inject clean water into the pump shaft seal under specified pressure [5]. Determining how to improve the service life of the mud pump and ensuring its safe operation is the focus of research in the dredging research area. It is obvious that MPWSS is a crucial part of ensuring normal operations of mud pump and optimizing dredging operations. Its primary function is to maintain the necessary seal water pressure for the mud pump shaft seal, preventing the ingress of mud into the pump shaft seal [6]. Consequently, maintaining the proper seal water pressure at the shaft is imperative for the uninterrupted and regular operation of the mud pump. Deviations in sealing conditions can result in slurry leakage around the pump, leading to issues such as corrosion and premature bearing failure [7]. Therefore, the seal water pressure must be higher than the pressure on the shaft side to ensure sufficient flow to flush sediment to a safe area [8; 9]. However, the operational environment for dredgers is inherently intricate and influenced by many factors [10]. During each construction period, factors such as soil quality variations, vibrations from wind and waves, and high salinity and corrosion in the sea can cause the sensor of the underwater pump to lose or even fail, resulting in the inability to accurately measure the pressure value of the shaft seal water pressure [11; 12]. Therefore, establishing how to predict the shaft seal water pressure of the underwater pump in real time is crucial to the safe operation of the mud pump. With the development of artificial intelligence, researchers have applied various technologies and methodologies to address issues such as sensor failures, sensor perception enhancement, and parameter prediction [13; 14; 15]. [PERSON] et al. [16] improved forest disturbance detection accuracy using ensemble stacking with Random Forest in remote sensing data analysis. [PERSON] et al. [17] presented a data mining method with model stacked generalization to predict the productivity of cutter suction dredgers. This model stacks five machine learning model and the results show that the performance of the stacked generalization model is greater than other algorithms investigated. [PERSON] et al. [18] employed intelligent data mining algorithms, including XGBoost, achieving over 90% accuracy in predicting cutter suction dredger productivity, surpassing traditional methods. [PERSON] et al. [19] addressed the challenge of time-lag effects in cutter suction dredger (CSD) design, proposing a data mining-based solution for short-term mud concentration forecasting, which employs a hybrid ensemble strategy for forecasting.