# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
###### Abstract
The approach demonstrates high accuracy and real-time capability, potentially improving CSD operations by providing timely mud concentration references. Specifically, the digital twin-driven virtual sensor approach has become a popular research direction at present, which can address the problem of sudden sensor failure [20; 21]. Digital twin can improve the accuracy and efficiency of data prediction for complex equipment working in a harsh environment, especially in prognostics and health management (PHM) [22; 23]. [PERSON] et al. [24] introduced a digital twin-driven virtual sensor (DTDVS) to predict dredger status, diagnose construction behavior, and provide accurate early warning of fault conditions by analyzing residual between physical and virtual sensors. [PERSON] et al. [25] introduced a digital twin approach that utilizes machine learning and prognostic algorithms for the analysis and prediction of risk probability in oil pipeline systems. [PERSON] et al. [26] proposed an innovative short-term water demand forecasting framework. The maximum information coefficient is used to select the input function, the attention BiLSTM network is used to design the deep learning architecture, and the XGBoost algorithm is combined for residual correction. As a result, this approach led to the development of a Virtual Intelligent Integrated Automated Control System designed to forecast the risk rate in the oil industry. In the field of natural language processing (NLP), deep learning network transformer models have received more and more attention. This model has been successfully used in image recognition, target detection, time series prediction in computer vision and text translation in NLP [27]. [PERSON] et al. [28] proposed TempreNet to predict subsurface seawater temperature by combining transformer and convolutional neural network (CNN). [PERSON] et al. [29] introduced a model called DeepST-CC for particle image velocimetry (PIV)estimation, which embeds an attentional transformer and cross-correlation strategy. In the regional wave prediction area, [PERSON] et al. [30] analyzed the disadvantages of CNN and proposed a vision transformer-based regional wave prediction model (VIT-RWP); this model applied convolution and transpose convolution as encoder and decoder, preserving positional information and relative point positions within the region, and completed higher-accuracy predictions. [PERSON] et al. [31] integrated the random decrement technique (RDT) and transformer neural network, and this research verified that the proposed RDT-transformer method can accurately determine the location of structural damage and identify its severity. Based on the above literature review, the relative research on the reliability enhancement of mud pump is limited, especially the MPWSS. In the fields related to mud pumps, CFD (Computational Fluid Dynamics) simulation analysis is mostly used to improve the reliability of mud pumps. The difference in this article is that it focuses on the water sealing system of the mud pump, and uses machine learning and intelligent algorithms to predict the sealing water pressure required by the mud pump. These technologies have not yet been applied in MPWSS to address the security issues in mud pumps and protect them in dredging projects. Additionally, it is obvious that hybrid ensemble strategy and digital twin-driven approach can improve the prediction of sensor data effectively. However, the above-proposed model can be further optimized and improved. Most researchers adopt the prediction algorithm data directly, without considering that the prediction of data is essentially a regression problem, and the embedding layer of most models is not suitable for feature extraction in regression problems. Some researchers use CNN combined with the transformer model, but this can only be used for single-scale time series prediction and is not suitable for the corresponding regression prediction problems of multi-scale time series.