A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger

Shang, Gang; Xu, Liyun; Tian, Jinzhu; Cai, Dongwei; Xu, Zhiting

doi:10.1016/j.energy.2023.127326

Cited by 13 publications

(4 citation statements)

References 40 publications

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“…Taking one monitoring point (Shaft seal water pressure) of CSD as an example, this method effectively detects the outlier and remove it, this process can be summarized as Figure 5. The construction data features for CSD originate from diverse sources, exhibiting varying dimensions, orders of magnitude, and significant disparities in their scales, making them unsuitable for direct computation [32]. Hence, the application of normalization is typically an essential preprocessing step in machine learning [33].…”

Section: Data Cleaning and Normalizationmentioning

confidence: 99%

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

Long,

Fan,

Gao

et al. 2024

Applied Sciences

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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.

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Section: Data Cleaning and Normalizationmentioning

confidence: 99%

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

Long,

Fan,

Gao

et al. 2024

Applied Sciences

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“…The original production data between different parameters are often on a different scale; the difference between the values may be very large, and not processing the data may affect the results of the data analysis. In this study, in order to avoid the influence of factors outside of the characteristic weight, the data need to be normalized before carrying out the correlation analysis of the preprocessed data for standardized processing [6]. Taking into account the fact that the numerical changes in the parameter items are not uniform, i.e., they have different distribution characteristics, we used the maximum and minimum normalization method to normalize the data, and the conversion formula is as follows [7]:…”

Section: Correlation Analysis and Feature Selection 41 Data Normaliza...mentioning

confidence: 99%

Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger

Chen,

Yuan,

Wang

et al. 2024

Processes

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Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of “Hua An Long” CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions.

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“…By continuously monitoring and analyzing sensor data, such as vibrations, temperatures, and cutting forces, these algorithms can accurately forecast the lifespan of tools, enabling manufacturers to schedule maintenance activities pre-emptively and avoid costly downtimes. This predictive prowess breathes new life into the manufacturing industry, fostering a proactive approach, reducing unplanned disruptions, and optimizing resource utilization [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis

Rakshith,

Kamath,

Vijay

2023

RAiSE-2023

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A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger

Cited by 13 publications

References 40 publications

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger

Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis

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