A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger
The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6075 |
| _version_ | 1827703486600445952 |
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| author | Bin Wang Shi-dong Fan Pan Jiang Han-hua Zhu Ting Xiong Wei Wei Zhen-long Fang |
| author_facet | Bin Wang Shi-dong Fan Pan Jiang Han-hua Zhu Ting Xiong Wei Wei Zhen-long Fang |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit <i>R</i><sup>2</sup> values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration. |
| first_indexed | 2024-03-10T15:20:44Z |
| format | Article |
| id | doaj.art-718bd75c2852444696fb9494797e28c8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:20:44Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-718bd75c2852444696fb9494797e28c82023-11-20T18:32:42ZengMDPI AGSensors1424-82202020-10-012021607510.3390/s20216075A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction DredgerBin Wang0Shi-dong Fan1Pan Jiang2Han-hua Zhu3Ting Xiong4Wei Wei5Zhen-long Fang6School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaThe dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit <i>R</i><sup>2</sup> values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.https://www.mdpi.com/1424-8220/20/21/6075mud concentrationdata miningsoft sensormachine learningdredger |
| spellingShingle | Bin Wang Shi-dong Fan Pan Jiang Han-hua Zhu Ting Xiong Wei Wei Zhen-long Fang A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger Sensors mud concentration data mining soft sensor machine learning dredger |
| title | A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger |
| title_full | A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger |
| title_fullStr | A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger |
| title_full_unstemmed | A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger |
| title_short | A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger |
| title_sort | novel method with stacking learning of data driven soft sensors for mud concentration in a cutter suction dredger |
| topic | mud concentration data mining soft sensor machine learning dredger |
| url | https://www.mdpi.com/1424-8220/20/21/6075 |
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