A Smart Control and Monitoring of a Pumping System

Mahjoub, Ridha

doi:10.1007/978-3-031-14615-2_1

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Mahjoub implemented an intelligent automated solution that integrates algorithms into microcontroller units, connecting PLC, operation panels, and industrial networks to web servers. This allows for online control, anomaly detection, and decision-making to enhance the availability, reliability, and safety of production systems [15]. Despite notable progress, traditional unmanned monitoring still faces limitations, necessitating regular maintenance by technical personnel.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Liu,

Chen,

Zhang

et al. 2024

Preprint

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As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. Currently, As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. Currently, It is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment and misjudgement, resulting in huge property losses. Based on this problem, this paper proposes a visualization and analysis method for unmanned pumping stations on a dynamic platform based on data fusion technology. First, the method uses the transfer learning method to make ResNet18 model obtain generalization ability. Secondly, the method uses the ResNet18 model to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM) model, Finally, the method uses the LSTM model outputs the classification results. The experimental results show that the algorithm model can achieve an accuracy of 99.032%, effectively recognize and classify pump station images, and reduce the occurrence of pump station accidents.

show abstract

Section: Introductionmentioning

confidence: 99%

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Liu,

Chen,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Mahjoub implemented an intelligent automated solution that integrates algorithms into microcontroller units, connecting PLC, operation panels and industrial networks to web servers. This allows for online control, anomaly detection and decision-making to enhance the availability, reliability and safety of production systems [18]. Despite notable progress, traditional unmanned monitoring still faces limitations, necessitating regular maintenance by technical personnel.…”

mentioning

confidence: 99%

Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Liu,

Chen,

Zhang

et al. 2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.

show abstract

A Smart Control and Monitoring of a Pumping System

Cited by 2 publications

References 8 publications

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

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