Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology
The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/10/966 |
| _version_ | 1827720651702534144 |
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| author | Xuejun Liang Juan Wu Kaiyi Ruan |
| author_facet | Xuejun Liang Juan Wu Kaiyi Ruan |
| author_sort | Xuejun Liang |
| collection | DOAJ |
| description | The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor’s state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs. |
| first_indexed | 2024-03-10T21:06:26Z |
| format | Article |
| id | doaj.art-f15b15bab640400ca00e657a35ffd317 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T21:06:26Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-f15b15bab640400ca00e657a35ffd3172023-11-19T17:08:45ZengMDPI AGMachines2075-17022023-10-01111096610.3390/machines11100966Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin TechnologyXuejun Liang0Juan Wu1Kaiyi Ruan2College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaThe temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor’s state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs.https://www.mdpi.com/2075-1702/11/10/966mine hoistdigital twinrecurrent neural networkstate prediction |
| spellingShingle | Xuejun Liang Juan Wu Kaiyi Ruan Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology Machines mine hoist digital twin recurrent neural network state prediction |
| title | Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology |
| title_full | Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology |
| title_fullStr | Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology |
| title_full_unstemmed | Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology |
| title_short | Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology |
| title_sort | simulation modeling and temperature over advance perception of mine hoist system based on digital twin technology |
| topic | mine hoist digital twin recurrent neural network state prediction |
| url | https://www.mdpi.com/2075-1702/11/10/966 |
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