A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer
ABSTRACT Real‐time monitoring, prediction, and early warning of operating status during intelligent mining are the key to ensuring stable production. To solve the problem of lag in determining the operating status of a shearer, this study proposes a new method for predicting and warning the real‐tim...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1953 |
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| author | Pei Zhang Yanpeng He Li Ma Changkui Cong |
| author_facet | Pei Zhang Yanpeng He Li Ma Changkui Cong |
| author_sort | Pei Zhang |
| collection | DOAJ |
| description | ABSTRACT Real‐time monitoring, prediction, and early warning of operating status during intelligent mining are the key to ensuring stable production. To solve the problem of lag in determining the operating status of a shearer, this study proposes a new method for predicting and warning the real‐time operating status of the shearer involving the Storm framework, based on parallel optimization of data processing and the gated recurrent unit (GRU) model based on hyperparameter optimization. First, the GRU model is optimized through hyperparameter optimization to achieve adaptive and accurate prediction and early warning of multidimensional state parameters of the shearer. Second, a virtual machine is constructed to host the Storm framework, parallel optimized real‐time processing of data is performed on the Storm framework, and real‐time data flow patterns are constructed to speed up data processing and retrieval, ensuring each tuple is fully processed through the topology structure. Finally, the optimized GRU model is embedded into the optimized Storm framework to achieve real‐time prediction and early warning of different dimensional data of the shearer. The prediction accuracy, early warning accuracy, and processing efficiency of the Storm platform are used as evaluation indicators to analyze and evaluate the model, verifying the efficiency and applicability of the proposed model. Experimental results show that the model has a prediction accuracy of 93%, an early warning accuracy of 93.05%, and consumes 10 s. It can achieve high performance, low latency, and high precision in predicting and providing early warnings for the shearer's state parameters, greatly improving the efficiency of predicting and early warning the operating status parameters of the shearer. This model realizes real‐time prediction and early warning of the shearer's operating status, providing technical support for intelligent mining in coal mines. |
| format | Article |
| id | doaj-art-81037d5ea5334894ac95024937833578 |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-81037d5ea5334894ac950249378335782025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-01131607510.1002/ese3.1953A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a ShearerPei Zhang0Yanpeng He1Li Ma2Changkui Cong3College of Energy Engineering Xi'an University of Science and Technology Xi'an ChinaCollege of Energy Engineering Xi'an University of Science and Technology Xi'an ChinaCollege of Safety Science and Engineering Xi'an University of Science and Technology Xi'an ChinaChina Coal Science & Technology Ecological Environment Technology Co. Ltd. Xi'an ChinaABSTRACT Real‐time monitoring, prediction, and early warning of operating status during intelligent mining are the key to ensuring stable production. To solve the problem of lag in determining the operating status of a shearer, this study proposes a new method for predicting and warning the real‐time operating status of the shearer involving the Storm framework, based on parallel optimization of data processing and the gated recurrent unit (GRU) model based on hyperparameter optimization. First, the GRU model is optimized through hyperparameter optimization to achieve adaptive and accurate prediction and early warning of multidimensional state parameters of the shearer. Second, a virtual machine is constructed to host the Storm framework, parallel optimized real‐time processing of data is performed on the Storm framework, and real‐time data flow patterns are constructed to speed up data processing and retrieval, ensuring each tuple is fully processed through the topology structure. Finally, the optimized GRU model is embedded into the optimized Storm framework to achieve real‐time prediction and early warning of different dimensional data of the shearer. The prediction accuracy, early warning accuracy, and processing efficiency of the Storm platform are used as evaluation indicators to analyze and evaluate the model, verifying the efficiency and applicability of the proposed model. Experimental results show that the model has a prediction accuracy of 93%, an early warning accuracy of 93.05%, and consumes 10 s. It can achieve high performance, low latency, and high precision in predicting and providing early warnings for the shearer's state parameters, greatly improving the efficiency of predicting and early warning the operating status parameters of the shearer. This model realizes real‐time prediction and early warning of the shearer's operating status, providing technical support for intelligent mining in coal mines.https://doi.org/10.1002/ese3.1953GRU modelreal‐time predictive warningshearerstorm framework |
| spellingShingle | Pei Zhang Yanpeng He Li Ma Changkui Cong A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer Energy Science & Engineering GRU model real‐time predictive warning shearer storm framework |
| title | A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer |
| title_full | A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer |
| title_fullStr | A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer |
| title_full_unstemmed | A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer |
| title_short | A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer |
| title_sort | storm frame optimization method for predicting and warning the safety status of a shearer |
| topic | GRU model real‐time predictive warning shearer storm framework |
| url | https://doi.org/10.1002/ese3.1953 |
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