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| Optimization of mine wind speed sensors layout based on CatBoost algorithm |
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Received:December 10, 2024
Revised:March 18, 2025
Accepted:March 19, 2025
Published Online:April 30, 2026
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| DOI：10.3969/j.issn.1005-7854.2026.01.016 |
| KeyWord:mine ventilation;machine learning;feature selection;CatBoost;ensemble learning |
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| 1.Zijin College of Geology and Mining, Fuzhou University, Fuzhou 350108, China;2.Guizhou Zijin Mining Co. Ltd., Zhenfeng 562200, Guizhou, China;3.Zijin Mining Group Co. Ltd., Shanghang 364200, Fujian, China |
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| Abstract: |
| Sensors can provide effective reference data for the safe operation and management of mine ventilation systems. In order to solve the problem of sensor layout in mine ventilation fault diagnosis, based on the ensemble learning theory, the CatBoost algorithm is introduced into the mine ventilation fault diagnosis, and a mine ventilation fault diagnosis and sensor layout optimization method based on CatBoost algorithm is proposed. Taking the air volume of the branch of the mine ventilation network and the location of the fault as the input and target labels, respectively, the fault diagnosis and feature selection of the ventilation network are carried out by using the ensemble learning algorithm CatBoost based on the decision tree, and on the basis of ensuring the accuracy of the model, the important network branches are reasonably selected to deploy wind speed sensors, and the number of sensors is reduced to lower the monitoring cost of the ventilation system. Taking the classical angular (Case 1) and the indoor experiment (Case 2) based on the actual layout of the mine as examples, three common machine learning algorithms (Logistic Regression, Support Vector Machine, and Artificial Neural Network) are compared and analyzed to verify the feasibility and superiority of the CatBoost ensemble learning method. The results of the two examples show that with the increase of the number of training branches, the accuracy of the ventilation network fault increases at first and then remains constant, which shows that the wind speed sensor can be effectively monitored by selecting important branches. Compared with common machine learning models, the CatBoost model has a significantly better prediction performance than other models, and can use the minimum number of sensors to obtain the highest fault diagnosis accuracy. Therefore, the optimization method of wind speed sensors deployment based on CatBoost algorithm can reasonably determine the number and location of sensor deployment, effectively save the cost of mine ventilation network monitoring, and conform to the development trend of green intensive mine development. |
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