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| Study of Mine Ventilation System Assessment Based on Bayes Discriminant Methods |
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Received:August 24, 2018
Revised:September 04, 2018
Accepted:September 06, 2018
Published Online:August 26, 2019
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| DOI： |
| KeyWord:Mine ventilation; Bayes discriminant method;Assessment index system |
| Author | Institution |
| LI Xiao-fei |
Kunming University of Science and Technology,Kunming |
| GUO Zhong-lin |
Kunming University of Science and Technology,Kunming |
| DING Pan |
Kunming University of Science and Technology,Kunming |
| FU Zi-guo |
Kunming University of Science and Technology,Kunming |
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| Abstract: |
| Mine ventilation technology is a prerequisite for ensuring the underground production safety, High-accuracy mine ventilation reliability evaluation can improve ventilation environment and prevent accidents from happening. In order to realize rapid and accurate evaluation of mine ventilation system, the article selects 16 evaluation indices in terms of the technologies, monitoring and abilities. The author refers to a multiple statistic method, sets up a Bayes discriminant analysis and evaluation model, and selects 18 groups of mine ventilation data as learning samples for training and testing. The results show that: when the total covariance matrixs are not equal, Bayes discriminant analysis model of five different training and tested samples still has good evaluation effect. The correct judgment rate in rejudgement method is 100%, while the correct judgment rate of model with cross-confirmation method is 83.33%, 94.44%, 88.89%, 94.44%, and 88.89% respectively. The model provides reference for the reliability evaluation of mine
ventilation system. |
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