Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

Yuxin Huang, Jingdao Fan, Zhenguo Yan, Shugang Li and Yanping Wang
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Yuxin Huang: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Jingdao Fan: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Zhenguo Yan: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Shugang Li: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Yanping Wang: College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Energies, 2021, vol. 14, issue 21, 1-19

Abstract: In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k -means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k -means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

Keywords: apriori algorithm; association rules; k -means algorithm; outlier detection; gas risks warning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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