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

• Published in: Energies (Basel) Vol. 14; no. 21; p. 6889
• Main Authors: Huang, Yuxin, Fan, Jingdao, Yan, Zhenguo, Li, Shugang, Wang, Yanping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2021
• Subjects: Accident prevention; Algorithms; apriori algorithm; association rules; Cluster analysis; Clustering; Coal mines; Coal mining; Confidence intervals; Data mining; Datasets; Gas monitoring; gas risks warning; Information processing; k-means algorithm; Mines; Mining accidents & safety; Neural networks; Occupational safety; Optimization; outlier detection; Outliers (statistics); Work face
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14216889

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.