The Information Methods to Identify Difficult-to-Control Factors Affecting Emergency Gas Contamination in Mine Excavations


The article is dedicated to the crucial problem of occupational safety for personnel at work and the hazardous operation of coalmines due to gas explosions. Natural, technogenic, natural & technogenic, as well as geomechanic factors causing methane accumulation in excavations and following emergency explosions have been described. These factors are classified as so-called difficult-to-control as they cannot be under control during workflows and cannot be managed by means and methods of mining production. The task of elaborating on methods of their identification has been set as the determination of signs (parameters) of emergencies serving as a source of information. The practical result of identification is the prevention of a combination of conditions and circumstances under which an accident occurs, considering the interaction with factors under control. The information about the signs of emergency in mines is inherently weak and complex. Due to the nature of physical phenomena always accompanied by noise, the numerical values of signs of emergency are random. Therefore, the description and identification of abrupt changes in difficult-to-control factors considered emergency have been performed by using entropy-probabilistic concepts and characteristics. It has been shown that the information methods of identification are based on well-known principles of the general theory of informational optimization of systems aimed at minimizing the uncertainty of solving the identification problem. The comparative analysis of the existing theoretical methods to find the optimal identification strategy has shown that with the reduction of the volume of signs specific for an emergency or their low information content as well due to noise, D-information strategies are the only possible ones. One of such strategies is based on the Shannon-Fano statistical code, whereas the other is based on the method of informational dichotomy. Testing these strategies has demonstrated their technical applicability.

1. Smirnyakov V.V., Smirnyakova V.V. Unhandy factors in statistics of accidental gas and dust explosions in coal mines in Russia. Gornyy zhurnal = Mining Journal. 2016. № 1. рр. 30–34. (In Russ). DOI: 10.17580/gzh.2016.01.07
2. Kupriyanov, V.V., Temkin, I.O., Bondarenko, I.S. Study of the Time Characteristics for Emergency Situations in the Coal Mines. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2022. № 1. рр. 39–45. (In Russ). DOI: 10.24000/0409-2961-2022-1-39-45
3. Li R. Optimal estimations, determination of characteristics and management. Мoscow: Nauka, 1966. 176 р. (In Russ).
4. Pontryagin L.S. Continuous groups. Мoscow: Nauka, 1973. 519 р. (In Russ).
5. Improving Safety when Extracting Water-soluble Ores by Optimizing the Parameters of the Backfill Mass. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2021. № 1. pp. 53–59. (In Russ). DOI: 10.24000/0409-2961-2021-1-53-59
6. Zhan J., Mao J., Liu Y., Guo J., Zhang M., Ma S. Learning discrete representation via constrained clustering for effective and efficient dense retrieval. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. рр. 1328–1336. DOI: 10.48550/arXiv.2110.05789
7. Trofimov V.B., Temkin I.O., Solodov S.V. Application of case-based reasoning in hazard evaluation in complex process flow control. Eurasian Mining. 2023. № 2. рр. 41–46. DOI: 10.17580/em.2023.02.09
8. Jin Jiang-tao, Xu Zi-fei, Li Chun, Miao Wei-pao, Li Gen. Bearing Fault Diagnosis Based on VMD Energy Entropy and Optimized Support Vector Machine. Acta Metrologica Sinica. 2021. Vol. 42. Iss. 7. рр. 898–905. DOI: 10.3969/j.issn.1000-1158.2021.07.11
9. Liu Jian-Chang, Quan He, Yu Xia, He Kan, Li Zhen-Hua. Rolling bearing fault diagnosis based on parameter optimization VMD and sample entropy. Acta Automatica Sinica. 2022. Vol. 48. № 3. рр. 808–819. DOI: 10.16383/j.aas.c190345
10. Yu M., Guo G., Fang M., Cong H. Intershaft bearing compound faults identification by using VMD and new index: the activity parameter in Hjorth parameters. Nonlinear Dynamics. 2022. Vol. 110. рр. 2657–2672. DOI: 10.10075-11071-022-07753-4
11. Liu Q., Zhao R., Yang Z. Research of fault recognition method of rolling bearings based on K-VMD envelope entropy and SVM. Noise and Vibration Control. 2022. Vol. 42. № 3. pp. 92–97. DOI: 10.3969/j.issn.1006-1355.2022.03.016
12. Barabash Yu.L., Varskiy B.V., Zinovev V.T., Kirichenko V.S., Sapegin V.F. Problems of statistical recognition theory. Мoscow: Sovetskoe radio, 1967. 400 p. (In Russ).
13. Fu K. Consistent methods of pattern recognition and machine learning. Мoscow: Nauka, 1971. 256 p. (In Russ).
14. Shennon K. Works on information theory and cybernetics. Мoscow: Izdatelstvo inostrannoy literatury, 1963. 830 p. (In Russ).
15. Guisu S. Information technology with applications. New York: McGrow-Hill, 2017. 439 p.
DOI: 10.24000/0409-2961-2024-3-65-72
Year: 2024
Issue num: March
Keywords : occupational safety coal mine mine workings identification difficult-to-control factors emergency explosion informational methods emergency gas contamination нештатная ситуация informational dichotomy method Shannon-Fano code