Shevchenko V.G., Nosal D.A. Risk assessment of mine labor safety by fuzzy logic

Details

Parent Category: Geo-Technical Mechanics, 2020

Category: Geo-Technical Mechanics, 2020, Issue 150

Geoteh. meh. 2020, 150, 35-45

DOI: https://doi.org/10.15407/geotm2020.150.035

 

RISK ASSESSMENT OF MINE LABOR SAFETY BY FUZZY LOGIC

¹Shevchenko V.G., ²Nosal D.A.

¹Institute of Geotechnical Mechanics named by N. Poliakov NAS of Ukraine, ² «DTEK ENERGO» LLC

UDC 622.8.012.2:658.382.3

Language: Russian

Annotation.

Currently, enterprises of the DTEK ENERGO LLC business-unit Coal identify dangers and asses risk by the procedure, which includes the following: hazard and risk identification, risk assessment and determining of the risk management level, development of control and minimization measures, implementation of the measures, monitoring and review. For each production operation, the following are determined: existing sources of danger or possible dangerous situations; potential consequences of their influence on people, production process and/or property; currently used methods of danger prevention; consequences and probability of a possible accident at work, determined in points; magnitude (degree) of risk. Based on the results of the risk assessment, risk assessment cards are compiled for each operation. For each source of danger, the matrix determines the level of risk. Risk is assessed on the basis of the conclusion of the expert group. At the same time, the initial conditions and prerequisites are often not clearly defined, and it is also impossible to speak clearly about a clear risk assessment in accordance with the potential probability and consequences identified by experts. In the existing procedure, level of risk takes strictly defined discrete values. At the same time, risk is a continuous value, taking all possible values in a given range. Often, an unambiguous, clear risk assessment is required based on fuzzy conditions and assumptions. A risk assessment using fuzzy logic methods is proposed, which is based on the concept of fuzzy sets. The potential probability and potential effects act as input variables, and the risk level is used as the output value, for which triangular membership functions are chosen. As the fuzzy inference algorithm, the Mamdani algorithm is chosen - the most common method of logical inference in fuzzy systems. The dependencies between the input and output variables are non-linear, and the output value of the risk level never reaches extreme values in the ranges of its change. On average, value of the risk level will tend to average within the specified ranges of change, i.e. level of risk is unevenly distributed in the ranges of its change. The proposed risk assessment by fuzzy logic methods allows to quantify the risk level as a continuous value, and also takes into account the nonlinear nature of the dependences of the risk level on potential probability and potential effects. Based on this assessment, one can elaborate more accurate measures for reducing risks and create a register of unacceptable risk levels.

Keywords:

labor protection, risk assessment, potential probability and effects, risk level, fuzzy conditions and prerequisites, membership functions, fuzzy inference algorithm.

References

1. Uzahalnena analitychna informatsiia na pidstavi richnykh zvitiv pidpryiemstv Minenerhovuhillia pro stan okhorony pratsi za 2018 rik. Vuhilno-promyslovyi kompleks [El. resurs]. – Rezhym dostupu: http://mpe.kmu.gov.ua/minugol/control/uk/publish/-article?art_id=245349333&cat_id=245293173.

2. DSTU ISO 9001:2015 (ISO 9001:2015, IDT) Systemy upravlinnia yakistiu.

3. DSTU OHSAS 18001:2010 Systemy upravlinnia hihiienoiu ta bezpekoiu pratsi. Vymohy.

4. ISO 45001:2018 Occupational health and safety management systems — Requirements with guidance for use.

5. Bunko T.V., Shevchenko V.H., Yashchenko Y.A., Kokoulyn Y.E. (2016) Sovershenstvovanye systemi upravlenyia proyzvodstvom i okhranoi truda. Geo-technical mechanics. Dnipropetrovs’k. Vol. 127. Pp. 3-17.

6. Zade L.A. (1976) Ponyatie lingvisticheskoy peremennoy i ego primenenie k prinyatiyu priblizhennyih resheniy. Moskva: Mir. 165 s.

7. Mamdani E.H. (1974) Application of fuzzy algorithms for control of simple dynamic plant // Proceeding of the IEEE, Control and Science. Vol. 121. № 12. P. 1585-1588.

https://doi.org/10.1049/piee.1974.0328

8. Shevchenko V.G. (2009) Modelirovanie podsistem upravleniya protsessom dobyichi uglya v usloviyah nechetkoy informatsii / V.G. Shevchenko // GeotehnIchna mehanIka. DnIpropetrovsk. Vol. 82. Pp. 130-144.

9. Shevchenko V.G. (2009) Raschet parametrov nechetkogo regulirovaniya zvena gornorabochih v kombaynovoy i strugovoy lavah // MaterIali mIzhnarodnoyi konferentsIyi “Forum gIrnikIv – 2009”. Dnipropetrovsk: NGU. Pp. 89-94.

10. Shevchenko V.G., Slaschyov A.I. and Yalanskiy Aleks.A. (2016) Primenenie metodov nechetkoy logiki v zadachah otsenki stepeni opasnosti geotehnicheskih sistem // Nauchno-tehnicheskoe obespechenie gornogo proizvodstva: Sbornik nauchnyih trudov IGD im. D.A. Kunaeva. Almatyi. Vol. 88. Pp. 186-194.

About the authors

Shevchenko Volodymyr Heorhiiovych, Doctor of Technical Sciences (D. Sc), Professor, Scientific Secretary of the Institute, Institute of Geotechnical Mechanics named by N. Poliakov NAS of Ukraine (IGTM, NAS of Ukraine), Dnipro, Ukraine, This email address is being protected from spambots. You need JavaScript enabled to view it.

Nosal Dmytro Oleksandrovych, Master of Science, Manager of the Department of Labor Protection and Industrial Safety of the Coal Mining Directorate, DTEK ENERGO LLC, Pavlograd, Ukraine, This email address is being protected from spambots. You need JavaScript enabled to view it.

• < Prev
• Next >