Application of Fuzzy Modeling Methods for Assessment of the Industrial Control Efficiency


Based on the results of the analysis of the Russian legislation requirements, it was revealed that currently there are no single criteria for assessment of industrial control over compliance with industrial safety requirements. The state standards on the management system were studied and the definitions of the effectiveness and efficiency were considered. It is established that to achieve the goal of industrial control the most important criterion is the concept of efficiency.

To evaluate the industrial control efficiency, it is proposed to use fuzzy logic modeling, since the mathematical tool of fuzzy logic is usually used in cases when the available quantitative information is insufficient, or it is not complete enough to obtain reliable statistically significant conclusions.

To determine the input parameters in the process of modeling, the goals and objectives of industrial control are analyzed. Based on the analysis, three input parameters were distinguished: the coefficient of the elimination of the revealed violations, the coefficient of repetition of the revealed violations, the rank indicator of the group of violations and one output parameter — of industrial control efficiency.

The tool of fuzzy sets implemented in the computer modeling system MATLAB is used. The fuzzy-multiple model has been developed for assessment, analysis and visualization of industrial control performance indicators based on data obtained from internal audits. It is shown that the development of fuzzy models allows to obtain the numerical evaluation of industrial control efficiency.

The obtained modeling results clearly demonstrate the dependence of industrial control efficiency on the considered parameters of internal audits.

1. Fatkhutdinov R.I. Assessment of efficiency of industrial control systems functioning at hazardous production facilities of oil and gas production plants. sb. tezisov: XII Vseros. konf. molodykh uchenykh, spetsialistov i studentov «Novye tekhnologii v gazovoy promyshlennosti» (gaz, neft, energetika) (Collection of the thesis: XII All-Russian conference of the young scientists, specialists and students «New technologies in gas industry» (gas, oil, energy). Moscow: RGU nefti i gaza (NIU) imeni I.M. Gubkina i PAO «Gazprom», 2017. pp. 250. (In Russ.).
2. GOST R ISO 9000: 2015. Quality management systems. Main provisions and vocabulary (with amendment). Available at: (accessed: January 14, 2019). (In Russ.).
3. Fruchtnicht E., Fellers J.W., Hanks C.D. Safety inspections: continuous improvement, Effectiveness & Efficiency. Professional safety volume. 2013. № 7. pp. 28–35.
4. Van Herwaarden A.J.F., Sykes R.M. HSE Auditing. New Orleans: Society of petrolium Engineers, 1996.
5. Murphy H., Janus B. Managing HSE performance through sustainability reporting. SPE E&P Health, Safety, Security and Environmental Conference-Americas, 16–18 March. Denver, 2015.
6. Tarrahi M., Shadravan A. Advanced big data analytics improves HSE management. SPE Bergen One Day Seminar, 20 April. Bergen, 2016.
7. On the organization and implementation of industrial control over compliance with industrial safety requirements at a hazardous production facility: Government Decree of the Russian Federation of March 10, 1999 № 263. Sobr. zakonodatelstva Ros. Federatsii = Collection of the Russian Federation Legislation. 1999. № 11. Art. 1305. (In Russ.).
8. Fatkhutdinov R.I., Klimova I.V. On the single specific problem when mixing commercial oils. Rassokhinskie chteniya: materialy mezhdunar. konf. V 2 ch. Ch. 1 (Rassokhinskie readings: materials of the International conference In 2 parts. Part 1). Ukhta: UGTU, 2018. pp. 279–284. (In Russ.).
9. Klimova I.V., Smirnov Yu.G. Application of fuzzy modeling to predict the disease of staff from exposure to working conditions. Mathematical Modeling. 2017. Iss. 2. pp. 113–116.
10. Klimova I.V., Zakharov D.Yu., Smirnov Yu.G. Information expertise of the results of the calculation of hydrocarbon reserves. Rassokhinskie chteniya: materialy mezhdunar. konf. V 2 ch. Ch. 2 (Rassokhinskie readings: materials of the International conference. In 2 parts. Part 1). Ukhta: UGTU, 2018. pp. 141–146. (In Russ.).
11. Leonenkov A.V. Fuzzy modeling in MATLAB and fuzzyTECH. Saint-Petersburg: BKhV-Peterburg, 2005. 736 p. (In Russ.).
12. Tindova M.G. Fuzzy model of economic assessment of the environmental damage. Ekonomika: vchera, segodnya, zavtra = Economy: yesterday, today, tomorrow. 2012. № 3–4. pp. 129–139. (In Russ.).
13. Jablonowski C.J. Identification of HSE leading indicators using regression analysis. SPE Americas E&P Health, safety, security, and environmental conference, 21–23 March. Houston, 2011.
DOI: 10.24000/0409-2961-2019-2-54-59
Year: 2019
Issue num: February
Keywords : industrial safety modeling efficiency industrial control assessment fuzzy logic
  • Klimova I.V.
    Cand. Sci. (Eng.), Assoc. Prof. Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
  • Smirnov Yu.G.
    Cand. Sci. (Phys.-Math.), Assoc. Prof. Ukhta State Technical University, Ukhta, Russia
  • Fatkhutdinov R.I., Head of the Department of Industrial Safety, occupational Safety and Health, and Environmental Protection Komnedra JSC, Usinsk, Russia Assistant of the Department FSBEI HE «USTU», Ukhta, Russia