Guaranteed Estimates of the Distribution Function of the Residual Life of Process Equipment at Industrial Enterprises


Annotation:

The article discusses the functioning processes of the process equipment at industrial enterprises, including numerical control machines (CNC), metal cutting machines, press-forging and bending equipment, welding and laser equipment, molding and casting equipment, etc. The problem of determining guaranteed estimates of the distribution function of process equipment’s residual life is formulated as a problem of finding guaranteed (lower and upper) estimates of the probabilistic functionality on the multitude of distribution functions with preset timings equal to the distribution timings determined based on the initial small sample size of trouble-free operation timings of process equipment. An approach to finding guaranteed estimates of the residual life distribution function in conditions of incomplete data on the process equipment operation time before a failure based on the results of the solution for Markov’s timing problem has been proposed. Guaranteed estimates of residual life distribution functions are based on a multitude of discrete distribution functions that meet the timing limitations. Examples of finding guaranteed estimates of process equipment residual life distribution function are provided. 

References:
1. Sedykh L.V. Progressive process equipment: study manual. Мoscow: MISiS, 2017. 95 p. (In Russ.).
2. GOST 27.002—2015. Dependability in technics. Terms and definitions. Available at: https://files.stroyinf.ru/Data2/1/4293754/4293754027.pdf (accessed: July 1, 2024). (In Russ.).
3. Sadykhov G.S., Savchenko V.P., Eliseeva O.V. Basics of product’s residual life assessment. Vestnik MGTU im. N.E. Baumana. Ser. «Estestvennye nauki» = Herald of the Bauman Moscow State Technical University. Series Natural Sciences. 2011. № S3. рр. 83–99. (In Russ.).
4. Makhutov N.A., Gadenin M.M., Pecherkin A.S., Krasnyh B.A. Scientific Problems of Service Life Determination and Management of Industrial Objects Safe Operation Life. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2019. № 4. pp. 7–15. (In Russ.). DOI: 10.24000/0409-2961-2019-4-7-15
5. Makhutov N.A., Gadenin M.M., Pecherkin A.S., Krasnyh B.A. Calculation and Experimental Approaches to the Analysis and Provision of the Service Life and Safe Operation Life of Industrial Facilities. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. 2020. № 1. pp. 7–15. (In Russ.). DOI: 10.24000/0409-2961-2020-1-7-15
6. Si X.-S., Wang W., Hu C.-H., Zhou D.-H. Remaining useful life estimation — A review on the statistical data-driven approaches. European Journal of Operational Research. 2011. Vol. 213. Iss. 1. pp. 1–14. DOI: 10.1016/j.ejor.2010.11.018
7. Mathew V., Toby T., Singh V., Rao B.M., Kumar M.G. Prediction of Remaining Useful Lifetime (RUL) of Turbofan Engine using Machine Learning. IEEE International Conference on Circuits and Systems (ICCS 2017). 2017. DOI: 10.1109/ICCS1.2017.8326010
8. Baykhelt F., Franken P. Reliability and maintenance. Mathematical approach. Мoscow: Radio i svyaz, 1988. 392 p. (In Russ.).
9. Gnedenko B.V., Belyaev Yu.K., Solovev A.D. Mathematical methods in reliability theory. Basic characteristics of reliability and their statistical analysis. 2-e izd., ispr. i dop. Мoscow: Librokom, 2012. 582 p. (In Russ.).
10. Lomakin M.I., Sukhov A.V., Dokukin A.V., Niyazova Y.M. Assessment of reliability indicators of space vehicles under conditions of incomplete data. Kosmicheskie issledovaniya = Cosmic Research. 2021. Vol. 59. № 3. pp. 235–239. (In Russ.). DOI: 10.31857/S0023420621030080
11. Lomakin M., Buryi A., Dokukin A., Niyazova J. Strekha А., Balvanovich A. Estimation of quality indicators based on sequential measurements analysis. Available at: http://www.ijqr.net/journal/v14-n1/10.pdf (accessed: July 1, 2024). (In Russ.).
12. Lomakin M.I. Guarantees estimates of trouble-free operation probability in the class of distributions with fixed timing. Avtomatika i telemekhanika = Automation and Remote Control. 1991. № 1. pp. 154–161. (In Russ.).
13. Bruce P., Bruce A. Practical Statistics for Data Scientists: 50 Essential Concepts. Sebastopol: O'Reilly Media, Inc., 2017. 318 р.
14. Lin G.D. Recent developments on the moment problem. Journal of Statistical Distributions and Applications. 2017. № 4 (1). DOI: 10.1186/s40488-017-0059-2
15. Wang H., Zhang Y.M., Yang Z. A Reliability Allocation Method of CNC Lathes Based on Copula Failure Correlation Model. Chinese Journal of Mechanical Engineering. 2018. Vol. 31. DOI: 10.1186/s10033-018-0303-9
16. Puchkov V.P., Yakunin V.V. Reliability examination of cnc turning chuck-type machine ТПК-125 under actual operating conditions. Privolzhskiy nauchnyy vestnik = The scientific bulletin of the Volga Region. 2013. № 12-2 (28). pp. 51–55. (In Russ.).
DOI: 10.24000/0409-2961-2024-12-27-32
Year: 2024
Issue num: December
Keywords : process equipment probability residual life model enterprise guaranteed estimate distribution function
Authors:
  • Lomakin M.I.
    Dr. Sci. (Eng.), Dr. Sci. (Econ.), Prof., Research Associate, lomakin@vniigoshs.ru, All-Russian Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center), Moscow, Russian Federation
  • Dokukin A.V.
    Dr. Sci. (Econ.), Research Associate, All-Russian Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center), Moscow, Russian Federation
  • Oltyan I.Yu.
    Cand. Sci. (Eng.), Scientific Secretary, All-Russian Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center), Moscow, Russian Federation
  • Niyazova Yu.M.
    Cand. Sci. (Econ.), Assoc. Prof., Moscow State University of Geodesy and Cartography, Moscow, Russian Federation