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Abstrakty
Readiness and reliability is a special attribute of rescue systems (army, police, fire service), where performance at the highest level is more important than economic efficiency. For this reason, special attention is given to the process of renewal of technical objects. In such systems, a preventive strategy is most often used. Though this is a safe model, it does not always take into account the specifics of the use of a technical object. Moreover, in some situations, it forces the end of life of a device that could still continue to operate as intended. The article analyzes precisely such technical objects, removed from operation after just 10 years of use. It was shown that such approach is not justified and that modern management strategies must be implemented also in relation to machinery and equipment operating in rescue systems. The most important achievements of the article are the use of reliability analysis methods in the systems where it is not common, and the indication of the benefits of such analysis. It has been shown that knowing the characteristics of reliability, you can consciously control each process and make decisions in this regard based on the technical condition of the facility and not on instructions. In the case under study, this would make it possible to undermine the decision to withdraw the analyzed objects from operation.
Rocznik
Tom
Strony
art. no. e146619
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
- Military University of Technology, Warsaw, Poland
Bibliografia
- [1] J. Lewandowski, S. Młynarski, R. Pilch, M. Smolnik, J. Szybka, and G. Wi ˛azania, “An evaluation method of preventive renewal strategies of railway vehicles selected parts,” Eksploat. Niezawodn., vol. 23, no. 4, pp. 678–684, 2021, doi: 10.17531/ein.2021.4.10.
- [2] A. Borucka, “Three-state Markov model of using transport means” in Proc. of the 18th International Scientific Conference Business Logistics in Modern Management, 2018, pp. 3–19.
- [3] X. Yang, H. Yihai, Z. Di, and Z. Xin, “Mission reliability-centered maintenance approach based on quality stochastic flow network for multistate manufacturing systems,” Eksploat. Niezawodn., vol. 24, no. 3, pp. 455–467, 2022, doi: 10.17531/ein.2022.3.7.
- [4] A. Sánchez-Herguedas, Á. Mena-Nieto, and F. Rodrigo-Muñoz, “A method for obtaining the preventive maintenance interval in the absence of failure time data,” Eksploat. Niezawodn., vol. 24, no. 3, pp. 564–573, 2022, doi: 10.17531/ein.2022.3.17.
- [5] V. Mykhailov et al., “Aviation search and rescue personnel training by the means of the information educational environment of the establishment of postgraduate education,” J. Int. Scope, vol. 11, no. 21, pp. 181–185, 2021.
- [6] M. Oszczypała, J. Ziółkowski, and J. Małachowski, “Semi-Markov approach for reliability modelling of light utility vehicles,” Eksploat. Niezawodn., vol. 25, no. 2, p. 161859, 2023, doi: 10.17531/ein/161859.
- [7] E. Kozłowski, A. Borucka, T. Szymczak, A. Świderski, and L. Gil, “Predicting the Fatigue Life of a Ball Joint,” Transp. Telecommun. J., vol. 22, no. 4, pp. 453–460, 2021, doi: 10.2478/ttj-2021-0035.
- [8] X. Biao, “The Warship Equipment Life Cycle Design Based on the Supportability System Engineering for the People’s Armed Police Coast Guard,” IOP Conf. Ser.: Mat. Sci. Eng., vol. 1043, no. 2, p. 022058, 2021, doi: 10.1088/1757-899X/1043/2/022058.
- [9] L. Li, Y. Wang and K-Y. Lin, “Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization,” J. Intell. Manuf., vol. 32, no. 2, pp. 545–558, 2021, doi: 10.1007/s10845-020-01588-9.
- [10] G. Figueredo, K. Owa, and R. John, “Multi-objective optimization for time-based preventive maintenance within the transport network: a review,” Academic and Library Computing, Tech. Rep., 2020, doi: 10.13140/RG.2.2.36132.01929.
- [11] A. Jaro´n, A. Borucka, and R. Parczewski, “Analysis of the Impact of the COVID-19 Pandemic on the Value of CO2 Emissions from Electricity Generation,” Energies, vol. 15, p. 4514, 2022, doi: 10.3390/en15134514.
- [12] J. Huang, Q. Chang, and J. Arinez, “Deep reinforcement learning based preventive maintenance policy for serial production lines,” Expert Syst. Appl., vol. 160, p. 113701, 2020, doi: 10.1016/j.eswa.2020.113701.
- [13] I. Alawaysheh et al., “Selecting maintenance practices based on environmental criteria: a comparative analysis of theory and practice in the public transport sector in UAE/DUBAI,” Int. J. Syst. Assur. Eng. Manag., vol. 11, pp. 1133–1155, 2020, doi: 10.1007/s13198-020-00964-1.
- [14] R. Dohare, S. Kumar, and G. Singhal, “Data Acquisition System for Chemical Iodine Generation Suitable for Flowing Medium Chemical Oxygen Iodine Laser,” Def. Sci. J., vol. 71, no. 6, pp. 798–806, 2021, doi: 10.14429/dsj.71.17026.
- [15] N.L. Dehghani, Y. Mohammadi Darestani, and A. Shafieezadeh, “Optimal Life-Cycle Resilience Enhancement of Aging Power Distribution Systems: A MINLP-Based Preventive Maintenance Planning,” IEEE Access, vol. 8, pp. 22324–22334, 2020, doi: 10.1109/ACCESS.2020.2969997.
- [16] X. Wang et al., “Imperfect preventive maintenance policies with unpunctual execution,” IEEE Trans. Reliab., vol. 69, no. 4, pp. 1480–1492, 2020, doi: 10.1109/TR.2020.2983415.
- [17] M.N. Mustafa, “Classification of maintenance techniques and diagnosing failures methods,” J. Phys., vol. 2060, no. 1, p. 012014, 2021, doi: 10.1088/1742-6596/2060/1/012014.
- [18] T. Zonta et al., “Predictive maintenance in the Industry 4.0: A systematic literature review,” Comput. Ind. Eng., vol. 150, p. 106889, 2020, doi: 10.1016/j.cie.2020.106889.
- [19] Ł. Sobaszek, A. Gola, and E. Kozłowski, “Predictive scheduling with Markov chains and ARIMA models,” Appl. Sci., vol. 10, no. 17, p. 6121, 2020, doi: 10.3390/app10176121.
- [20] M. Jasiulewicz-Kaczmarek and K. Antosz, “The concept of sustainable maintenance criteria assessment,” in Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (APMS 2021), vol. 633, 2021, pp. 427–436, doi: 10.1007/978-3-030-85910-7_45.
- [21] S. Muthukumar, R.A. Srivardhan, and P. Subhash Chandra Bose, “System reliability estimation of divert attitude control system of a launch vehicle using Bayesian networks,” Def. Sci. J., vol. 70, no. 1, pp. 9094, 2020, doi: 10.14429/dsj.70.13708.
- [22] K. Ahmadi, “Reliability analysis for a class of exponential distribution based on progressive first-failure censoring,” Reliab. Theory Appl., vol.16, no. 4, pp. 137–149, 2021.
- [23] K-C, Chiou and K–S Chen, “Lifetime performance evaluation model based on quick response thinking,” Eksploat. Niezawodn., vol. 24, no. 1, pp. 1–6, 2022, doi: 10.17531/ein.2022.1.1.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-647a4077-880f-48d6-a1e0-d06b6e890e80