Models and information technology of aging management of man-made systems in the conditions of modern risks

Oleg Fedorovich, Liudmyla Lutai, Oleg Uruskiy, Sergii Gubka, Yuliia Leshchenko

Abstract


The urgent task of researching logistical measures and actions aimed at increasing the life of man-made systems is being formed and solved. The research being conducted is related to the management of complex, high-cost man-made systems (continuation of their resource) with long-term life (high-tech production, critical infrastructure, nuclear power, etc.).Therefore, the topic of the proposed publication, which examines the sequence of logistical actions in planning projects for managing the aging of man-made systems, is relevant. The purpose of this research is to create a complex of mathematical and agent models that can be used to analyze the problem of age degradation and plan actions to manage the aging of man-made systems. The problems that arose due to the aging of the man-made system are analyzed, such as: outdated technological equipment, the effects of climate change risks, regular disruption of energy supply, acts of terrorism, and military threats. Much attention is paid to critical man-made systems with high risks of impact on the environment and people, which are associated with their aging. A systematic analysis of the sequence of logistic actions related to the management of the aging of the man-made system was carried out. A number of possible strategies for aging management are emerging, such as: modernization and replacement of obsolete equipment; reducing the risks of impact on the environment of critical man-made systems, which requires the extension of their resources. The limited capabilities of enterprises are considered when determining the costs of preventive measures to extend the life of a man-made system. An analysis of influencing factors (external and internal) on the aging process of man-made systems is also conducted. Significant influencing factors are separated using virtual experiments and expert assessments. To identify outdated system components, a method is proposed based on assessing their condition with the help of experts and lexicographic ordering of a set of component variants. A cost optimization model is being created for the implementation of measures and actions to manage the of the man-made system. An agent model is formed on the Any Logic platform to analyze the sequence of logistic actions related to the management of man-made system. An example of extending the life of high-tech aircraft production is provided. The scientific novelty of the study is related to the solution of the urgent problem of the formation of strategies, models, and project planning methods for the management of the aging of man-made systems, which contributes to the extension of their existence and the reduction of the risks of impact on the environment and people. The results of this study should be used by the management of enterprises in planning projects related to the management of the aging of man-made systems.

Keywords


man-made system aging management; critical infrastructures of enterprises, risks of man-made system, damaged system components; factors influencing aging; cost optimization; modeling of logistic actions, simulation modeling, agent modeling, integer optimi

Full Text:

PDF

References


Fedorovich, O., Lutai, L., Kompanets, V., & Bahaiev, I. The Creation of an Optimisation Component-Oriented Model for the Formation of the Architecture of Science-Based Products. Integrated Computer Technologies in Mechanical Engineering - 2023. ICTM 2023. Lecture Notes in Networks and Systems, Springer, Cham, 2024, vol. 996, pp. 415-426. DOI: 10.1007/978-3-031-60549-9_31.

Rusinski, E., Czmochowski, J., Moczko, P., & Pietrusiak, D. Challenges and Strategies of Long-life Operation and Maintenance of Technical Objects. FME Transactions, 2016, vol. 44, no. 3, pp. 219-228. DOI: 10.5937/fmet1603219R.

Simi, A. Investigating new Techniques and Building Materials Compatible With the Environment in Construction. Encyclopedia of Sustainable Technologies, Second Edition, 2024, vol. 2, pp. 417-426. DOI: 10.1016/B978-0-323-90386-8.00140-6.

Wang, L., & Cheng, Z. Impact of the Belt and Road Initiative on enterprise green transformation. Journal of Cleaner Production, 2024, article no. 143043. DOI: 10.1016/j.jclepro.2024.143043.

Prokhorov, O., Fedorovich, O., Prokhorov, V., Shatalov, O., & Pakhomov, Y. Modeling of Distributed Mosaic Systems of Mobile Bionic Robots. Smart Technologies in Urban Engineering. STUE 2022. Lecture Notes in Networks and Systems, Springer, Cham, 2023, vol. 536, pp. 163-174. DOI: 10.1007/978-3-031-20141-7_16.

Shugailo, O. Operational Experience in Degradation (Ageing Effects) and Implementation of Ageing Management Programmes for Dry Storage Systems in Ukraine. Nuclear and Radiation Safety, 2023, no. 3, pp. 43-55. DOI: 10.32918/nrs.2023.3(99).04.

Li, Q., Zhou, Y., Wei, F., Li, S., Wang, Z., Li, J., Zhou, G., Liu, J, Yan, P., & Yu, D. Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems. Applied Energy, 2024, vol. 362, article no. 122980. DOI: 10.1016/j.apenergy.2024.122980.

Alsanousie, A. A., Elsamni, O. A., Attia, A. E., & Elhelw, M. Transient and troubleshoots management of aged small-scale steam power plants using Aspen Plus Dynamics. Energy, 2021, vol. 223, article no. 120079. DOI: 10.1016/j.energy.2021.120079.

Kahle, W. Imperfect repair in degradation processes: A Kijima-type approach. Applied Stochastic Models in Business and Industry, 2019, vol. 35, pp. 211-220. DOI: 10.1002/asmb.2438.

Ma, W., Zhao, Z., Yang, J., Li, Y., Yang, W., Zeng, W., Zheng Y., & Yang, J. A high-precision transient state prediction framework for ageing hydropower systems: Refined model, two-stage parameter identification and impact analysis. Journal of Cleaner Production, 2024, vol. 450, article no. 141748. DOI: 10.1016/j.jclepro.2024.141748.

Che, H., Zeng, S., Zhao, Y., & Guo, J. Reliability assessment of multi-state weighted k-out-of-n man-machine systems considering dependent machine deterioration and human fatigue. Reliability Engineering & System Safety, 2024, vol. 246, article no. 110048. DOI: 10.1016/j.ress.2024.110048.

Huang, Y., Zhang, P., Lu, J., Xiong, R., & Cai, Z. A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon. Applied Energy, 2024, vol. 360, article no. 122825. DOI: 10.1016/j.apenergy.2024.122825.

Alabi, T. M., Aghimien, E. I., Agbajor, F. D., Yang, Z., Lu, L., Adeoye, A. R., Gopaluni, B. A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. Renewable Energy, 2022, vol. 194, pp. 822-849. DOI: 10.1016/j.renene.2022.05.123.

Che, H., Zen, S., Li, K., & Guo, J. Reliability analysis of load-sharing man-machine systems subject to machine degradation, human errors, and random shocks. Reliability Engineering & System Safety, 2022, vol. 226, article no. 108679. DOI: 10.1016/j.ress.2022.108679.

Liu, K., Kang, L., Wan, L., Xie, D., Li, J. Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation. Journal of Energy Storage, 2023, vol. 74, article no. 109352. DOI: 10.1016/j.est.2023.109352.

Pronchakov, Y., Prokhorov, O., & Fedorovich, O. Concept of High-Tech Enterprise Development Management in the Context of Digital Transformation. Computation, 2022, vol. 10, iss. 7, article no. 118. DOI: 10.3390/computation10070118.

Chen, C., & Reniers, G. Risk Assessment of Processes and Products in Industrial Biotechnology. Sustainability and Life Cycle Assessment in Industrial Biotechnology. Advances in Biochemical Engineering/Biotechnology, Springer, Cham, 2018, vol. 173, pp. 255-279. DOI: 10.1007/10_2018_74.

He, X., Teng, R., Feng, D., & Gai, J. Industrial robots and pollution: Evidence from Chinese enterprises. Economic Analysis and Policy, 2024, vol. 82, pp. 629-650. DOI: 10.1016/j.eap.2024.03.001.

Etminan, J., Kamranfar, H., Chahkandi, M., & Fouladirad, M. Analysis of time-to-failure data for a repairable system subject to degradation. Journal of Computational and Applied Mathematics, 2022, vol. 408, article no. 114098. DOI: 10.1016/j.cam.2022.114098.

Krivanek, R., Fiedler, J. Main deficiencies and corrective measures of nuclear power plants in ageing management for safe long term operation. Nuclear Engineering and Design, 2017, vol. 323, pp. 78-83. DOI: 10.1016/j.nucengdes.2017.07.035.

Dinh, D.-H., Do, P., Bang, T. Q., & Nguyen-Ho, S.-H. Degradation modeling and opportunistic maintenance for two-component systems with an intermittent operation component. Computers & Industrial Engineering, 2023, vol. 185, article no. 109698. DOI: 10.1016/j.cie.2023.109698.

Giorgi, M. G., Donateo, T., Ficarella, A., Menga, N., Chiodo, L. S., & Strafella, L. Coupling principal component analysis-based sensor data reduction techniques and multi-net systems for simultaneous prediction of multi-component degradation levels in hybrid electric rotorcraft engines. Measurement, 2024, vol. 227, article no. 114212. DOI: 10.1016/j.measurement.2024.114212.

Milazzo, M. F., & Bragatto, P. A framework addressing a safe ageing management in complex industrial sites: The Italian experience in «Seveso» establishments. Journal of Loss Prevention in the Process Industries, 2019, vol. 58, pp. 70-81. DOI: 10.1016/j.jlp.2019.01.005.

Nulkes, L. J. Ageing processes in the chemical industry. Chemical Engineering Transactions, 2019, vol. 77, pp. 991-996. DOI: 10.3303/CET1977166.

Mukhopadhyay, K., Liu, B., Bedford, T., & Finkelstein, M. Remaining lifetime of degrading systems continuously monitored by degrading sensors. Reliability Engineering & System Safety, 2023, vol. 231, article no. 109022. DOI: 10.1016/j.ress.2022.109022.

Finkelstein, M., & Cha J.H. Reducing degradation and age of items in imperfect repair modeling. TEST, 2022, vol. 31, pp. 1058-1081. DOI: 10.1007/s11749-022-00813-2.

Nirbheram, J. S., Mahesh, A., & Bhimaraju, A. Techno-economic optimization of standalone photovoltaic-wind turbine-battery energy storage system hybrid energy system considering the degradation of the components. Renewable Energy, 2024, vol. 222, article no. 119918. DOI: 10.1016/j.renene.2023.119918.

Liu, K., Ashwin, T.R., Hu, X., Lucu, M., & Widanage, W. D. An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries. Renewable and Sustainable Energy Reviews, 2020, vol. 131, article no. 110017. DOI: 10.1016/j.rser.2020.110017.

Fordham, A., Milojevic, Z., Giles, E., Du, W., Owen, R. E., Michalik, S., Chater, P. A., Das, P. K., Attidekou, P. S., Lambert, S. M., Allan, P. K., Slater, P. R., Anderson, P. A., Jervis, R., Shearing, P. R., & Brett, D. J. L. Correlative non-destructive techniques to investigate aging and orientation effects in automotive Li-ion pouch cells. Joule, 2023, vol. 7, iss. 11, pp. 2622-2652. DOI: 10.1016/j.joule.2023.10.011.

Kanso, S., Jha M. S., & Theilliol, D. Degradation Tolerant Control Learning for Discrete-Time Affine Nonlinear Systems. IFAC-PapersOnLine, 2023, vol. 56, iss. 2, pp. 7734-7739. DOI: 10.1016/j.ifacol.2023.10.1178.

Milazzo, M. F., Bragatto, P., Ancione, G., & Scionti, G. Ageing Assessment and Management at Major-Hazard Industries. Chemical Engineering Transactions, 2018, vol. 67, pp. 73-78. DOI: 10.3303/CET1867013.

Lee, M., Han, D., Yoo, K., & Kim, J. Impedance technique combined with supervised algorithms-based internal degradation state classification and its economic analysis for safety in retired battery pack. Journal of Energy Storage, 2023, vol. 73, article no. 109037. DOI: 10.1016/j.est.2023.109037.




DOI: https://doi.org/10.32620/reks.2024.3.12

Refbacks

  • There are currently no refbacks.