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Advances in Intelligent Reliability and Maintainability of Energy Infrastructure Assets

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Intelligent Reliability and Maintainability of Energy Infrastructure Assets

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 473))

Abstract

This chapter reviews the state-of-the-art research on the reliability and maintainability of energy infrastructure assets and emphasizes the Intelligent tools and methods proposed from a bibliometric and literature review point of view. The purpose of this chapter is to show the practical need for the ideas and methodologies presented in this book. A brief introduction to the reliability and maintainability of energy infrastructure assets is finalized. Subsequently, details of advances in reliability and the maintainability of energy infrastructure assets are summarized. The potential opportunities of the related research and engineering applications are also provided in this chapter.

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References

  1. Omer, A.M.: Energy, environment and sustainable development. Renew. Sustain. Energy Rev. 12(9), 2265–2300 (2008)

    Article  Google Scholar 

  2. Jiang, G.J., Huang, C.G., Nedjati, A., et al. Discovering the sustainable challenges of biomass energy: a case study of Tehran metropolitan. Environ. Dev. Sustain. 8 (2023)

    Google Scholar 

  3. Nedjati, A., Yazdi, M., Abbassi, R.: A sustainable perspective of optimal site selection of giant air-purifiers in large metropolitan areas. Environ. Dev. Sustain. 24, 8747–8778 (2022)

    Article  Google Scholar 

  4. Cho, E.: Making reliability reliable: a systematic approach to reliability coefficients. Organ. Res. Methods 19(4), 651–682 (2016)

    Article  Google Scholar 

  5. Li, H., Soares, C.G.: Assessment of failure rates and reliability of floating offshore wind turbines. Reliab. Eng. Syst. Saf. 228, 108777 (2022)

    Article  Google Scholar 

  6. Li, H., Peng, W., Huang, C.G., Guedes Soares, C.: Failure rate assessment for onshore and floating offshore wind turbines. J. Marine Sci. Eng. 10(12), 1965 (2022)

    Article  Google Scholar 

  7. Smith, D.J.: Reliability, maintainability and risk: practical methods for engineers. Butterworth-Heinemann (2021)

    Google Scholar 

  8. Gardoni, P. (ed.). (2017). Risk and Reliability Analysis: Theory and Applications, p. 556. Springer Nature.

    Google Scholar 

  9. Leung, L.: Validity, reliability, and generalizability in qualitative research. J. Family Med. Prim. Care 4(3), 324 (2015)

    Article  MathSciNet  Google Scholar 

  10. Coit, D.W., Zio, E.: The evolution of system reliability optimization. Reliab. Eng. Syst. Saf. 192, 106259 (2019)

    Article  Google Scholar 

  11. Li, H., Teixeira, A.P., Guedes Soares, C.: An improved failure mode and effect analysis of floating offshore wind turbines. J. Marine Sci. Eng. 10(11), 1616 (2022)

    Article  Google Scholar 

  12. Li, H., Yazdi, M., Huang, C.G., Peng, W.: A reliable probabilistic risk-based decision-making method: Bayesian technique for order of preference by similarity to ideal solution (B-TOPSIS). Soft. Comput. 26(22), 12137–12153 (2022)

    Article  Google Scholar 

  13. Li, H., Yazdi, M., Huang, H.-Z., Huang, C.-G., Peng, W., Nedjati, A., Adesina, K.A.: A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex Intell. Syst. (2023). https://doi.org/10.1007/s40747-023-01002-w.

  14. Peyghami, S., Palensky, P., Blaabjerg, F.: An overview on the reliability of modern power electronic based power systems. IEEE Open J. Power Electron. 1, 34–50 (2020)

    Article  Google Scholar 

  15. Li, H., Yazdi, M.: An advanced TOPSIS-PFS method to improve human system reliability. In: Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems, pp. 109–125. Springer, Cham (2022)

    Google Scholar 

  16. Li, H., DĂ­az, H., Soares, C.G.: A failure analysis of floating offshore wind turbines using AHP-FMEA methodology. Ocean Eng. 234, 109261 (2021)

    Article  Google Scholar 

  17. Li, H., Diaz, H., Soares, C.G.: A developed failure mode and effect analysis for floating offshore wind turbine support structures. Renew Energy 164, 133–145 (2021)

    Article  Google Scholar 

  18. Velmurugan, R.S., Dhingra, T.: Maintenance strategy selection and its impact in maintenance function: a conceptual framework. Int. J. Oper. Prod. Manage. (2015)

    Google Scholar 

  19. Ren, Z., Verma, A.S., Li, Y., Teuwen, J.J., Jiang, Z.: Offshore wind turbine operations and maintenance: a state-of-the-art review. Renew. Sustain. Energy Rev. 144, 110886 (2021)

    Article  Google Scholar 

  20. Li, H., Soares, C.G., Huang, H.Z.: Reliability analysis of a floating offshore wind turbine using Bayesian Networks. Ocean Eng. 217, 107827 (2020)

    Article  Google Scholar 

  21. Ben-Daya, M., Kumar, U., Murthy, D.P.: Introduction to maintenance engineering: modelling, optimization and management. Wiley (2016)

    Book  Google Scholar 

  22. Li, H., Huang, C.G., Soares, C.G.: A real-time inspection and opportunistic maintenance strategies for floating offshore wind turbines. Ocean Eng. 256, 111433 (2022)

    Article  Google Scholar 

  23. Bokrantz, J., Skoogh, A., Berlin, C., Wuest, T., Stahre, J.: Smart Maintenance: a research agenda for industrial maintenance management. Int. J. Prod. Econ. 224, 107547 (2020)

    Article  Google Scholar 

  24. Zhang, Y., Andrews, J., Reed, S., Karlberg, M.: Maintenance processes modelling and optimisation. Reliab. Eng. Syst. Saf. 168, 150–160 (2017)

    Article  Google Scholar 

  25. Podlesnik, C.A., Kelley, M.E., Jimenez-Gomez, C., Bouton, M.E.: Renewed behavior produced by context change and its implications for treatment maintenance: a review. J. Appl. Behav. Anal. 50(3), 675–697 (2017)

    Article  Google Scholar 

  26. Varkevisser, R.D.M., Van Stralen, M.M., Kroeze, W., Ket, J.C.F., Steenhuis, I.H.M.: Determinants of weight loss maintenance: a systematic review. Obes. Rev. 20(2), 171–211 (2019)

    Article  Google Scholar 

  27. Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015)

    Google Scholar 

  28. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)

    Article  Google Scholar 

  29. Cavalcante, C.A., Lopes, R.S.: Multi-criteria model to support the definition of opportunistic maintenance policy: a study in a cogeneration system. Energy 80, 32–40 (2015)

    Article  Google Scholar 

  30. Erguido, A., Márquez, A.C., Castellano, E., Fernández, J.G.: A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renew. Energy 114, 843–856 (2017)

    Article  Google Scholar 

  31. Li, H., Yazdi, M.: Develo** failure modes and effect analysis on offshore wind turbines using two-stage optimization probabilistic linguistic preference relations. In: Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems. Studies in Systems, Decision and Control, vol. 211. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07430-1_4

  32. Li, H., Soares, C.G.: Reliability analysis of floating offshore wind turbines support structure using hierarchical Bayesian network. In: Proceedings of the 29th European Safety and Reliability Conference, pp. 2489–2495. Research Publishing Services Singapore (2019)

    Google Scholar 

  33. Li, H., Yazdi, M.: Reliability analysis of correlated failure modes by transforming fault tree model to Bayesian network: a case study of the MDS of a CNC machine tool. In: Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems. Studies in Systems, Decision and Control, vol. 211. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07430-1_2

  34. Balog, R.S., Weaver, W.W., Krein, P.T.: The load as an energy asset in a distributed DC smartgrid architecture. IEEE Trans. Smart Grid 3(1), 253–260 (2011)

    Article  Google Scholar 

  35. Muñoz-Delgado, G., Contreras, J., Arroyo, J.M.: Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability. IEEE Trans. Power Syst. 31(5), 3715–3728 (2015)

    Article  Google Scholar 

  36. Baek, J., Vu, Q.H., Liu, J.K., Huang, X., **ang, Y.: A secure cloud computing based framework for big data information management of smart grid. IEEE Trans. Cloud Comput. 3(2), 233–244 (2014)

    Article  Google Scholar 

  37. Kwon, D., Hodkiewicz, M.R., Fan, J., Shibutani, T., Pecht, M.G.: IoT-based prognostics and systems health management for industrial applications. IEEE Access 4, 3659–3670 (2016)

    Article  Google Scholar 

  38. Moghaddass, R., Zuo, M.J.: An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliab. Eng. Syst. Saf. 124, 92–104 (2014)

    Article  Google Scholar 

  39. Yazdi, M., Mohammadpour, J., Li, H., Huang, H.-Z., Zarei, E., Pirbalouti, R.G., Adumene, S.: Fault tree analysis improvements: a bibliometric analysis and literature review. Qual. Reliab. Eng. Int. (2023). https://doi.org/10.1002/qre.3271

  40. Yazdi, M.: A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis. Int. J. Syst. Assur. Eng. Manag. 10, 1–18 (2019). https://doi.org/10.1007/s13198-018-00757-7

    Article  Google Scholar 

  41. Shafiee, M., Sørensen, J.D.: Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies. Reliab. Eng. Syst. Saf. 192, 105993 (2019)

    Article  Google Scholar 

  42. Schneider, J., Gaul, A.J., Neumann, C., Hogräfer, J., Wellßow, W., Schwan, M., Schnettler, A.: Asset management techniques. Int. J. Electr. Power Energy Syst. 28(9), 643–654 (2006)

    Article  Google Scholar 

  43. Ge, H., Asgarpoor, S.: Reliability and maintainability improvement of substations with aging infrastructure. IEEE Trans. Power Delivery 27(4), 1868–1876 (2012)

    Article  Google Scholar 

  44. Errandonea, I., Beltrán, S., Arrizabalaga, S.: Digital Twin for maintenance: a literature review. Comput. Ind. 123, 103316 (2020)

    Article  Google Scholar 

  45. Daneshkhah, A., Stocks, N.G., Jeffrey, P.: Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets. Reliab. Eng. Syst. Saf. 163, 33–45 (2017)

    Article  Google Scholar 

  46. Olesen, J.F., Shaker, H.R.: Predictive maintenance within combined heat and power plants based on a novel virtual sample generation method. Energy Convers. Manage. 227, 113621 (2021)

    Article  Google Scholar 

  47. Wong, S.Y., Ye, X., Guo, F., Goh, H.H.: Computational intelligence for preventive maintenance of power transformers. Appl. Soft Comput. 114, 108129 (2022)

    Article  Google Scholar 

  48. Rausand, M., Vatn, J.: Reliability centred maintenance. In: Complex System Maintenance Handbook, pp. 79–108. Springer, London (2008)

    Google Scholar 

  49. Wang, Y., Deng, C., Wu, J., Wang, Y., **ong, Y.: A corrective maintenance scheme for engineering equipment. Eng. Fail. Anal. 36, 269–283 (2014)

    Article  Google Scholar 

  50. Li, M., Wang, M., Kang, J., Sun, L., **, P.: An opportunistic maintenance strategy for offshore wind turbine system considering optimal maintenance intervals of subsystems. Ocean Eng. 216, 108067 (2020)

    Article  Google Scholar 

  51. Zhang, C., Gao, W., Guo, S., Li, Y., Yang, T.: Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance. Renew. Energy 103, 606–612 (2017)

    Article  Google Scholar 

  52. Scheu, M.N., Tremps, L., Smolka, U., Kolios, A., Brennan, F.: A systematic failure mode effects and criticality analysis for offshore wind turbine systems towards integrated condition based maintenance strategies. Ocean Eng. 176, 118–133 (2019)

    Article  Google Scholar 

  53. Besnard, F., Bertling, L.: An approach for condition-based maintenance optimization applied to wind turbine blades. IEEE Trans. Sustain. Energy 1(2), 77–83 (2010)

    Article  Google Scholar 

  54. Efthymiou, K., Papakostas, N., Mourtzis, D., Chryssolouris, G.: On a predictive maintenance platform for production systems. Procedia CIRP 3, 221–226 (2012)

    Article  Google Scholar 

  55. Yazdi, M., Khan, F., Abbassi, R., Rusli, R.: Improved DEMATEL methodology for effective safety management decision-making. Saf. Sci. 127, 104705 (2020). https://doi.org/10.1016/j.ssci.2020.104705

    Article  Google Scholar 

  56. Li, X., Han, Z., Yazdi, M., Chen, G.: A CRITIC-VIKOR based robust approach to support risk management of subsea pipelines, Appl. Ocean Res. 124 (2022) 103187. https://doi.org/10.1016/j.apor.2022.103187

  57. Yazdi, M.: Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93352-4%0A%0A.

  58. Li, H., Guo, J.-Y., Yazdi, M., Nedjati, A., Adesina, K.A.: Supportive emergency decision-making model towards sustainable development with fuzzy expert system. Neural Comput. Appl. 33, 15619–15637 (2021). https://doi.org/10.1007/s00521-021-06183-4

    Article  Google Scholar 

  59. Li, H., Yazdi, M.: A holistic question: is it correct that decision-makers neglect the probability in the risk assessment method? In: Li, H., Yazdi, M. (eds.) BT—Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems: Approaches, Case Studies, Multi-Criteria Decision-Making, pp. 185–189. Springer International Publishing, Cham (2022)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto SuperiorTĂ©cnico, Universidade de Lisboa, Lisbon, Portugal

    He Li

  2. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China

    Weiwen Peng

  3. School of Ocean Technology, Fisheries and Marine Institute, Memorial University of Newfoundland, St. John’s, Canada

    Sidum Adumene

  4. Faculty of Science and Engineering, Macquarie University, Sydney, Australia

    Mohammad Yazdi

  5. School of Computing, Engineering and Physical Sciences, University of the West of Scotland, London, UK

    Mohammad Yazdi

Authors
  1. He Li
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  2. Weiwen Peng
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  3. Sidum Adumene
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  4. Mohammad Yazdi
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Correspondence to Mohammad Yazdi .

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Li, H., Peng, W., Adumene, S., Yazdi, M. (2023). Advances in Intelligent Reliability and Maintainability of Energy Infrastructure Assets. In: Intelligent Reliability and Maintainability of Energy Infrastructure Assets. Studies in Systems, Decision and Control, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-031-29962-9_1

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