Skip to main content

Industry 4.0 Technologies for the Sustainable Management of Maintenance Resources

  • Conference paper
  • First Online:
Innovations in Industrial Engineering II (icieng 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

Application of modern maintenance approaches such as Maintenance 4.0 is highlighted as one of the prevailing smart & sustainable manufacturing topics. The goal of this paper is to describe the latest trends within the area of maintenance management from the perspective of the challenges of the fourth industrial revolution and the economic, environmental and social challenges of sustainable development. The four stages of the machine maintenance approach related to the four industrial revolutions are characterized together with benefits and weaknesses of each approach to machine maintenance. Knowledge-Based Maintenance evolution has been shown. Comparison of maintenance data in different maintenance ages has been characterized. Less than three decades ago the concept of Green Maintenance (GMn) was proposed as a support for the implementation of the Green Manufacturing paradigm. Digitalization can empower maintenance services by making use of collected data and advance technologies to monitor the equipment health, diagnose faults, predict and troubleshoot failures well before it could happen and even optimize the performance. Intelligent and sustainable maintenance has been considered in three perspectives.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Koochaki, J., Bokhorst, J., Wortmann, H., Klingenberg, W.: Evaluating condition based maintenance effectiveness for two processes in series. J. Qual. Maint. Eng. 17(4), 398–414 (2011)

    Article  Google Scholar 

  2. Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the Industry 4.0: a systematic literature review. Comput. Ind. Eng. 150, 106889 (2020)

    Google Scholar 

  3. Iung, B., Levrat, E., Marquez, A.C., Erbe, H.: Conceptual framework for e-Maintenance: illustration by e-Maintenance technologies and platforms. Annu. Rev. Control. 33(2), 220–229 (2009)

    Article  Google Scholar 

  4. Johansson, N., Roth, E., Reim, W.: Smart and sustainable emaintenance: capabilities for digitalization of maintenance. Sustainability 11(13) (2019)

    Google Scholar 

  5. Navas, M. A., Sancho, C., Carpio, J.: Disruptive maintenance engineering 4.0. International Journal of Quality & Reliability Management 37 (6/7), 853–871 (2020)

    Google Scholar 

  6. Turner, C.J., Emmanouilidis, C., Tomiyama, T., Tiwari, A., Roy, R.: Intelligent decision support for maintenance: an overview and future trends. Int. J. Comput. Integr. Manuf. 32(10), 936–959 (2019)

    Article  Google Scholar 

  7. Kinz, A., Bernerstaetter, R., Biedermann, H.: Lean smart maintenance – efficient and effective asset management for smart factories. In: MOTSP 2016 - 8th International Scientific Conference (2016)

    Google Scholar 

  8. Kumar, U., Galar, D.: Maintenance in the era of industry 4.0, issues and challenges. In: Quality, IT and Business Operations: Modeling and Optimization, pp. 231–250 (2018)

    Google Scholar 

  9. Bokrantz, J., Skoogh, A., Berlin, C., Wuest, T., Stahre, J.: Smart maintenance: an empirically grounded conceptualization. Int. J. Prod. Econ. 223, 107534 (2020)

    Article  Google Scholar 

  10. Compare, M., Baraldi, P., Zio, E.: Challenges to IoT-enabled predictive maintenance for industry 4.0. IEEE Internet Things J. 7(5), 4585–4597 (2019)

    Google Scholar 

  11. Yoon, J.T., Youn, B.D., Yoo, M., Kim, Y., Kim, S.: Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis. Reliab. Eng. Syst. Safety 184, 181–192 (2019)

    Article  Google Scholar 

  12. Antosz, K., Jasiulewicz-Kaczmarek, M., Paśko, Ł, Zhang, C., Wang, S.: Application of machine learning and rough set theory in lean maintenance decision support system development. Eksploatacja i Niezawodnosc-Mainten. Reliab. 23(4), 695–708 (2021)

    Article  Google Scholar 

  13. Matyas, K., Nemeth, T., Kovacs, K., Glawar, R.: A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Ann. Manuf. Technol. 66, 461–464 (2017)

    Article  Google Scholar 

  14. Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., Cesarotti, V.: Maintenance transformation through Industry 4.0 technologies: a systematic literature review. Comput. Indust. 123, 103335 (2020)

    Google Scholar 

  15. Grijalvo Martín, M., Pacios Álvarez, A., Ordieres-Meré, J., Villalba-Díez, J., Morales-Alonso, G.: New business models from prescriptive maintenance strategies aligned with sustainable development goals. Sustainability 13(1), 216 (2021)

    Article  Google Scholar 

  16. Bokrantz, J., Skoogh, A., Berlin, C., Stahre, J.: Maintenance in digitalised manufacturing: delphi-based scenarios for 2030. Int. J. Prod. Econ. 191, 154–169 (2017)

    Article  Google Scholar 

  17. 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 

  18. Jasiulewicz-Kaczmarek, M., Legutko, S., Kluk, P.: Maintenance 4.0 Technologies—new opportunities for sustainability driven maintenance. Manag. Prod. Eng Rev. 11(2), 74–87 (2020)

    Google Scholar 

  19. Cao, Q., et al.: KSPMI: a knowledge-based system for predictive maintenance in Industry 4.0. Robot. Comput. Integr. Manufac. 74, 102281 (2022)

    Google Scholar 

  20. Gopalakrishnan, M., Subramaniyan, M., Skoogh, A.: Data-driven machine criticality assessment–maintenance decision support for increased productivity. Product. Plan. Control 33(1), 1–19 (2022)

    Article  Google Scholar 

  21. Pawellek, G.: Integrierte Instandhaltung und Ersatzteillogistik: Vorgehensweisen, Methoden, Tools. Springer, Berlin (2013)

    Google Scholar 

  22. Chen, C., Wang, C., Lu, N., Jiang, B., Xing, Y.: A data-driven predictive maintenance strategy based on accurate failure prognostics. Eksploatacja i Niezawodnosc – Mainten. Reliab. 23(2), 387–394 (2021). https://doi.org/10.17531/ein.2021.2.19

  23. Karim, R., Westerberg, J., Galar, D., Kumar, U.: Maintenance analytics–the new know in maintenance. IFAC-PapersOnLine 49(28), 214–219 (2016)

    Article  Google Scholar 

  24. Ansari, F., Glawar, R.: Tanja, Nemeth: PriMa: a prescriptive maintenance model for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 32(4–5), 482–503 (2019)

    Article  Google Scholar 

  25. Lee, E.A.: What is real time computing? a personal view. IEEE Des. Test 35, 64–72 (2017)

    Google Scholar 

  26. Siddiqa, A., Hashem, I.A.T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., et al.: A survey of big data management: taxonomy and state-of-the-art. J Network Comput Appl 71, 151–166 (2016)

    Article  Google Scholar 

  27. Smith, R., Hawkins, B.: Lean Maintenance: Reduce Costs, Improve Quality, and Increase Market Share. Elsevier Butterworth-Heinemann, MA (2004)

    Google Scholar 

  28. Antosz, K., Ratnayake, R.C.: Machinery classification and prioritization: empirical models and AHP based approach for effective preventive maintenance. In: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1380–1386. IEEE (2016)

    Google Scholar 

  29. Jasiulewicz-Kaczmarek, M., Saniuk, A.: How to make maintenance processes more efficient using lean tools. In: Goossens, Richard H.M. (ed.) Advances in Social & Occupational Ergonomics. Proceedings of the AHFE 2017 International Conference on Social & Occupational Ergonomics 17–21 July 2017, Advances in Intelligent Systems and Computing, vol. 605, pp. 9–20 (2018)

    Google Scholar 

  30. Ribeiro, D.R.S., Forcellini, F.A., Pereira, M., Xavier, F.A.: An overview about the implementing of lean maintenance in manufacturing processes. J. Lean Syst. 4(3), 44–59 (2019)

    Google Scholar 

  31. Marttonen-Arola, S., Baglee, D.: Assessing the information waste in maintenance management processes. J. Qual. Maint. Eng. 26(3), 383–398 (2019)

    Article  Google Scholar 

  32. Skoogh, A., Johansson, B., Hansson, L.: Data requirements and representation for simulation of energy consumption in production systems. In: Proceedings of the 44th CIRP Conference on Manufacturing Systems, pp. 1–3 (2011)

    Google Scholar 

  33. Lewis, A., Elmualim, A., Riley, D.: Linking energy and maintenance management for sustainability through three American case studies. Facilities 29(5/6), 243–254 (2011)

    Article  Google Scholar 

  34. Costantino, F., Di Gravio, G., Tronci, M.: Integrating environmental assessment of failure modes in maintenance planning of production systems. Appl. Mech. Mater. 295–298, 651–660 (2013)

    Article  Google Scholar 

  35. Ajukumar, V.N., Gandhi, O.P.: Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach. J. Clean. Prod. 51, 34–46 (2013)

    Article  Google Scholar 

  36. Fraser, K., Hvolby, H.H.: Maintenance management models: a study of the published literature to identify empirical evidence: a greater practical focus is needed. Int. J. Qual. Reliab. Manage. 32(6), 635–664 (2015)

    Article  Google Scholar 

  37. Franciosi, C., Di Pasquale, V., Iannone, R., Miranda, S.: Multi-stakeholder perspectives on indicators for sustainable maintenance performance in production contexts: an exploratory study. J. Qual. Maint. Eng. 27(2), 308–330 (2020)

    Article  Google Scholar 

  38. Jasiulewicz-Kaczmarek, M., et al.: Application of MICMAC, Fuzzy AHP and fuzzy TOPSIS for evaluation of the maintenance factors affecting sustainable manufacturing. Energies 14(5), 1436 (2021)

    Article  Google Scholar 

  39. Zhang, C., Zhang, Y., Dui, H., Wang, S., Tomovic, M.M.: Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodnosc – Mainten. Reliab. 24(1), 15–24 (2022). https://doi.org/10.17531/ein.2022.1.3

  40. Macchi, M., Farruku, K., Holgado, M., Negri, E., Panarese, D.: Economic and environmental impact assessment through system dynamics of technology-enhanced maintenance services. Int. J. Ind. Syst. Eng. 23(1), 36–56 (2016)

    Google Scholar 

  41. Al-Turki, U.M., Ayar, T., Yilbas, B.S., Sahin, A.Z.: Health, safety and sustainability in maintenance. In: Integrated Maintenance Planning in Manufacturing Systems. SAST, pp. 59–69. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06290-7_4

  42. Xia, T., Xi, L., Du, S., Xiao, L., Pan, E.: Energy-oriented maintenance decision-making for sustainable manufacturing based on energy saving window. J. Manuf. Sci. Eng. 140(5), 051001 (2018)

    Article  Google Scholar 

  43. Vrignat, P., Kratz, F., Avila, M.: Sustainable manufacturing, maintenance policies, prognostics and health management: a literature review. Reliab. Eng. Syst. Saf. 218, 108140 (2022)

    Article  Google Scholar 

  44. Jasiulewicz-Kaczmarek, M., Drożyner, P.: Social dimension of sustainable development – safety and ergonomics in maintenance activities. In: Stephanidis, C., Antona, M. (eds.) UAHCI 2013. LNCS, vol. 8009, pp. 175–184. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39188-0_19

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stanislaw Legutko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Legutko, S. (2023). Industry 4.0 Technologies for the Sustainable Management of Maintenance Resources. In: Machado, J., et al. Innovations in Industrial Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-09360-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09360-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09359-3

  • Online ISBN: 978-3-031-09360-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics