Skip to main content

Advertisement

SpringerLink
Log in

Cloud-enhanced predictive maintenance

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bo S, Shengkui Z, Rui K, Pecht MG (2012) Benefits and challenges of system prognostics. IEEE Trans Reliab 61(2):323–335. doi:10.1109/TR.2012.2194173

    Article  Google Scholar 

  2. Alsyouf I (2007) The role of maintenance in improving companies’ productivity and profitability. Int J Prod Econ 105(1):70–78. doi:10.1016/j.ijpe.2004.06.057

    Article  Google Scholar 

  3. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Pr 20(7):1483–1510. doi:10.1016/j.ymssp.2005.09.012

    Article  Google Scholar 

  4. CEN (2001) Maintenance terminology. European Standard EN13306

  5. ISO (2014) Condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. International Standard ISO13381-1

  6. Lee J, Lapira E, Bagheri B, Kao H-a (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1(1):38–41. doi:10.1016/j.mfglet.2013.09.005

    Article  Google Scholar 

  7. Salonen A, Deleryd M (2011) Cost of poor maintenance: a concept for maintenance performance improvement. J Qual Maint Eng 17(1):63–73

    Article  Google Scholar 

  8. Galar D, Gustafson A, Tormos B, Berges L (2012) Maintenance decision making based on different types of data fusion. Podejmowanie decyzji eksploatacyjnych w oparciu o fuzję różnego typu danych 14(2):135–144

    Google Scholar 

  9. Foster I, Yong Z, Raicu I, Shiyong L (2008) Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, 2008. GCE ‘08, 12–16 Nov. 2008. pp 1–10. doi:10.1109/GCE.2008.4738445

  10. Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86. doi:10.1016/j.rcim.2011.07.002

    Article  Google Scholar 

  11. Lee J, Yang S, Lapira E, Kao H-A, Yen N (2013) Methodology and framework of a cloud-based prognostics and health management system for manufacturing industry. Chem Eng Transcr 33:205–210. doi:10.3303/CET1333035

    Article  Google Scholar 

  12. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the Internet of Things: a survey. IEEE Commun Surv Tutorials 16(1):414–454. doi:10.1109/SURV.2013.042313.00197

    Article  Google Scholar 

  13. Ashton K (2009) That ‘Internet of Things’ thing. In the real world, things matter more than ideas. RFID Journal. http://www.rfidjournal.com/articles/view?4986. Accessed 4 November 2015

  14. Zhang L, Luo Y, Tao F, Li BH, Ren L, Zhang X, Guo H, Cheng Y, Hu A, Liu Y (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8(2):167–187. doi:10.1080/17517575.2012.683812

    Article  Google Scholar 

  15. Wang L, Wang XV, Gao L, Váncza J (2014) A cloud-based approach for WEEE remanufacturing. CIRP Ann Manuf Technol 63(1):409–412. doi:10.1016/j.cirp.2014.03.114

    Article  Google Scholar 

  16. Ren L, Zhang L, Tao F, Zhao C, Chai X, Zhao X (2013) Cloud manufacturing: from concept to practice. Enterprise Information Systems:1–24. doi:10.1080/17517575.2013.839055

    Article  Google Scholar 

  17. Galar D, Kumar U, Juuso E, Lahdelma S (2012) Fusion of maintenance and control data: a need for the process. Paper presented at the 18th World Conference on Nondestructive Testing, Durban, South Africa

  18. Bangemann T, Rebeuf X, Reboul D, Schulze A, Szymanski J, Thomesse JP, Thron M, Zerhouni N (2006) PROTEUS—creating distributed maintenance systems through an integration platform. Comput Ind 57(6):539–551. doi:10.1016/j.compind.2006.02.018

    Article  Google Scholar 

  19. Voisin A, Medina-Oliva G, Monnin M, Léger J-B, Iung B (2013) Fleet-wide diagnostic and prognostic assessment. In: Sankararaman S (ed) Proceedings of the Annual Conference of the Prognostics and Health Management Society. pp 521–530

  20. Medina-Oliva G, Voisin A, Monnin M, Peysson F, Leger J-B (2012) Prognostics assessment using fleet-wide ontology. Paper presented at the PHM Conference, Minneapolis, Minnesota, USA

  21. Tianyi W, Jianbo Y, Siegel D, Lee J (2008) A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: Prognostics and Health Management, 2008. PHM 2008. International Conference on, 6–9 Oct. 2008. pp 1–6. doi:10.1109/PHM.2008.4711421

  22. Bahga A, Madisetti VK (2012) Analyzing massive machine maintenance data in a computing cloud. IEEE Trans Parallel Distrib Syst 23(10):1831–1843

    Article  Google Scholar 

  23. Foo PH, Ng GW (2013) High-level information fusion: an overview. J Adv Inf Fusion 8(1):33–72

    Google Scholar 

  24. Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fusion 14(1):28–44. doi:10.1016/j.inffus.2011.08.001

    Article  Google Scholar 

  25. De Vin LJ, Ng AHC, Oscarsson J, Andler SF (2006) Information fusion for simulation based decision support in manufacturing. Robot Comput Integr Manuf 22(5–6):429–436. doi:10.1016/j.rcim.2005.11.007

    Article  Google Scholar 

  26. Si X-S, Wang W, Hu C-H, Zhou D-H (2011) Remaining useful life estimation—a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14. doi:10.1016/j.ejor.2010.11.018

    Article  MathSciNet  Google Scholar 

  27. Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M (2015) Cloud-enabled prognosis for manufacturing. CIRP Ann Manuf Technol 64(2):749–772. doi:10.1016/j.cirp.2015.05.011

    Article  Google Scholar 

  28. Sankararaman S, Daigle MJ, Goebel K (2014) Uncertainty quantification in remaining useful life prediction using first-order reliability methods. IEEE Trans Reliab 63(2):603–619. doi:10.1109/TR.2014.2313801

    Article  Google Scholar 

  29. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D (2014) Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Pr 42(1–2):314–334. doi:10.1016/j.ymssp.2013.06.004

    Article  Google Scholar 

  30. Wang XH, Da Qing Z, Tao G, Pung HK Ontology based context modeling and reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications, 14–17 March 2004. pp 18–22. doi:10.1109/PERCOMW.2004.1276898

Download references

Author information

Authors and Affiliations

  1. School of Engineering Science, University of Skövde, Skövde, 541 28, Sweden

    Bernard Schmidt & Lihui Wang

  2. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden

    Lihui Wang

Authors
  1. Bernard Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lihui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bernard Schmidt.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schmidt, B., Wang, L. Cloud-enhanced predictive maintenance. Int J Adv Manuf Technol 99, 5–13 (2018). https://doi.org/10.1007/s00170-016-8983-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-8983-8

Keywords

Search

Navigation