Advertisement

Cloud-enhanced predictive maintenance

  • Bernard SchmidtEmail author
  • Lihui Wang
ORIGINAL ARTICLE

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.

Keywords

Predictive maintenance Condition-based maintenance Context awareness Cloud manufacturing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 CrossRefGoogle Scholar
  2. 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 CrossRefGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 4.
    CEN (2001) Maintenance terminology. European Standard EN13306Google Scholar
  5. 5.
    ISO (2014) Condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. International Standard ISO13381-1Google Scholar
  6. 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 CrossRefGoogle Scholar
  7. 7.
    Salonen A, Deleryd M (2011) Cost of poor maintenance: a concept for maintenance performance improvement. J Qual Maint Eng 17(1):63–73CrossRefGoogle Scholar
  8. 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):135–144Google Scholar
  9. 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. 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 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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 CrossRefGoogle Scholar
  13. 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. 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 CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 AfricaGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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 521–530Google Scholar
  20. 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, USAGoogle Scholar
  21. 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. 22.
    Bahga A, Madisetti VK (2012) Analyzing massive machine maintenance data in a computing cloud. IEEE Trans Parallel Distrib Syst 23(10):1831–1843CrossRefGoogle Scholar
  23. 23.
    Foo PH, Ng GW (2013) High-level information fusion: an overview. J Adv Inf Fusion 8(1):33–72Google Scholar
  24. 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 CrossRefGoogle Scholar
  25. 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 CrossRefGoogle Scholar
  26. 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 MathSciNetCrossRefGoogle Scholar
  27. 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 CrossRefGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 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 CrossRefGoogle Scholar
  30. 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

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.School of Engineering ScienceUniversity of SkövdeSkövdeSweden
  2. 2.Department of Production EngineeringKTH Royal Institute of TechnologyStockholmSweden

Personalised recommendations