Advertisement

Challenges from Data-Driven Predictive Maintenance in Brownfield Industrial Settings

  • Georgios KoutroulisEmail author
  • Stefan ThalmannEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

In the last years many companies made substantial investments in digitization of production and started collecting a lot of data. However, the big question arises how to make sense of all these data and to create competitive advantage? In this regard maintenance is an ever-urged topic and seems to be a low hanging fruit to realize benefits from analyzing large amounts of sensor data now available. This is however, very challenging in typical industrial environments where we can find a mixture of old and new production infrastructure, called brownfield environment. In this work in progress paper we want to investigate this context and identify challenges for the introduction of Big Data approaches for predictive maintenance. For this purpose, we conducted a case study with a world reputed electronic components company. We found that making sense out of sensor data and finding the right level of detail for the analysis is very challenging. We developed a feedback app to incorporate the employees’ domain knowledge in the sense making process.

Keywords

Predictive maintenance Big data Brownfield environment Industrial analytics Manufacturing 

Notes

Acknowledgement

Pro2Future is funded within the Austrian COMET Program—Competence Centers for Excellent Technologies—under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria. COMET is managed by the Austrian Research Promotion Agency FFG.

References

  1. 1.
    Wee, D., Kelly, R., Cattel, J., Breunig, M.: Industry 4.0—How to Navigate Digitization of the Manufacturing Sector. McKinsey & Company, p. 58 (2015)Google Scholar
  2. 2.
    Reinsel, D., Gantz, J., Rydning, J.: Data Age 2025: The Evolution of Data to Life-Critical. Don’t Focus on Big Data; Focus on the Data That’s Big. IDC White Paper (2017). http://www.seagate.com/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf
  3. 3.
    Yan, J., Meng, Y., Lu, L., Li, L.: Industrial big data in an industry 4.0 environment: challenges, schemes, and applications for predictive maintenance. IEEE Access 5, 23484–23491 (2017)CrossRefGoogle Scholar
  4. 4.
    Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)CrossRefGoogle Scholar
  5. 5.
    Khan, M., Wu, X., Xu, X., Dou, W.: Big data challenges and opportunities in the hype of Industry 4.0. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE, May 2017Google Scholar
  6. 6.
    Yam, R.C.M., Tse, P.W., Li, L., Tu, P.: Intelligent predictive decision support system for condition-based maintenance. Int. J. Adv. Manuf. Technol. 17(5), 383–391 (2001)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M.: Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 47, 145–156 (2012)CrossRefGoogle Scholar
  8. 8.
    O’Donovan, P., Leahy, K., Bruton, K., O’Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2(1), 25 (2015)CrossRefGoogle Scholar
  9. 9.
    Alsyouf, I.: The role of maintenance in improving companies’ productivity and profitability. Int. J. Prod. Econ. 105(1), 70–78 (2007)CrossRefGoogle Scholar
  10. 10.
    Prajapati, A., Bechtel, J., Ganesan, S.: Condition based maintenance: a survey. J. Qual. Maint. Eng. 18(4), 384–400 (2012)CrossRefGoogle Scholar
  11. 11.
    Park, C., Moon, D., Do, N., Bae, S.M.: A predictive maintenance approach based on real-time internal parameter monitoring. Int. J. Adv. Manuf. Technol. 85(1–4), 623–632 (2016)CrossRefGoogle Scholar
  12. 12.
    Aljumaili, M., Wandt, K., Karim, R., Tretten, P.: eMaintenance ontologies for data quality support. J. Qual. Maint. Eng. 21(3), 358–374 (2015)CrossRefGoogle Scholar
  13. 13.
    Klein, H.K., Myers, M.D: A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Q. 23, 67–93 (1999)CrossRefGoogle Scholar
  14. 14.
    Vathoopan, M., Brandenbourger, B., Zoitl, A.: A human in the loop corrective maintenance methodology using cross domain engineering data of mechatronic systems. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4. IEEE, September 2016Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Pro2Future GmbHGrazAustria
  2. 2.Institute of Interactive Systems and Data ScienceTechnical University of GrazGrazAustria

Personalised recommendations