Skip to main content
SpringerLink
Log in

Design Principles for Shared Maintenance Analytics in Fleet Management

  • Conference paper
  • First Online:
The Next Wave of Sociotechnical Design (DESRIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12807))

  • 1611 Accesses

Abstract

Many of today’s production facilities are modular in design and therefore to some degree individual. Further, there is a variety of differing application contexts that impact the operation of machines as compared to the manufacturer’s test cases. However, knowledge for their maintenance at the place of operation is typically limited to the manufacturer’s (digitally) printed technical documentation delivered with the product. Even with local knowledge, this requires extensive time and fault data to understand and prevent machine failures. Thus, there is a need for shared maintenance analytics, a scalable, networked learning process that address these issues in fleet management. Despite partial success, which mainly stems from individual use cases, there is no generalizable architecture for a broader adoption or practical use as of yet. To address this issue, we derive design requirements, design principles, and design features to specify a system architecture for shared maintenance analytics in fleet management.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Muchiri, P., Pintelon, L., Gelders, L., Martin, H.: Development of maintenance function performance measurement framework and indicators. Int. J. Prod. Econ. 131, 295–302 (2011)

    Article  Google Scholar 

  2. Fabri, L., Häckel, B., Oberländer, A.M., Töppel, J., Zanker, P.: Economic perspective on algorithm selection for predictive maintenance. In: Proceedings of the 27th European Conference on Information System, pp. 1–16. AIS, Stockholm (2019)

    Google Scholar 

  3. Al-Dahidi, S., Di Maio, F., Baraldi, P., Zio, E., Seraoui, R.: A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis. Appl. Soft Comput. 69, 213–231 (2018)

    Article  Google Scholar 

  4. Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44, 642–652 (2013)

    Article  Google Scholar 

  5. Medina-Oliva, G., Voisin, A., Monnin, M., Leger, J.-B.: Predictive diagnosis based on a fleet-wide ontology approach. Knowl.-Based Syst. 68, 40–57 (2014)

    Article  Google Scholar 

  6. Li, Z., Wang, Y., Wang, K.-S.: Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv. Manuf. 5(4), 377–387 (2017). https://doi.org/10.1007/s40436-017-0203-8

    Article  Google Scholar 

  7. Li, B.-H., et al.: Further discussion on cloud manufacturing. Comput. Integr. Manuf. Syst. 17, 449–457 (2011)

    Google Scholar 

  8. Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., Zhao, X.: Cloud manufacturing: from concept to practice. Enterp. Inf. Syst. 9, 186–209 (2015)

    Article  Google Scholar 

  9. Al-Dahidi, S., Di Maio, F., Baraldi, P., Zio, E.: Remaining useful life estimation in heterogeneous fleets working under variable operating conditions. Reliab. Eng. Syst. Saf. 156, 109–124 (2016)

    Article  Google Scholar 

  10. Olsson, E., Funk, P., Xiong, N.: Fault diagnosis in industry using sensor readings and case-based reasoning. J. Intell. Fuzzy Syst. 15, 41–46 (2004)

    Google Scholar 

  11. Umiliacchi, P., Lane, D., Romano, F., SpA, A.: Predictive maintenance of railway subsystems using an ontology based modelling approach. In: Proceedings of the World Conference on Railway Research, pp. 22–26 (2011)

    Google Scholar 

  12. Rigamonti, M., Baraldi, P., Zio, E., Astigarraga, D., Galarza, A.: Particle filter-based prognostics for an electrolytic capacitor working in variable operating conditions. IEEE Trans. Power Electron. 31, 1567–1575 (2015)

    Article  Google Scholar 

  13. Voisin, A., Medina-Oliva, G., Monnin, M., Leger, J.-B., Iung, B.: Fleet-wide diagnostic and prognostic assessment. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, New Orleans, LA, pp. 521–530 (2013)

    Google Scholar 

  14. Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, pp. 1–9. IEEE, (2008)

    Google Scholar 

  15. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Quart. 28, 75-105 (2004)

    Google Scholar 

  16. Venable, J., Pries-Heje, J., Baskerville, R.: A comprehensive framework for evaluation in design science research. In: Peffers, K., Rothenberger, M., Kuechler, B. (eds.) DESRIST 2012. LNCS, vol. 7286, pp. 423–438. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29863-9_31

    Chapter  Google Scholar 

  17. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25, 77–89 (2016)

    Article  Google Scholar 

  18. 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)

    Google Scholar 

  19. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41, 2065–2073 (2014)

    Article  Google Scholar 

  20. Ma, J., Jiang, J.: Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog. Nucl. Energy 53, 255–266 (2011)

    Article  Google Scholar 

  21. Mahyari, A.G.: Robust predictive maintenance for robotics via unsupervised transfer learning. In: Proceedings of the International FLAIRS Conference Proceedings, vol. 34 (2021)

    Google Scholar 

  22. Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6

    Article  Google Scholar 

  23. Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electron. Markets 1–11 (2021)

    Google Scholar 

  24. Chandra, L., Seidel, S., Gregor, S.: Prescriptive knowledge in IS research: conceptualizing design principles in terms of materiality, action, and boundary conditions. In: Proceedings of the 48th Hawaii International Conference on System Sciences, Kauai, pp. 4039–4048. IEEE (2015)

    Google Scholar 

  25. Broy, M., Gleirscher, M., Merenda, S., Wild, D., Kluge, P., Krenzer, W.: Toward a holistic and standardized automotive architecture description. Computer 42, 98–101 (2009)

    Article  Google Scholar 

  26. Meth, H., Mueller, B., Maedche, A.: Designing a requirement mining system. J. Assoc. Inf. Syst. 16, 799–837 (2015)

    Google Scholar 

Download references

Acknowledgement

This research and development project is funded by the Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) within the framework concept “Informations- und Kommunikationstechnik” (grant no. DIK0143/02) and managed by the project management agency VDI+VDE Innovation + Technik GmbH.

Author information

Authors and Affiliations

  1. Julius-Maximilians-Universität Würzburg, Würzburg, Germany

    Christian Janiesch, Jonas Wanner & Lukas-Valentin Herm

  2. HAW Landshut, Landshut, Germany

    Christian Janiesch

Authors
  1. Christian Janiesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonas Wanner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lukas-Valentin Herm
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Janiesch .

Editor information

Editors and Affiliations

  1. University of Liechtenstein, Vaduz, Liechtenstein

    Leona Chandra Kruse

  2. University of Liechtenstein, Vaduz, Liechtenstein

    Stefan Seidel

  3. University of Agder, Kristiansand, Norway

    Geir Inge Hausvik

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Janiesch, C., Wanner, J., Herm, LV. (2021). Design Principles for Shared Maintenance Analytics in Fleet Management. In: Chandra Kruse, L., Seidel, S., Hausvik, G.I. (eds) The Next Wave of Sociotechnical Design. DESRIST 2021. Lecture Notes in Computer Science(), vol 12807. Springer, Cham. https://doi.org/10.1007/978-3-030-82405-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-82405-1_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82404-4

  • Online ISBN: 978-3-030-82405-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation