Advertisement

Information and Data Provision of Operational Data for the Improvement of Product Development

  • Klaus-Dieter ThobenEmail author
  • Marco Lewandowski
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 467)

Abstract

Today’s usage of supporting technologies like RFID, condition monitoring or further embedded systems provides a huge amount of data to the operation and maintenance (O&M) phase of complex technical systems. While analyzing this data for the purpose of more efficient operation is already extensively adopted, the transfer of data to other lifecycle phases is most often lacking. This paper will analyze the obstacles and requirements for information and data provision from the usage phase in order to support the development of next generation products. This is carried out by analyzing sub-aspects of data provisioning for product development purposes thus leading to a comprehensive framework for the reorganization of information backflows from the O&M phase. The findings are discussed in the case of a windfarm. The paper gives a valuable insight regarding the derivation of targets and action fields for information and data provision to improve the product development process.

Keywords

Product lifecycle management Internet of things Operation and maintenance Maintenance concepts Data mining Wind turbines 

References

  1. 1.
    Mobley, R.K.: An Introduction to Predictive Maintenance, 2nd edn. Butterworth-Heinemann, Amsterdam (2002)Google Scholar
  2. 2.
    Wuest, T., Hribernik, K.A., Thoben, K.D.: Capturing, managing and sharing product information along the lifecycle for design improvement. In: Meyer, A., Schirmeyer, R., Sándor, V. (eds.) Proceedings of the 10th International Workshop on Integrated Design Engineering, pp. 107–115. Inst. of Machine Design. Univ, Magdeburg (2015)Google Scholar
  3. 3.
    Wortmann, F., Flüchter, K.: Internet of things. Bus. Inf. Syst. Eng. 57(3), 221–224 (2015). doi: 10.1007/s12599-015-0383-3 CrossRefGoogle Scholar
  4. 4.
    Finger, S., Dixon, J.: A review of research in mechanical engineering design. Part I: descriptive, prescriptive, and computer-based models of design processes. Res. Eng. Des. 1(1), 51–67 (1989). doi: 10.1007/BF01580003 CrossRefGoogle Scholar
  5. 5.
    Gold, A.H., Malhotra, A., Segars, A.H.: Knowledge management: an organizational capabilities perspective. J. Manage. Inf. Syst. 18(1), 185–214 (2001)Google Scholar
  6. 6.
    Jun, H., Kiritsis, D., Xirouchakis, P.: Closed-loop PLM. In: Thoben, M., Thoben, K., Montorio, M. (eds.) Advanced Manufacturing: An ICT and Systems Perspective, pp. 79–87. Taylor and Francis, London (2007)Google Scholar
  7. 7.
    Jun, H., Kiritsis, D., Xirouchakis, P.: Research issues on closed-loop PLM. Comput. Ind. 58, 855–868 (2007). doi: 10.1016/j.compind.2007.04.001 CrossRefGoogle Scholar
  8. 8.
    Jun, H., Shin, J., Kiritsis, D., et al.: System architecture for closed-loop PLM. Int. J. Comput. Integr. Manuf. 20(7), 684–698 (2007). doi: 10.1080/09511920701566624 CrossRefGoogle Scholar
  9. 9.
    Hribernik, K.A.: The Product Avatar as a product-instance-centric information management concept. Product lifecycle management: emerging solutions and challenges for global networked enterprise. In: Proceedings of the International Conference on Product Life Cycle Management (PLM 2005) held at the Lumière University, Lyon, France during the 11 – 13th July 2005, pp. 10–20 (2005)Google Scholar
  10. 10.
    Wuest, T., Hribernik, K., Thoben, K-D.: Digital representations of intelligent products: product avatar 2.0. In: Abramovici, M., Stark, R. (eds.) Smart Product Engineering. LNPE, vol. 5, pp. 675–684. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Lee, E.A.: Cyber physical systems: design challenges. In: 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369. IEEE (2008)Google Scholar
  12. 12.
    Broy, M.: Cyber-physical Systems: Innovation durch softwareintensive eingebettete Systeme. Acatech DISKUTIERT. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    McFarlane, D., Sarma, S., Chirn, J.L., et al.: Auto ID systems and intelligent manufacturing control. Intelligent Manufacturing 16(4), 365–376 (2003). doi: 10.1016/S0952-1976(03)00077-0 Google Scholar
  14. 14.
    IoT and Big Data: A Joint Whitepaper by Bosch Software Innovations and MongoDB (2014)Google Scholar
  15. 15.
    Chakravarthy, S.: Stream Data Processing: A Quality of Service Perspective. Advances in Database Systems, vol. 36. Springer, New York (2009)zbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Institute for Integrated Product DevelopmentUniversity of BremenBremenGermany
  2. 2.BIBA – Bremer Institut Für Produktion Und Logistik GmbHUniversity of BremenBremenGermany

Personalised recommendations