Function performance evaluation and its application for design modification based on product usage data

  • Jong-Ho Shin
  • Dimitris Kiritsis
  • Paul Xirouchakis
Conference paper


In recent years, companies have been able to gather more data from their products thanks to new technologies such as product embedded information devices (PEIDs, Kiritsis et al. (2004)), advanced sensors, internet, wireless telecommunication, and so on. Using them, companies can access working products directly, monitor and handle products remotely, and transfer generated data back to appropriate company repositories wirelessly. However, the application of the newly gathered data is still primitive since it has been difficult to obtain this kind of data without the recently developed new technologies. The newly gathered data can be applicable for product improvement in that it is transformed into appropriate information and knowledge. To this end, we propose a new method to manage the newly gathered data to complete closed-loop PLM. The usage data gathered at the MOL phase is transferred to the BOL phase for design modification so as to improve the product. To do this, we define new terms regarding function performance considering the historical change of function performance. The proposed definitions are developed to be used in design modification so that they help engineers to understand components/parts working status during the usage period of a product. Based on the evaluation of components/parts working status, the critical components/parts are discriminated. For the found critical components/parts, the working status of them is examined and correlated with field data which consists of operational and environmental data. The correlation provides engineers with critical field data which has an important effect on the worse working status. Hence, the proposed method provides the transformation from usage data gathered in the MOL phase to information for design improvement in the BOL phase. To verify our method, we use a locomotive case study.


Quality Function Deployment Importance Rate Controller Area Network Design Improvement Product Lifecycle Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bansal, D., D. J. Evans, et al. (2005) A Real-Time Predictive Maintenance System for Machine Systems - An Alternative to Expensive Motion Sensing Technology. Sensors for Industry Conference, 2005.Google Scholar
  2. 2.
    Bucchianico, A. et al. (2004) A multi-scale approach to functional signature analysis for product end-of-life management. Quality and Reliability Engineering International, 20(5), 457-467.CrossRefGoogle Scholar
  3. 3.
    Chang-Ching, L. & T. Hsien-Yu (2005) A neural network application for reliability modelling and condition-based predictive maintenance. International Journal of Advanced Manufacturing Technology, 25(1-2), 174-179.CrossRefGoogle Scholar
  4. 4.
    Chin, K.-S., A. Chan, et al. (2008) Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology, 36(7-8), 633-649.CrossRefGoogle Scholar
  5. 5.
    Delaney, K. D. & P. Phelan (2009) Design improvement using process capability data. Journal of Materials Processing Technology, 209(1), 619-624.CrossRefGoogle Scholar
  6. 6.
    Djurdjanovic, D. et al. (2003) Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 17(3-4), 109-125.CrossRefGoogle Scholar
  7. 7.
    Giese, H. and M. Tichy (2006) Component-based hazard analysis: Optimal designs, product lines, and onlinereconfiguration. Gdansk, Poland, Springer Verlag.Google Scholar
  8. 8.
    Jayaram, J. S. R. & Girish, T. (2005) Reliability prediction through degradation data modeling using a quasi-likelihood approach. Annual Reliability and Maintainability Symposium, 2005 Proceedings, pp.193--199.Google Scholar
  9. 9.
    Kiritsis, D. (2004) Ubiquitous product lifecycle management using product embedded information devices. Intelligent Maintenance System (IMS) 2004 International Conference, Arles.Google Scholar
  10. 10.
    Lee, J. et al. (2006) Intelligent prognostics tools and e-maintenance. Computers in Industry, 57(6), 476-489.CrossRefGoogle Scholar
  11. 11.
    Osteras, T. et al. (2006) Product performance and specification in new product development. Journal of Engineering Design, 17(2), 177-192.CrossRefGoogle Scholar
  12. 12.
    Stone, R., I. Tumer, et al. (2005) Linking product functionality to historic failures to improve failure analysis in design. Research in Engineering Design, 16(1), 96-108.CrossRefGoogle Scholar
  13. 13.
    Tang, L. C. et al. (2004) Planning of step-stress accelerated degradation test. Annual Reliability and Maintainability Symposium, 2004 Proceedings, pp.287-292.Google Scholar
  14. 14.
    Xiaoling, B., X. Quanzhi, et al. (2007) Equipment fault forecasting based on a two-level hierarchical model. Piscataway, NJ, USA, IEEE.Google Scholar
  15. 15.
    Zampino, E. J. (2001) Application of fault-tree analysis to troubleshooting the NASA GRC icing research tunnel. Piscataway, NJ, USA, IEEE.Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Jong-Ho Shin
    • 1
  • Dimitris Kiritsis
    • 1
  • Paul Xirouchakis
    • 1
  1. 1.Institute de Génie MécaniqueEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations