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

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.

[1]  J.S.R. Jayaram,et al.  Reliability prediction through degradation data modeling using a quasi-likelihood approach , 2005, Annual Reliability and Maintainability Symposium, 2005. Proceedings..

[2]  Jay Lee,et al.  Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction , 2003, Adv. Eng. Informatics.

[3]  M. Xie,et al.  Planning of step-stress accelerated degradation test , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.

[4]  Henry P. Wynn,et al.  A Multi-scale Approach to Functional Signature Analysis for Product End-of-Life Management , 2004 .

[5]  David J. Evans,et al.  A real-time predictive maintenance system for machine systems , 2004 .

[6]  Holger Giese,et al.  Component-Based Hazard Analysis: Optimal Designs, Product Lines, and Online-Reconfiguration , 2006, SAFECOMP.

[7]  Kevin Delaney,et al.  Design improvement using process capability data , 2009 .

[8]  Hsien-Yu Tseng,et al.  A neural network application for reliability modelling and condition-based predictive maintenance , 2005 .

[9]  E. J. Zampino Application of fault-tree analysis to troubleshooting the NASA GRC icing research tunnel , 2001, Annual Reliability and Maintainability Symposium. 2001 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.01CH37179).

[10]  Limei Xu,et al.  Equipment Fault Forecasting Based on a Two-level Hierarchical Model , 2007, 2007 IEEE International Conference on Automation and Logistics.

[11]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[12]  D. N. P. Murthy,et al.  Product performance and specification in new product development , 2006 .

[13]  Jian-Bo Yang,et al.  Development of a fuzzy FMEA based product design system , 2008 .

[14]  Irem Y. Tumer,et al.  Linking product functionality to historic failures to improve failure analysis in design , 2005 .