Design modification supporting method based on product usage data in closed-loop PLM

Thanks to recent technology related to product monitoring and data communication, it is now possible for a company to gather various kinds of product usage data during its operation in a ubiquitous way. However, the application of the newly gathered data is still vague and immature. If the data is transformed into appropriate information or knowledge, it would be possible to improve products through design modification, maintenance enhancement, elaborate end-of-life decision and so on. This article deals with the application of product usage data to support design modification. Design modification can be achieved by finding and fixing defective design parameters and affecting factors. This done by a complex procedure which is composed of several steps including diverse data processing technique such as function structure model, degradation scenario, function performance degradation evaluation, clustering and relation matrix. The proposed method assesses the status of working product, classifies the field data (i.e. working environment/operation) according to the status and identifies suspicious field data causing the poor working status. Then, the suspicious field data is correlated with design parameters; thus highly defective design parameters can be identified. For the product improvement, the defective design parameters should be reviewed and modified with a high priority. The proposed method is validated by a case study on a locomotive braking system. This work shows the possibility to transform product usage data into information for product improvement, which can serve as a guideline for product usage data application for other product improvement through product usage data.

[1]  Tiranee Achalakul,et al.  Method for failure pattern analysis in disk drive manufacturing , 2011, Int. J. Comput. Integr. Manuf..

[2]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models , 2011 .

[3]  Jim Browne,et al.  Knowledge-enriched shop floor control in end-of-life business , 2011 .

[4]  Aarnout Brombacher,et al.  The use of reliability-oriented field feedback information for product design improvement: a case study , 2009, Qual. Reliab. Eng. Int..

[5]  Dimitris Kiritsis,et al.  A framework for RFID applications in product lifecycle management , 2009, Int. J. Comput. Integr. Manuf..

[6]  Robin R. Murphy,et al.  Reliability analysis of mobile robots , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  Karl T. Ulrich,et al.  Product Design and Development , 1995 .

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

[9]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

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

[11]  Lei Guo,et al.  Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description , 2009 .

[12]  Joseph Mathew,et al.  Intelligent condition-based prediction of machinery reliability , 2009 .

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

[14]  Dennis F. Kehoe,et al.  The Theory and Development of a Relationship Matrix-based Approach to Evaluating e-Marketplaces , 2006, Electron. Mark..

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

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

[17]  Michael Pecht,et al.  Environment and Usage Monitoringof Electronic Products for Health Assessment and Product Design , 2007 .

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

[19]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections , 2011, Expert Syst. Appl..

[20]  David F. Redmiles,et al.  Large-Scale Collection of Usage Data to Inform Design , 2001, INTERACT.

[21]  Don T. Phillips,et al.  An expert system framework for machine fault diagnosis , 1992 .

[22]  Erik Simmons.  The usage model: a structure for richly describing product usage during design and development , 2005, 13th IEEE International Conference on Requirements Engineering (RE'05).

[23]  Yong Zeng,et al.  A science-based approach to product design theory Part I: formulation and formalization of design process , 1999 .

[24]  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).

[25]  J. Dixon,et al.  Engineering Design , 2019, Springer Handbook of Mechanical Engineering.

[26]  M Srinivas,et al.  Product Design and Manufacturing Process Improvement Using Association Rules , 2006 .

[27]  P. van der Meulen,et al.  Design for Maintenance , 2008, 2008 IEEE/SEMI Advanced Semiconductor Manufacturing Conference.

[28]  Jörg Feldhusen,et al.  Design for Quality , 1996 .

[29]  John F. MacGregor,et al.  Product design through multivariate statistical analysis of process data , 1998 .