System performance prognostic: context, issues and requirements

Maintenance plays now a critical role in manufacturing for achieving important cost savings and competitive advantage while preserving product and process conditions. Such a role suggests moving from conventional maintenance practices to predictive strategy. However industrial systems are complex and need to have a global view of the system health and its performance. Indeed a maintenance action has to be done at the right time according to the system performance and the component Remaining Useful Life (RUL) given by a prognostic process. Nevertheless system performance prognostic are lacking in generic methodology and support tools for assessing system performance vs. component degradations. In that way, generic concepts in relation with dysfunctional causality of the system performance are introduced. These concepts are traditionally modelled separately although they interact with each others. Thus this paper aims at giving issues and requirements on models representative of each concept and for information to share between them in order to reach a global system performance prognostic model.

[1]  Benoît Iung,et al.  Degradation state model-based prognosis for proactively maintaining product performance , 2008 .

[2]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[3]  Laurent Doyen,et al.  Classes of imperfect repair models based on reduction of failure intensity or virtual age , 2004, Reliab. Eng. Syst. Saf..

[4]  Martin Doerr,et al.  The CIDOC Conceptual Reference Module , 2003 .

[5]  J.-B. Leger,et al.  Integration of maintenance in the enterprise: Towards an enterprise modelling-based framework compliant with proactive maintenance strategy , 2001 .

[6]  M.J. Roemer,et al.  Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft] , 2002, Proceedings, IEEE Aerospace Conference.

[7]  Martin Doerr,et al.  The CIDOC Conceptual Reference Module: An Ontological Approach to Semantic Interoperability of Metadata , 2003, AI Mag..

[8]  Benoît Iung,et al.  Methodology for assessing system performance loss within a proactive maintenance framework , 2009, ArXiv.

[9]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

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

[11]  Dimitris Kiritsis,et al.  Function performance evaluation and its application for design modification based on product usage data , 2009 .

[12]  Jay Lee,et al.  Maintenance: Changing role in life cycle management , 2004 .

[13]  Benoît Iung,et al.  Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed , 2010, J. Intell. Manuf..

[14]  Benoît Iung,et al.  Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system , 2008, Reliab. Eng. Syst. Saf..