Context extraction for self-learning production systems

A new approach for the realisation of self-learning production systems based on a context aware approach, allowing self-adaptation of flexible manufacturing processes in production systems, is presented. The usage of dynamically changing context for adaptation of flexible manufacturing lines/processes is proposed. The presented solution includes services for context extraction, adaptation and self-learning allowing high adaptation of production systems depending on the identified context. A generic architecture following Service Oriented Principles is presented allowing for integration of the proposed solution into various production systems. A successful application of the developed solution in real world manufacturing environment is presented.

[1]  Tapio Seppänen,et al.  RDF-based model for context-aware reasoning in rich service environment , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

[2]  Wolfgang Kellerer,et al.  Situational reasoning - a practical OWL use case , 2005, Proceedings Autonomous Decentralized Systems, 2005. ISADS 2005..

[3]  Noureddine Zerhouni,et al.  A neuro-fuzzy monitoring system: Application to flexible production systems , 2006, Comput. Ind..

[4]  Wolfgang Mahnke,et al.  OPC Unified Architecture The future standard for communication and information modeling in automation , 2009 .

[5]  Dragan Stokic,et al.  Service-based knowledge monitoring of collaborative environments for user-context sensitive enhancement , 2009, 2009 IEEE International Technology Management Conference (ICE).

[6]  Stefan Decker,et al.  Creating Semantic Web Contents with Protégé-2000 , 2001, IEEE Intell. Syst..

[7]  Dragan Stokic,et al.  Innovating in Product/Process Development , 2009 .

[8]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[9]  Dragan Stokic,et al.  Self-learning embedded services for integration of complex, flexible production systems , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[10]  Dragan Stokic,et al.  Reliable Self-Learning Production Systems Based on Context Aware Services , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[11]  Rui Neves-Silva,et al.  A SERVICE ORIENTED FRAMEWORK FOR CONTEXT AWARE KNOWLEDGE ENHANCING , 2010 .

[12]  Ahmed Karmouch,et al.  ACAI: agent-based context-aware infrastructure for spontaneous applications , 2005, J. Netw. Comput. Appl..

[13]  Dragan Stokic,et al.  Energy efficiency improvement through context sensitive self-learning of machine availability , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[14]  Xiaojun Zhou,et al.  Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA , 2008, Comput. Ind..

[15]  S. C. Liu,et al.  An Efficient Expert System for Machine Fault Diagnosis , 2003 .

[16]  J. Barata,et al.  A generic communication interface for DPWS-based web services , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[17]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.

[18]  K. Murphy,et al.  Overview of Machine Learning , 2022, International Journal of Advanced Research in Science, Communication and Technology.

[19]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[20]  REPORT OF THE HIGH LEVEL GROUP FOR THE DEVELOPMENT OF THE MULTILATERAL QUOTA SYSTEM , 2011 .

[21]  Volker Haarslev,et al.  RACER System Description , 2001, IJCAR.

[22]  Rui Neves-Silva,et al.  Services for context sensitive enhancing of knowledge in networked enterprises , 2008, 2008 IEEE International Technology Management Conference (ICE).

[23]  László Monostori,et al.  AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing , 2003 .