An agent based monitoring architecture for plug and produce based manufacturing systems

In recent years a set of production paradigms were proposed in order to allow companies the possibility to face the new market requirements and needs, such as the new demand for highly customized products and the decrease of product's life cycle. These new paradigms propose solutions capable of facing these requirements, giving manufacturing systems high capacity to adapt along with high flexibility and intelligence in order to deal with disturbances, like unexpected orders or malfunctions. Evolvable Production Systems propose a solution based on the usage of modularity and self-organization to support pluggability and in this way allow the companies to add and / or remove components during execution without any programming effort and without wasting much valued time. Although these new systems give companies the capacity to be more flexible, they were not designed to perform monitoring as flexible as the control system itself, because the usual monitoring software, mostly based on SCADA systems, is not capable of re-adapting during execution to this new plugging and / or unplugging of devices and to changes in the characteristics of the entire system's topology. Considering these aspects, this article proposes a fully distributed agent based architecture, capable of performing monitoring at different levels while still supporting the addition and removal of monitoring entities, responsible for data extraction and analysis during runtime.

[1]  David E. Culler,et al.  The ganglia distributed monitoring system: design, implementation, and experience , 2004, Parallel Comput..

[2]  Mauro Onori,et al.  Self-organization in automation - the IDEAS pre-demonstrator , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[3]  Paulo Leitão,et al.  A holonic approach to dynamic manufacturing scheduling , 2008 .

[4]  F. Jovane,et al.  Reconfigurable Manufacturing Systems , 1999 .

[5]  Agostino Poggi,et al.  Jade - a fipa-compliant agent framework , 1999 .

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

[7]  A. Gunasekaran,et al.  Agile manufacturing: The drivers, concepts and attributes , 1999 .

[8]  Mikal Ziane,et al.  Monitoring and organizational-level adaptation of multi-agent systems , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[9]  Paulo Leitão,et al.  ADACOR: A holonic architecture for agile and adaptive manufacturing control , 2006, Comput. Ind..

[10]  Li Lin,et al.  Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets , 2003, J. Intell. Manuf..

[11]  Mauro Onori,et al.  Evolvable Assembly Systems Basic Principles , 2006, BASYS.

[12]  Mary Shaw,et al.  Software Engineering for Self-Adaptive Systems: A Research Roadmap , 2009, Software Engineering for Self-Adaptive Systems.

[13]  Yoram Koren,et al.  Design of reconfigurable manufacturing systems , 2010 .

[14]  M. Onori,et al.  An architecture development approach for evolvable assembly systems , 2005, (ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005..

[15]  Giovanna Di Marzo Serugendo,et al.  Designing Self-Organization for Evolvable Assembly Systems , 2008, 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[16]  J. Barata,et al.  Evolvable production systems , 2009, 2009 IEEE International Symposium on Assembly and Manufacturing.

[17]  Luis Ribeiro,et al.  Re-thinking diagnosis for future automation systems: An analysis of current diagnostic practices and their applicability in emerging IT based production paradigms , 2011, Comput. Ind..

[18]  J.R. McDonald,et al.  Applying multi-agent system technology in practice: automated management and analysis of SCADA and digital fault recorder data , 2006, 2006 IEEE Power Engineering Society General Meeting.

[19]  Lifeng Xi,et al.  A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes , 2009, Expert Syst. Appl..

[20]  Giovanni Di Orio Adapter module for self-learning production systems , 2013 .

[21]  Angappa Gunasekaran,et al.  Agile manufacturing: A framework for research and development , 1999 .

[22]  W. Mahmood,et al.  A Multi-Agent system for data collection from power generators and weather stations in heterogeneous environments , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.