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  • Automatically detecting component failures and unexpected behaviors is an essential service for achieving fault-tolerant robust manufacturing systems. The application of a multi-agent system is regarded as a promising approach for designing complex systems such as manufacturing systems due to its distributed nature. In this paper we present an agent-based control system with diagnostic capabilities on several layers for a pallet transport system. Local diagnostic tasks for detecting failures are performed by the automation agents each controlling one physical component such as a diverter. In order to observe the correct behavior of the components on a system-wide scale, an approach based on Hidden Markov Models linked to the agents is employed.
  • Automatically detecting component failures and unexpected behaviors is an essential service for achieving fault-tolerant robust manufacturing systems. The application of a multi-agent system is regarded as a promising approach for designing complex systems such as manufacturing systems due to its distributed nature. In this paper we present an agent-based control system with diagnostic capabilities on several layers for a pallet transport system. Local diagnostic tasks for detecting failures are performed by the automation agents each controlling one physical component such as a diverter. In order to observe the correct behavior of the components on a system-wide scale, an approach based on Hidden Markov Models linked to the agents is employed. (en)
Title
  • A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents
  • A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents (en)
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  • A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents
  • A Multi-Layer Approach for Failure Detection in a Manufacturing System Based on Automation Agents (en)
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  • RIV/68407700:21230/12:00194497!RIV13-MSM-21230___
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  • I
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  • 120327
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  • RIV/68407700:21230/12:00194497
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  • Hidden Markov Model; IEC 61499; Diagnostics; Heartbeat Mechanism; World Model (en)
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http://linked.open...ontrolniKodProRIV
  • [6669252446E1]
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  • Las Vegas
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  • Los Alamitos
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  • Proceedings of the Ninth International Conference on Information Technology (ITNG 2012)
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  • Vrba, Pavel
  • Jirkovský, Václav
  • Kadera, Petr
  • Lepuschitz, W. L.
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number of pages
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  • 10.1109/ITNG.2012.8
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  • IEEE Computer Society
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  • 978-0-7695-4654-4
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  • 21230
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