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Multi-agent system architectures for collaborative prognostics

Author

Listed:
  • Adrià Salvador Palau

    (University of Cambridge)

  • Maharshi Harshadbhai Dhada

    (University of Cambridge)

  • Ajith Kumar Parlikad

    (University of Cambridge)

Abstract

This paper provides a methodology to assess the optimal multi-agent architecture for collaborative prognostics in modern fleets of assets. The use of multi-agent systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Different architectures have been postulated for industrial multi-agent systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using different multi-agent systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement multi-agent systems for predictive maintenance that significantly decrease the whole-life cost of their assets.

Suggested Citation

  • Adrià Salvador Palau & Maharshi Harshadbhai Dhada & Ajith Kumar Parlikad, 2019. "Multi-agent system architectures for collaborative prognostics," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2999-3013, December.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-019-01478-9
    DOI: 10.1007/s10845-019-01478-9
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    References listed on IDEAS

    as
    1. Luca Fasanotti & Sergio Cavalieri & Emanuele Dovere & Paolo Gaiardelli & Carlos E Pereira, 2018. "An artificial immune intelligent maintenance system for distributed industrial environments," Journal of Risk and Reliability, , vol. 232(4), pages 401-414, August.
    2. Hao Li & Adrià Salvador Palau & Ajith Kumar Parlikad, 2018. "A social network of collaborating industrial assets," Journal of Risk and Reliability, , vol. 232(4), pages 389-400, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Edgar Chacón & Luis Alberto Cruz Salazar & Juan Cardillo & Yenny Alexandra Paredes Astudillo, 2021. "A control architecture for continuous production processes based on industry 4.0: water supply systems application," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2061-2081, October.
    2. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    3. Mezzour Ghita & Benhadou Siham & Medromi Hicham & Mounaam Amine, 2022. "HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring," Energies, MDPI, vol. 15(15), pages 1-38, July.
    4. Matthias Seitz & Felix Gehlhoff & Luis Alberto Cruz Salazar & Alexander Fay & Birgit Vogel-Heuser, 2021. "Automation platform independent multi-agent system for robust networks of production resources in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2023-2041, October.
    5. Eleonora Herrera-Medina & Antoni Riera Font, 2023. "A Multiagent Game Theoretic Simulation of Public Policy Coordination through Collaboration," Sustainability, MDPI, vol. 15(15), pages 1-20, August.
    6. William Derigent & Olivier Cardin & Damien Trentesaux, 2021. "Industry 4.0: contributions of holonic manufacturing control architectures and future challenges," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1797-1818, October.

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