Multi-agent system architectures for collaborative prognostics

Adrià Salvador Palau (), Maharshi Harshadbhai Dhada and Ajith Kumar Parlikad
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Adrià Salvador Palau: University of Cambridge
Maharshi Harshadbhai Dhada: University of Cambridge
Ajith Kumar Parlikad: University of Cambridge

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 8, No 14, 2999-3013

Abstract: 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.

Keywords: Multi-agent systems; Distributed systems; Prognostics; Asset management; Predictive maintenance; Cost assessment (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10845-019-01478-9

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