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. Di fferent 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 di fferent 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 signi ficantly decrease the whole-life cost of their assets.The project that has generated these results has been supported by a la Caixa Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049. This research was partly supported by Siemens Industrial Turbomachinery UK. This research was also partly supported by the Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT. The server used to perform the experiments in this paper was funded by the Centre for Digital Built Britain
