期刊
IFAC PAPERSONLINE
卷 51, 期 11, 页码 1237-1242出版社
ELSEVIER
DOI: 10.1016/j.ifacol.2018.08.421
关键词
Cyber-Physical Systems; Industrial Internet of Things; Digital Twins; Collaborative Learning; Industry Automation; Multi Agent Systems; Distributed Computing
资金
- Royal Academy of Engineering under the Newton Bhabha scheme [HEPI-1516-10]
- EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Skodowska-Curie Research and Innovation Staff Exchange (Rise) [645733 Sustain-owner H2020-MSCA-RISE-2014]
- EPSRC [EP/K000314/1, EP/L010917/1, EP/R004935/1, EP/I019308/1] Funding Source: UKRI
The 'Industrial Internet of Things' aims to connect industrial assets with one another and benefit from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging concept here is that of 'social assets': assets that collaborate with each other in order to improve system optimisation. Cyber-Physical Systems (CPSs) are formed by embedding the assets with computers, or microcontrollers, which run real-time decision-making algorithms over the data originating from the asset. These are known as the 'Digital Twins' of the assets, and form the backbone of social assets. It is essential to have an architecture which enables a seamless integration of these technological advances for an industry. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the findings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify 'friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008)). (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据