Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/6797
Title: A Multi Agent System architecture to implement Collaborative Learning for social industrial assets
Authors: Bakliwal, Kshitij
Dhada, Maharshi Harshadbhai
Lad, Bhupesh Kumar
Keywords: Computer architecture;Cyber Physical System;Decision making;Digital twin;Distributed computer systems;Embedded systems;Industrial internet of things (IIoT);Learning systems;Real time systems;Turbofan engines;Collaborative learning;Cyber physical systems (CPSs);Industry automation;Information communication technology;Multi-agent system architecture;Real time decision-making;Seamless integration;Technological advances;Multi agent systems
Issue Date: 2018
Publisher: Elsevier B.V.
Citation: Bakliwal, K., Dhada, M. H., Palau, A. S., Parlikad, A. K., & Lad, B. K. (2018). A multi agent system architecture to implement collaborative learning for social industrial assets. Paper presented at the , 51(11) 1237-1242. doi:10.1016/j.ifacol.2018.08.421
Abstract: 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)). © 2018
URI: https://doi.org/10.1016/j.ifacol.2018.08.421
https://dspace.iiti.ac.in/handle/123456789/6797
ISSN: 2405-8963
Type of Material: Conference Paper
Appears in Collections:Department of Mechanical Engineering

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