EPIDEMOLOGICAL MODELS OF PHENOMENA PROPAGATION IN HOTS

摘要

The industrial Internet of Things networks (IIoTs), are systems where industrial resources (workstations, storage or control areas, cobots, etc.) are interconnected, in highly developed industry. It offers many advantages for smart manufacturing and offers high reactivity and flexibility. The reliability and security of these networks, however, is still low due to the high density and heterogeneity of the connected objects together with complexity of the network. It is therefore crucial to propose monitoring methodologies to ensure reduced propagation of problems like failure or energy over-consumption in the network and also to optimize maintenance strategies. The aim of our work is to define an epidemiological model of disease propagation adapted to IIoTs. The proposed model allows evaluation of the implementation dynamics and will represent the phenomena of intermediate degradation by identifying system transitional states. It will explore and guide on the best maintenance strategy to be implemented for production efficiency. Indeed, to identify key points in the smart production system, dynamic analysis of hazards propagation ensures and monitors the state of the production interconnected resources through e-maintenance.