A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context

Abstract

Nowadays, manufacturers must deal with huge international competition and continually improve their performances. In this context, several essential approaches namely CBM (Condition-based maintenance), PHM (Prognostics and Health Management), and PLM (Product Lifecycle Management) are used for manufacturing systems to maintain and increase their availability, reliability and performance. This implies that operational usage data of the manufacturing equipment must then be made available to all stakeholders concerned through efficient informational chains. However confronted with a large amount of data, the stakeholders must be assisted in their decision-making. This paper aims to propose a generic architecture that models the information and decision chain from the target system to the relevant stakeholders by assisting them in their decision-making. The proposed generic architecture is illustrated by a use case based on the LSTM (Long Short-Term Memory) algorithm in the context of energy management for a fleet of mobile robots.