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

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

MMC.
2023;
Volume 10, Number 4
: pp. 1023–1036
https://doi.org/10.23939/mmc2023.04.1023
Received: May 26, 2023
Revised: November 22, 2023
Accepted: November 23, 2023

Mathematical Modeling and Computing, Vol. 10, No. 4, pp. 1023–1036 (2023)

Authors:
  1. I. Mallouk
  2. B. Abou el Majd
  3. Y. Sallez
1
University Polytechnique des Hauts-de-France; LMSA, FSR, Mohammed V University in Rabat
2
LMSA, FSR, Mohammed V University in Rabat
3
University Polytechnique des Hauts-de-France

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.

manufacturing
decision-making
prognostic and health management (PHM)
long short-term memory (LSTM)
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