Causality Learning Approach for Supervision in the Context of Industry 4.0 - Archive ouverte HAL
Chapitre D'ouvrage Année : 2021

Causality Learning Approach for Supervision in the Context of Industry 4.0

Résumé

In order to have a full control on their processes, companies need to ensure real time monitoring and supervision using Key Performance Indicators (KPI). KPIs serve as a powerful tool to inform about the process flow status and objectives’ achievement. Although, experts are consulted to analyze, interpret, and explain KPIs’ values in order to extensively identify all influencing factors; this does not seem completely guaranteed if they only rely on their experience. In this paper, the authors propose a generic causality learning approach for monitoring and supervision. A causality analysis of KPIs’ values is hence presented, in addition to a prioritization of their influencing factors in order to provide a decision support. A KPI prediction is also suggested so that actions can be anticipated.
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Dates et versions

hal-03949196 , version 1 (20-01-2023)

Identifiants

Citer

Kenza Amzil, Esma Yahia, Nathalie Klement, Lionel Roucoules. Causality Learning Approach for Supervision in the Context of Industry 4.0. Advances on Mechanics, Design Engineering and Manufacturing III, Springer International Publishing; Springer International Publishing, pp.316-322, 2021, Lecture Notes in Mechanical Engineering, ⟨10.1007/978-3-030-70566-4_50⟩. ⟨hal-03949196⟩
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