Industrie 4.0

Résumé

Le concept d’industrie 4.0 (aussi appelée industrie du futur ou quatrième révolution industrielle) correspond à une nouvelle façon d’organiser les moyens de production. Cette nouvelle industrie s'affirme comme la convergence du monde virtuel, de la conception numérique, de la gestion (opérations, finance et marketing) avec les produits et objets du monde réel. Les grandes promesses de cette quatrième révolution industrielle sont de séduire les consommateurs avec des produits uniques et personnalisés, et malgré de faibles volumes de fabrication, de maintenir des gains. Ces mêmes consommateurs peuvent ainsi communiquer avec les machines durant les phases de réalisation : ce type de production s'appelle « smart production » (en). Selon ce principe, dans le contexte de l’automatisation industrielle, cela se caractérise par la mise en œuvre de capteurs qui sont les éléments de base des Système de contrôle et d'acquisition de données en temps réel (SCADA). Ils permettent de transformer de

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Detecting Latent Training Needs Using Large Datasets

Ramtin Yazdanian

In today's world, there is no shortage of disruptors acting on various professional domains. The Fourth Industrial Revolution, with its AI-driven and automation-focused technologies, has fundamentally changed many domains -- particularly the Information and Communication Technologies (ICT) domain -- and continues to do so. The COVID-19 pandemic and its ensuing lockdowns have resulted in dramatic change in the workplace. These disruptors are fast-acting and rapidly change the skills landscape of many professional domains, introducing new skills and rendering old ones obsolete. In such a situation, it is imperative for educational institutions to keep their curricula updated in order to (re)train the workforce with the right set of skills for the new economy. However, most of the methodologies commonly used for analyzing training needs and effecting curricular change were developed during a time when the pace of change was slower, and thus have trouble quickly and effectively responding to today's rapid changes. As a result, the development of new approaches for the analysis of training needs is of utmost importance.The present dissertation is the result of an effort to develop such new approaches, using different types of data and approaching the problem from different perspectives. These approaches use big data methods on data sources that are large, pre-existing, and continually updated in order to identify the skill needs of the present and/or predict the skill needs of the future for various professions. Focusing on the ICT domain but also making forays into vocational domains, the present work examines the usefulness and feasibility of methodologies such as the estimation of topical difficulty, the prediction of the appearance of new skills, and the prediction of emerging skills (less popular skills expected to experience a surge in hiring demand) for the identification of training needs. The findings of this dissertation are twofold. Firstly, they demonstrate the pivotal role that decentralized educational platforms such as Stack Overflow (an online Q&A platform for ICT) and Udemy (a MOOC platform where anyone can create a course) play in lifelong learning, essentially making the case for a policy of sponsorship and promotion of such platforms in other professional domains. Secondly and more importantly, the results showcase the feasibility of predicting the emerging skills of the near future. The successful emerging skills methodology can therefore be used to provide valuable insights to training providers at a moment's notice, giving them time to prepare for the skills landscape of the future in advance. Thus, AI can be used to help solve the challenges it creates for educational institutions and the workforce. Future work, both in the form of policymaking and in the form of research, can help capitalize and expand on the results of this dissertation, furthering the cause of rapid curricular change.

EPFL2022

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Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A concept drift is a change in data distribution which will, at some point, decrease the accuracy of the model. To address this problem, various frameworks are presented in the literature, but there is no optimal methodology for implementing them. This paper presents a methodology to implement a problem-oriented complete solution to ensure the maintenance of an industrial data-based model. The final drift-handling solution is composed of a sampling decision system and an update system. The methodology begins with a concept-drift identification phase. Solutions are then pre-selected based on the identified concept drifts. Next, an optimization problem is designed to select the solution that optimizes the costs and respects the constraints. To better link the concept drift characteristics and the drift-handling solutions, a causal concept-drift classification system is proposed. The industrial implementation of such a solution is discussed and several questions are raised. This paper presents an original and detailed methodology that shows encouraging results to address the model-maintenance challenge; however, concept drift identification, and links between concept-drift charac-teristics and drift detection, require further research.

ELSEVIER SCI LTD2022

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As of today, to cope with traditional maintenance policies such as reactive and preventive maintenance, the manufacturing companies need the deployment of adaptive and responsive maintenance strategies. Meanwhile, the advent of Industry 4.0 leads the maintenance paradigm shift facilitated by the efficient monitoring of physical assets and forecasting of the potential risks. As the advanced maintenance policies benefit in terms of cost-efficiency, inventory management and reliability management, most of the manufacturing companies are trying to make their own advanced maintenance strategies and to elaborate on the development of an innovative platform for it. However, since advanced enabling technologies collect a huge amount of data from different data sources such as machine, component, document, process and so on, data federation should necessarily be achieved for further discussion, but manufacturing companies are immature to address this issue. H2020 EU project Z-BRE4K, i.e., Strategies and predictive maintenance models wrapped around physical systems for zero unexpected-breakdowns and increased operating life of factories, deploys semantic technologies to address this issue. This paper deals with the debate on how to efficiently federate various data formats with the support of the semantic technologies in the context of maintenance. In addition, it proposes a maintenance ontology validated and implemented with an actor from European industry. Copyright (C) 2020 The Authors.

ELSEVIER2020

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Detecting Latent Training Needs Using Large Datasets

Ramtin Yazdanian

EPFL2022