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Theses

Autonomic Process Management for Industry 4.0

Abstract : Because of the digital revolution, also known as Industry 3.0, the boundaries between the physical and digital worlds are shrinking to give life to a more interconnected and smart factories. These factories allow employees, machines, processes, and products to interact oriented to provide a better organization of all the productive means, empowering the entire company itself to achieve higher levels of efficiency and productivity. These technologies are profoundly transforming our society, allowing customizing everything in detail, reducing goods and services costs, transforming worker's and job’s conditions for safety and security, among others. In that sense, Industry 3.0 acted as a catalyst that promoted new production mechanisms, which originated a new industrial revolution known as Industry 4.0. The concept of Industry 4.0, is used to designate the new generation of connected, robotics, and intelligent factories. Fundamentally, the vision of Industry 4.0 is to give smart capabilities to the production and physical operations to create a more holistic and better-connected ecosystem. One crucial aspect to consider, regarding the idea of the Industry 4.0 concept, is related to integrability and interoperability of the actors involved in manufacturing processes. It means that people, things, processes, and data have to be able not only to make decisions for themselves and to carry out their work in a more autonomous way (independence) but, also, the self-management of the whole factory (need to promote integrability and interoperability). The previous statement implies that the production processes’ actors should be able to autonomously negotiate in order to reach agreements linked to achieve both individual and collective production goals. In that sense, Industry 4.0 represents not only a new way to produce goods and services but also a crucial integration challenge of the actors involved in the manufacturing processes that need connection, communication, coordination, cooperation, and collaboration (denoted as 5C) capabilities that allow them to comply with the vision of Industry 4.0. Principally, this thesis aims at empowering processes management for Industry 4.0, proposing a stack of five levels, denoted as 5C. The 5C stack levels represent a way to deal with integration and interoperability challenges so that they can be solved incrementally at each level. From this perspective, we must start solving connection and communication issues as a first step to promote more elaborated organization processes like coordination, cooperation, and collaboration. Mainly, the 5C denote the elements needed to allow autonomous integration and interoperability of actors in Industry 4.0. From this point of view, in this thesis project, we present a first contribution that is oriented to deal with the integration challenges regarding the Industry 4.0 context at the level of connection and communication. This solution is based in a Multi-agent system in which the physical elements of the system are characterized virtually as agents. Notably, the use of Multi-agent systems allows creating an intelligent environment dotted with characteristics of autonomy, decentralization, self-organization, self-direction, standardized protocol, and other properties of Multi-agent systems. Moreover, the proposed solution allows actors to extend their limited capabilities with service deployed through the Internet, as an intent to automatize, optimize, and in more mature stages, transform any environment into a fully integrated, automated, and intelligent environment. Consequently, the proposed architecture will be evaluated and compared to previous researches in this field. In the second place, we will solve some integration challenges of Industry 4.0 at the level of coordination, cooperation, and collaboration. In this case, we design a framework for autonomous integration of actors in Industry 4.0, to allow them to autonomously coordinate, cooperate, and collaborate. This framework uses technologies like the Internet of Everything, Everything mining, and Autonomic computing. Next, we design some autonomic cycles of data analytics tasks, oriented to enable autonomous coordination in manufacturing processes. Fundamentally, these data analytics tasks create the knowledge bases needed in a production environment to support self-planning, self-manage, self-supervising, self-healing, etc. to the manufacturing process. Finally, we implement an autonomous cycle of data analytics tasks for self-supervising, using several Everything-mining techniques over data sources corresponding to a real manufacturing process. It defines a self-value-driven supervisory system, according to the classification made by Xu et al. (2017), that can process and verify the functionalities and applicability of our framework in manufacturing processes. Moreover, the self-supervising system developed in this thesis project is compared to other research works.
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Manuel Sanchez. Autonomic Process Management for Industry 4.0. Artificial Intelligence [cs.AI]. Université de Pau et des Pays de l’Adour; Universidad de los Andes - Mérida (Venezuela), 2020. English. ⟨tel-02956888⟩

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