Guidelines to develop demonstration models on industry 4.0 for engineering training

[EN] Industrial digitization is currently a great challenge which involves continuous advances in tech-nologies such as automation, robotics, internet of things, cloud computing, big data, virtual and augmented reality or cybersecurity. Only those companies able to adapt and with qualified work-ers will be competitive. Therefore, it is necessary to design new environments to train students and workers in these enabling technologies. In this paper, a set of guidelines is proposed to develop a demonstration model on Industry 4.0. Following these guidelines, an existing manufacturing industrial system, based on an electro-pneumatic cell for classifying pieces, is updated to the Industry 4.0 paradigm. The result is an Industry 4.0 demonstration model where enabling tech-nologies are added in an integrated way. In this manner, students do not only train in each technology, but also understand the interactions between them. In the academic year 2020/21, this demonstration model has been used by engineering students in a subject of a master’s degree. Impressions and comments from students about the structure and management of the environ-ment, as well as the influence on their learning process are collected and discussed.SIThis work was supported by the Spanish State Research Agency, MCIN/AEI/10.13039/501100011033 under Grant PID2020-117890RB-I00. The work of José Ramón Rodríguez- Ossorio was supported by a grant of the Research Programme of the Universidad de León 2020. The work of Guzmán González-Mateos was supported by a grant of the Research Programme of the University of León 202

Environment for Education on Industry 4.0

[EN] A new industrial production model based on digitalization, system interconnection, virtualization and data exploitation, has emerged. Upgrade of production processes towards this Industry 4.0 model is one of the critical challenges for the industrial sector and, consequently, the training of students and professionals has to address these new demands. To carry out this task, it is essential to develop educational tools that allow students to interact with real equipment that implements, in an integrated way, new enabling technologies, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins and industrial cybersecurity measures. For that reason, in this work, we present an educational environment on Industry 4.0 that incorporates these technologies reproducing realistic industrial conditions. This environment includes cutting-edge industrial control system technologies, such as an industrial firewall and a virtual private network (VPN) to strengthen cybersecurity, an Industrial Internet of Things (IIoT) gateway to transfer process information to the cloud, where it can be stored and analyzed, and a digital twin that virtually reproduces the system. A set of hands-on tasks for an introductory automation course have been proposed, so that students acquire a practical understanding of the enabling technologies of Industry 4.0 and of its function in a real automation. This course has been taught in a master’s degree and students have assessed its usefulness by means of an anonymous survey. The results of the educational experience have been useful both from the students’ and faculty’s viewpoint.SIAgencia estatal de investigación MCIN/AEI/ 10.13039/501100011033Comité español de Automática y Siemens a través del premio ‘Automatización y Digitalización. Industria 4.0

Flow virtual sensor based on deep learning techniques

[ES] En el contexto de la digitalización de la industria, los sensores virtuales resultan muy útiles tanto para construir gemelos digitales, que permiten simular comportamientos que ayudan a optimizar el proceso productivo y prevenir errores, como para ser utilizados en las situaciones en las que un sensor real es muy costoso o directamente no puede ser instalado. En este artículo se propone un método para implementar sensores virtuales utilizando tres de las técnicas de deep learning más comunes: redes convolucionales, redes neuronales densas y redes recurrentes. El método se ha utilizado para construir un sensor virtual de caudal en una maqueta de control de procesos que dispone de instrumentación industrial real.[EN] In the context of industry digitalization, virtual sensors are very useful both to develop digital twins, which simulate behaviors that help us to optimize the process and prevent faults, such as to be used on the cases where a real sensor is very expensive or cannot be installed. In this paper, it is proposed a method to develop virtual sensors using three of the most common deep learning techniques: convolutional networks, dense neural networks and recurrent neural networks. The method has been used to develop a flow virtual sensor for a pilot plant that has real industrial instrumentation

Digital twin of an electro-pneumatic classification cell

[Resumen] Actualmente se está produciendo una transformaci´on digital en la industria gracias a la incorporaci´on de diversas tecnologías habilitadoras como la automatización, robótica, computación en la nube, ciberseguridad industrial, integración de sistemas o gemelos digitales, entre otras. Esta última está generando un gran interés por el valor añadido que supone incorporar simulaciones realistas y completamente operativas de los procesos. En este trabajo, se propone una metodología para desarrollar gemelos digitales, en los que se incorporan, además, otras tecnologías habilitadoras como la integración de sistemas o la computación en la nube. Adicionalmente, se presenta una aplicación desarrollada con Unity3D donde se emplea dicha metodología para obtener un gemelo digital de una célula electro-neumática robotizada.[Abstract] A digital transformation is currently taking place in industry thanks to the incorporation of various enabling technologies, such as automation, robotics, cloud computing, industrial cybersecurity, systems integration or digital twins, among others. The latter is generating great interest due to the added value of incorporating realistic and fully operational simulations of the processes. This paper proposes a methodology for developing digital twins, which also incorporates other enabling technologies such as systems integration or cloud computing. In addition, an application developed with Unity3D is presented where this methodology is used to obtain a digital twin of a robotic electro-pneumatic cell.Ministerio de Ciencia e Innovación; PID2020-117890RBI0

Flow control with a virtual sensor based on deep learning techniques

[Resumen] En el ámbito del control industrial, resulta sumamente interesante el empleo de sensores virtuales en los casos en los que no se puede tener acceso a la variable física a controlar, bien sea porque el sensor real es costoso, no puede ser instalado o se encuentra averiado. En este artículo se propone la implementación de un lazo de control del caudal de una planta piloto industrial, comparando el desempeño de este lazo cuando se emplea el caudalímetro real y cuando se emplea un sensor virtual de caudal. El sensor virtual utilizado se ha desarrollado empleando una red neuronal recurrente.[Abstract] In the field of industrial control, the use of virtual sensors has great interest in cases where it is not possible to have access to the physical variable to be controlled, either because the real sensor is expensive, cannot be installed or is broken-down. This paper proposes the implementation of a flow control loop in an industrial pilot plant, comparing the performance of this loop when the real flow meter is used with the performance when a virtual flow sensor is used. The virtual sensor used has been developed using a recurrent neural network.Esta publicación es parte de proyecto PID2020-117890RB-I00, financiado por MCIN/AEI/10.13039/501100011033/. El trabajo de José Ramón Rodríguez-Ossorio y de Guzmán González-Mateos ha sido financiado por un beca del Programa de Investigación de la Universidad de León.Ministerio de Ciencia e Innovación; 10.13039/50110001103

Demostradores para la formación en digitalización de la industria

Accésit 2022[ES] Los procesos de digitalización en la industria son actualmente un enorme reto dentro de la Unión Europea que implica continuos cambios no solo en las tecnologías industriales que se utilizan, sino que también en los procesos de aprendizaje, tanto para estudiantes como para trabajadores