Diseño de un Modelo Predictivo en el Contexto Industria 4.0

The Internet of Things (IoT), the development and installation of advanced sensors for data collection, computer solutions for remote connection and other disruptive technologies are marking a transformation process in the industry; giving rise to what various sectors have called the fourth industrial revolution or Industry 4.0. With this process of change, organizations face both new opportunities and challenges. This article focuses on the modeling and integration of industrial data, generated by sensors installed in machines. The extraction of patterns is proposed, using data fusion techniques that allow the design of a predictive maintenance model. Finally, a case study is presented with a database that is applied to the Naive Bayes Algorithm to obtain predictions.Keywords: Industry 4.0, Sensors, Internet of Things, Pattern Extraction, Omnibus Models.

Extracción de patrones para la Industria 4.0 a través de un modelo predictivo

The Internet of Things has been incorporated into our lives progressively, bringing with it great benefits for humanity such as having more interconnected and efficient infrastructures and services, generating employment, reducing operating costs and increasing profits. In this sense, the development and installation of advanced sensors for data collection, remote connection computing solutions and other disruptive technologies are marking a process of transformation in the industry; giving rise to what several sectors have called the fourth industrial revolution or Industry 4.0. This article presents a predictive model for the extraction of patterns using data fusion techniques that allow the design of a predictive maintenance model, which allows using a supervised training approach, perform data classification and probabilistically predictive values.El Internet de las Cosas ha venido incorporándose a nuestras vidas de forma progresiva, trayendo consigo grandes beneficios para la humanidad como lo es disponer de infraestructuras y servicios más interconectados y eficientes, generación de empleo, reducción de costos operativos e incremento de ganancias. En este sentido, el desarrollo e instalación de sensores avanzados para recolección de datos, las soluciones informáticas de conexión remota y otras tecnologías disruptivas están marcando un proceso de transformación en la industria; dando inicio a lo que diversos sectores han denominado cuarta revolución industrial o Industria 4.0. En este artículo se presenta un modelo predictivo para la extracción de patrones utilizando técnicas de fusión de datos que permitan el diseño de un modelo de mantenimiento predictivo, a través de un enfoque de entrenamiento supervisado, realizar la clasificación de datos y probabilísticamente valores predictivos

Arquitecturas de Referencia Edge Computing para la Industria 4.0: una revisión

Investigaciones recientes intentan demostrar que las arquitecturas de Edge Computing representan soluciones óptimas para minimizar la latencia, mejorar la privacidad y reducir el ancho de banda y los costos relacionados con los desarrollos para escenarios basados en el Internet de las Cosas (IoT), tales como: ciudades inteligentes, consumo eficiente de energía, agricultura inteligente o industria 4.0. Este trabajo es una revisión de las principales arquitecturas de referencia existentes de Edge Computing dirigidas a la Industria 4.0 propuestas por el Edge Computing Consortium, el Proyecto FAR-Edge Project, el Industrial Internet Consortium y finalmente por la asociación entre INTEL y SAP. Este trabajo incluye una comparación entre estas arquitecturas de referencia, así como sus características más importantes con el objetivo de proponer como trabajo futuro, el diseño de una Arquitectura de Referencia de Edge Computing aplicable a otros escenarios que no necesariamente estén relacionados con la industria 4.0

A Hybrid System For Pandemic Evolution Prediction

The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures./n/n/n/n/n/

A Hybrid System For Pandemic Evolution Prediction

The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures

Arquitecturas de Referencia Edge Computing para la Industria 4.0: una revisión

Investigaciones recientes intentan demostrar que las arquitecturas de Edge Computing representan soluciones óptimas para minimizar la latencia, mejorar la privacidad y reducir el ancho de banda y los costos relacionados con los desarrollos para escenarios basados en el Internet de las Cosas (IoT), tales como: ciudades inteligentes, consumo eficiente de energía, agricultura inteligente o industria 4.0. Este trabajo es una revisión de las principales arquitecturas de referencia existentes de Edge Computing dirigidas a la Industria 4.0 propuestas por el Edge Computing Consortium, el Proyecto FAR-Edge Project, el Industrial Internet Consortium y finalmente por la asociación entre INTEL y SAP. Este trabajo incluye una comparación entre estas arquitecturas de referencia, así como sus características más importantes con el objetivo de proponer como trabajo futuro, el diseño de una Arquitectura de Referencia de Edge Computing aplicable a otros escenarios que no necesariamente estén relacionados con la industria 4.0

Edge Computing, IoT and Social Computing in Smart Energy Scenarios

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller