Towards Logistics 4.0: A Skill-Based OPC UA Communication between WMS and the PLC of an Automated Storage and Retrieval System

In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection of manufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UA-based approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation

Development of machine learning strategies for fault diagnosis in virtual plants (Digital Twin)

En aquest projecte, s’ha validat la possibilitat de realitzar la monitorització de dades i el diagnòstic d’errors en línia (mentre s’executa la simulació) d’una planta química simulada (Digital Twin, en Anglès). La simulació es troba funcionant a un ordinador remot, mentre que s’accedeixen als resultats de la monitorització de dades i el diagnòstic d’errors per mitjà de l’accés, amb un ordinador personal, al núvol, més conegut com a ‘Cloud’ pel seu terme en Anglès. En primer lloc, s’explica la implementació, mòdul a mòdul, del prototipus modular proposat i emprat per a l’intercanvi d’informació des del ‘Digital Twin’ cap al núvol, el qual permet la monitorització de dades. Per a cada mòdul, s’introdueixen els programes o eines de programació necessaris per a la creació i/o execució. Les raons considerades alhora d’escollir aquests programes o eines de programació també s’exposen. A més a més, s’introdueix la plataforma on s’allotja el núvol junt amb els diferents servicis que ofereix el núvol, els quals han sigut utilitzats per a mostrar els resultats de la monitorització de dades. En segon lloc, els algorismes d’aprenentatge automàtic (Machine Learning en Anglès) i d’anàlisi de dades (Data Analysis en Anglès), que han sigut implementats pel diagnòstic d’errors en la planta simulada, són comentats des dels punts de vista teòric i d’implementació. També, s’explica el desenvolupament de les eines de monitorització per a la diagnosi d’errors, les quals són el resultat de combinar els algorismes anteriors amb el prototipus modular encarregat de l’intercanvi d’informació. Finalment, es documenta una prova de concepte del prototipus global que permet demostrar que aquestes tecnologies son factibles i fiables per a la realització de la monitorització de dades i el diagnòstic d’errors. Addicionalment, s’inclouen pautes a seguir per a millorar el prototipus.En este proyecto, se ha validado la posibilidad de realizar la monitorización de datos y el diagnóstico de errores en línea (mientras se ejecuta la simulación) de una planta química simulada (Digital Twin en Inglés). La simulación se encuentra funcionando en un ordenador remoto, mientras que se accede a los resultados de la monitorización de datos y el diagnóstico de errores por medio del acceso, con un ordenador personal, a la nube, más conocida como ‘Cloud’ por su término en Inglés. En primer lugar, se explica la implementación, módulo a módulo, del prototipo modular propuesto y empleado para el intercambio de información desde el ‘Digital Twin’ hacia la nube (Cloud), lo que permite la monitorización de datos. Para cada módulo, se introducen los programas y herramientas de programación necesarios para crear y/o ejecutar el módulo. Las razones para seleccionar los programas y las herramientas también son expuestas. Además, se introduce la plataforma donde se aloja la nube empleada junto con los diferentes servicios disponibles en la nube, los cuales se han usado para mostrar los resultados de la monitorización de datos. En segundo lugar, los algoritmos de aprendizaje automático (Machine Learning en Inglés) y de análisis de datos (Data Analysis en Inglés), implementados para el diagnóstico de fallos, se comentan desde los puntos de vista teórico y de implementación. Además, se explica el desarrollo de las herramientas de monitorización para el diagnóstico de fallos, que consiste en la combinación de los anteriores algoritmos con el prototipo modular encargado del intercambio de información. Finalmente, se documenta una prueba de concepto del prototipo en global, que demuestra que estas tecnologías son factibles y fiables para la monitorización de datos y el diagnóstico de fallos. Adicionalmente, se incluyen unas pautas a seguir para mejorar el prototipo.In this project, the possibility of performing the on-line data monitoring and fault diagnosis over a simulated chemical plant (Digital Twin) has been validated, which is running on a remote computer, by accessing the Cloud with a personal computer. Firstly, the implementation is explained, module by module, of the modular prototype proposed and employed for the exchange of information from the Digital Twin to the Cloud, which enables the data monitoring. For each of the modules, the programs or programming tools required for its creation and/or execution are introduced. The reasons for its selection are also exposed when explaining each of the modules. Moreover, the Cloud Platform chosen is also introduced together with the different services associated with it that have been used for displaying the results from data monitoring. In the second place, the Machine Learning and Data Analysis algorithms implemented for the fault diagnosis are commented from the theoretical and the implementation points of view. Furthermore, the development of monitoring tools for fault diagnosis is also explained, which consists of the coupling between the algorithms and the modular prototype for the exchange of information. Finally, it is documented a proof of concept of the global prototype, which demonstrates the feasibility and reliability of these technologies for performing data monitoring and fault diagnosis. Additionally, guidelines for the further development of the prototype are provided

Evaluation of Containerized Simulation Software in Docker Swarm and Kubernetes

The modern industrial systems are large and complex so that a new simulation method, cooperative simulation or co-simulation, is used to simulate sub-models of a whole system. A large and complex system will be divided into several smaller subsystems, and these smaller systems will be modeled and simulated by multiple cooperative simulators. This simulation method enables the simulation process to be efficient and can provide many advantages, such as viewing results in real-time, consuming resources efficiently, and providing more accurate results than simulating the whole large and complex system. Besides the co-simulation method, this thesis also introduces the Docker container technology, a container virtualization tool used to build and pull images, run containers, and orchestrate containers. Another container orchestrating tool, Kubernetes, is also used in the experiment for managing pods and containers. This thesis discusses the possibility of containerizing simulation software in Docker and uses Docker swarm and Kubernetes to orchestrate cooperative simulation containers. A co-simulation platform is created in a Docker swarm cluster and Kubernetes cluster, where multiple simulation containers are running cooperatively by receiving commands from the co-simulation platform. The experiment results prove that the co-simulation platform is working as expected, and that multiple cooperative simulation containers have better performance than running a standalone complex simulation process

Koneoppimiskehys OPC UA datalle (Industry 4.0)

Machine learning has rapidly gained popularity in all industries with the increase of computational power and data gathering capabilities. Process industry is a good candidate for machine learning based modeling due to the large amounts of data gathered and need for accurate process state predictions. In this work the viability of combining the OPC UA protocol with existing open source machine learning libraries to create data driven models and generate real time predictions was studied. Scikit-learn was used to generate soft sensor style models for the butane content of a debutanizer column output. The data for offline model training was dynamically fetched from an OCP UA server and with a trained model predictions could be generated in real time. The accuracy of the generated models needs to be further researched with better methodology and larger datasets.Koneoppiminen on kasvattanut suosiotaan nopeasti kaikilla toimialoilla laskentatehon ja datankeruun kasvaessa. Prosessiteollisuus on hyvä kandidaatti koneoppimispohjaiselle mallinnukselle suurien datamäärien sekä vaadittujen tarkkojen prosessimallien takia. Tässä työssä tutkittiin mahdollisuutta OPC UA protokollan yhdistämistä olemassaolevien avoimen lähdekoodin koneoppimiskirjastojen kanssa mittausdataan perustuvien mallien opettamiseksi ja reaaliaikaisten ennusteiden luomiseksi. Scikit-learn kirjastoa käytettiin luomaan malleja butaaninpoistokolonnin ulostulon butaanipitoisuuden ennustamiseen. Data mallien offline opetukseen ladattiin dynaamisesti OPC UA palvelimelta ja valmiiksi opetetulla mallilla ennusteita voitiin generoida reaaliaikaisesti. Luotujen mallien tarkkuutta täytyy tutkia tarkemmin paremmalla metodologialla ja suuremmilla datamäärillä

Digital Transformation in the Ornamental Stone Industry: Case Studies on Industry 4.0 and Digital Twins

Funding program "LISBOA-01-0247-FEDER-046083" for this R&D scholarship.The rapid evolution of Industry 4.0 technologies has ushered in a new era in manufacturing systems, with Digital Twins leading the way. These virtual replicas offer invaluable opportunities for simulating and optimizing new manufacturing processes, and their most transformative impact may lie in the creation of these digital models. This research unifies the main key concepts of four separate studies, all of which explore the application of Digital Twins in the ornamental stone industry. Industry 4.0 systems and their technologies have directly influenced the ornamental stone industry, addressing both the effects on mineral resources and energy consumption in daily operations. In addition, research and development initiatives seek to make this industry more efficient and sustainable, addressing crucial issues such as economic growth, environmental impact, and social welfare. The increasing digitization of manufacturing systems and their integration with digital models has played a key role in this process, enabling the replication of shop floor operations and the optimization of material use. The application of Digital Twins, which are virtual replicas of physical systems, has been explored in an ornamental stone manufacturing company. These digital models have demonstrated the ability to save time and resources during prototype design, as well as offering continuous diagnostics and optimization throughout production. It is important to note that the implementation of Digital Twins requires care due to technical challenges, but their adoption promises to significantly impact business value, despite the initial complexities. Managing stone cutting devices with Digital Twins presents real challenges in the ornamental stone industry, but it also paves the way for greater precision, efficiency, and cost savings. These digital models enable real-time monitoring, predictive maintenance, and virtual simulations. This study explores different approaches to connecting physical cutting machines to their respective Digital Twins, evaluating criteria such as communication speed, security, scalability, and cost. The results of this analysis provide valuable information for implementing Digital Twins in the stone cutting industry

Data science on industrial data -- Today's challenges in brown field applications

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls

A Novel Design of Vitual and Mixed Reality Scenarios for Automation Training

A thesis presented to the faculty of the College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Andrés Salinas-Hernández on April 23, 2021