An Edge-Cloud based Reference Architecture to support cognitive solutions in Process Industry

Process Industry is one of the leading sectors of the world economy, characterized however by intense environmental impact, and very high-energy consumption. Despite a traditional low innovation pace in PI, in the recent years a strong push at worldwide level towards the dual objective of improving the efficiency of plants and the quality of products, significantly reducing the consumption of electricity and CO2 emissions has taken momentum. Digital Technologies (namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies) are enabling drivers for a Twin Digital-Green Transition, as well as foundations for human centric, safe, comfortable and inclusive workplaces. Currently, digital sensors in plants produce a large amount of data, which in most cases constitutes just a potential and not a real value for Process Industry, often locked-in in close proprietary systems and seldomly exploited. Digital technologies, with process modelling-simulation via digital twins, can build a bridge between the physical and the virtual worlds, bringing innovation with great efficiency and drastic reduction of waste. In accordance with the guidelines of Industrie 4.0 this work proposes a modular and scalable Reference Architecture, based on open source software, which can be implemented both in brownfield and greenfield scenarios. The ability to distribute processing between the edge, where the data have been created, and the cloud, where the greatest computational resources are available, facilitates the development of integrated digital solutions with cognitive capabilities. The reference architecture is being validated in the three pilot plants, paving the way to the development of integrated planning solutions, with scheduling and control of the plants, optimizing the efficiency and reliability of the supply chain, and balancing energy efficiency

Engineering framework for service-oriented automation systems

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

Semantic-guided predictive modeling and relational learning within industrial knowledge graphs

The ubiquitous availability of data in today’s manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from different production domains requires high manual effort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in different domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a unified semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simplification and abstraction of statistical analysis and predictive modeling, especially in manufacturing. In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports efficient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms. In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm. Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment. We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation. They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

A holonic manufacturing architecture for line-less mobile assembly systems operations planning and control

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2022.O Line-Less Mobile Assembly Systems (LMAS) é um paradigma de fabricação que visa maximizar a resposta às tendências do mercado através de configurações adaptáveis de fábrica utilizando recursos de montagem móvel. Tais sistemas podem ser caracterizados como holonic manufacturing systems (HMS), cujas chamadas holonic control architecture (HCA) são recentemente retratadas como abordagens habilitadoras da Indústria 4.0 devido a suas relações de entidades temporárias (hierárquicas e/ou heterárquicas). Embora as estruturas de referência HCA como PROSA ou ADACOR/ADACOR² tenham sido muito discutidas na literatura, nenhuma delas pode ser aplicada diretamente ao contexto LMAS. Assim, esta dissertação visa responder à pergunta \"Como uma arquitetura de produção e sistema de controle LMAS precisa ser projetada?\" apresentando os modelos de projeto de arquitetura desenvolvidos de acordo com as etapas da metodologia para desenvolvimento de sistemas holônicos multi-agentes ANEMONA. A fase de análise da ANEMONA resulta em uma especificação do caso de uso, requisitos, objetivos do sistema, simplificações e suposições. A fase de projeto resulta nos modelos de organização, interação e agentes, seguido de uma breve análise de sua cobertura comportamental. O resultado da fase de implementação é um protótipo (realizado com o Robot Operation System) que implementa os modelos ANEMONA e uma ontologia LMAS, que reutiliza elementos de ontologias de referência do domínio de manufatura. A fim de testar o protótipo, um algoritmo para geração de dados para teste baseado na complexidade dos produtos e na flexibilidade do chão de fábrica é apresentado. A validação qualitativa dos modelos HCA é baseada em como o HCA proposto atende a critérios específicos para avaliar sistemas HCA. A validação é complementada por uma análise quantitativa considerando o comportamento dos modelos implementados durante a execução normal e a execução interrompida (e.g. equipamento defeituoso) em um ambiente simulado. A validação da execução normal concentra-se no desvio de tempo entre as agendas planejadas e executadas, o que provou ser em média irrelevante dentro do caso simulado considerando a ordem de magnitude das operações típicas demandadas. Posteriormente, durante a execução do caso interrompido, o sistema é testado sob a simulação de uma falha, onde duas estratégias são aplicadas, LOCAL\_FIX e REORGANIZATION, e seu resultado é comparado para decidir qual é a opção apropriada quando o objetivo é reduzir o tempo total de execução. Finalmente, é apresentada uma análise sobre a cobertura desta dissertação culminando em diretrizes que podem ser vistas como uma resposta possível (entre muitas outras) para a questão de pesquisa apresentada. Além disso, são apresentados pontos fortes e fracos dos modelos desenvolvidos, e possíveis melhorias e idéias para futuras contribuições para a implementação de sistemas de controle holônico para LMAS.Abstract: The Line-Less Mobile Assembly Systems (LMAS) is a manufacturing paradigm aiming to maximize responsiveness to market trends (product-individualization and ever-shortening product lifecycles) by adaptive factory configurations utilizing mobile assembly resources. Such responsive systems can be characterized as holonic manufacturing systems (HMS), whose so-called holonic control architectures (HCA) are recently portrayed as Industry 4.0-enabling approaches due to their mixed-hierarchical and -heterarchical temporary entity relationships. They are particularly suitable for distributed and flexible systems as the Line-Less Mobile Assembly or Matrix-Production, as they meet reconfigurability capabilities. Though HCA reference structures as PROSA or ADACOR/ADACOR² have been heavily discussed in the literature, neither can directly be applied to the LMAS context. Methodologies such as ANEMONA provide guidelines and best practices for the development of holonic multi-agent systems. Accordingly, this dissertation aims to answer the question \"How does an LMAS production and control system architecture need to be designed?\" presenting the architecture design models developed according to the steps of the ANEMONA methodology. The ANEMONA analysis phase results in a use case specification, requirements, system goals, simplifications, and assumptions. The design phase results in an LMAS architecture design consisting of the organization, interaction, and agent models followed by a brief analysis of its behavioral coverage. The implementation phase result is an LMAS ontology, which reuses elements from the widespread manufacturing domain ontologies MAnufacturing's Semantics Ontology (MASON) and Manufacturing Resource Capability Ontology (MaRCO) enriched with essential holonic concepts. The architecture approach and ontology are implemented using the Robot Operating System (ROS) robotic framework. In order to create test data sets validation, an algorithm for test generation based on the complexity of products and the shopfloor flexibility is presented considering a maximum number of operations per work station and the maximum number of simultaneous stations. The validation phase presents a two-folded validation: qualitative and quantitative. The qualitative validation of the HCA models is based on how the proposed HCA attends specific criteria for evaluating HCA systems (e.g., modularity, integrability, diagnosability, fault tolerance, distributability, developer training requirements). The validation is complemented by a quantitative analysis considering the behavior of the implemented models during the normal execution and disrupted execution (e.g.; defective equipment) in a simulated environment (in the form of a software prototype). The normal execution validation focuses on the time drift between the planned and executed schedules, which has proved to be irrelevant within the simulated case considering the order of magnitude of the typical demanded operations. Subsequently, during the disrupted case execution, the system is tested under the simulation of a failure, where two strategies are applied, LOCAL\_FIX and REORGANIZATION, and their outcome is compared to decide which one is the appropriate option when the goal is to reduce the overall execution time. Ultimately, it is presented an analysis about the coverage of this dissertation culminating into guidelines that can be seen as one possible answer (among many others) for the presented research question. Furthermore, strong and weak points of the developed models are presented, and possible improvements and ideas for future contributions towards the implementation of holonic control systems for LMAS

Smart manufacturing: role of Internet of Things in process optimization

This research is primarily focused on process optimization in manufacturing field in business-to-business context. The study is an effort to point out the issues manufacturers face at their shop floor and it provides solutions for dealing with those issues. During the last decade the Internet of Things (IoT) has gained a lot of attention from both academia and practitioners. IoT emphasizes on the importance of physical objects transferring information by using both software and the Internet. Based on the global trends, nowadays, there is a clear requirement for companies to focus on how they can implement IoT in order to facilitate their businesses and create new business and market opportunities. IoT is able to connect various things and objects around us which are able to interact with each other. In other words, IoT technologies not only connect a particular industrial system or supply chain, but also connects stakeholders and end-customers. The goal of the thesis is to discuss IoT technologies and elaborate on how they are implemented in manufacturing processes. One empirical case study on IoT applications in shop floors and production lines carried out. Two cases were selected based on being a pioneer in implementing IoT technologies into manufacturing and highly optimized production at targeted factories. The cases represent next generation of smart factories which IoT technologies and in particular RFID solutions play an important role. A qualitative document analysis was conducted. The topic of this research is relatively new and therefore majority of references used for this paper are from 2014 onwards. Data were collected from public, non-confidential information sources including press releases, newspapers, articles and journals. The research approach was primarily descriptive with the focus on differences between previous production optimization technologies and IoT applications in use today. The results of thesis demonstrates that IoT technologies bring transparency, traceability, adaptability, scalability and flexibility to the system. Therefore, the adoption of IoT has quite a few potential benefits, including improvement in cost and risk reduction, operational processes and value creation. This research also shows that using IoT technologies for their benefits is not an easy task for enterprises. Companies face many challenges on the way including layout changes in the factory’s shop floor, changes in the design of the products, security concerns and consumer privacy. Moreover, since the IoT is a recent development, different aspects of the IoT such as economical, managerial and industrial aspects need to be studied. And this makes companies hesitant to make decisions regarding the adoption of IoT

Factory Radio Design of a 5G Network in Offline Mode

The manufacturing industry is connecting people and equipment with new digital technologies, enabling a more continuous stream of data to represent processes. With more things connected, the interest in a connectivity solution that can support communication with high reliability and availability will increase. The fifth generation of telecommunication, i.e., 5G has promising features to deliver this, but the factory environment introduces new challenges to ensure reliable radio coverage. This will require efficient ways to plan the Factory Radio Design prior to installation. 3D laser scanning is used at an ever-increasing rate for capturing the spatial geometry in a virtual representation to perform layout planning of factories. This paper presents how to combine 3D laser scanning and physical optics (PO) for planning the Factory Radio Design of a cellular Long-Term Evolution (LTE) network (5G) in a virtual environment. 3D laser scanning is applied to obtain the spatial data of the factory and the virtual representation serves as the environment where PO computation techniques can be performed. The simulation result is validated in this paper by comparison to measurements of the installed network and empirical propagation models. The results of the study show promising opportunities to simulate the radio coverage in a virtual representation of a factory environment

Industrial internet and its role in process automation

Modern process automation undergoes a major shift in the way it addresses conventional challenges. Moreover, it is adapting to the newly arising challenges due to changing business scenarios. Nowadays, the areas of the automation that recently were rather separate start to merge and the border between them is fading. This situation only adds struggle to the already highly competitive production industry. In order to be successful, companies should adopt new approaches to the way their processes are automated, controlled, and managed. One of these approaches is the so-called Industrial Internet. It is the next step after the traditional paradigm of the process automation pyramid that leads to the new vision of interconnected processes, services, machines and people. However, general company does not usually eager to implement the new technology to its business. One of the reasons for this is that it does not see the advantages that the Industrial Internet brings. This is due to the lack of sufficient number of successful implementation examples in various industrial areas and of clear business scenarios for the use of the Industrial Internet. Aim of the presented thesis is to create a convincing Industrial Internet application scenario. For the implementation, a mineral concentration plant was chosen as one of the industrial premises that possesses the shortage of the Industrial Internet examples. Literature review section describes the process automation state of art. It lists and reviews the research and development initiatives related to the Industrial Internet. Moreover, the Industrial Internet fundamentals are given. Finally, it describes the Industrial Internet applications and the case studies. In the practical part, at first, the description of the mineral concentration plant is given. Then, the next section describes the Industrial Internet application scenario. In the following section technical guidelines for the system implementation are given. Also, in the concluding part of the thesis the future direction of research work are discussed

Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen