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

DIN Spec 91345 RAMI 4.0 compliant data pipelining: An approach to support data understanding and data acquisition in smart manufacturing environments

Today, data scientists in the manufacturing domain are confronted with a set of challenges associated to data acquisition as well as data processing including the extraction of valuable in-formation to support both, the work of the manufacturing equipment as well as the manufacturing processes behind it. One essential aspect related to data acquisition is the pipelining, including various commu-nication standards, protocols and technologies to save and transfer heterogenous data. These circumstances make it hard to understand, find, access and extract data from the sources depend-ing on use cases and applications. In order to support this data pipelining process, this thesis proposes the use of the semantic model. The selected semantic model should be able to describe smart manufacturing assets them-selves as well as to access their data along their life-cycle. As a matter of fact, there are many research contributions in smart manufacturing, which already came out with reference architectures or standards for semantic-based meta data descrip-tion or asset classification. This research builds upon these outcomes and introduces a novel se-mantic model-based data pipelining approach using as a basis the Reference Architecture Model for Industry 4.0 (RAMI 4.0).Hoje em dia, os cientistas de dados no domínio da manufatura são confrontados com várias normas, protocolos e tecnologias de comunicação para gravar, processar e transferir vários tipos de dados. Estas circunstâncias tornam difícil compreender, encontrar, aceder e extrair dados necessários para aplicações dependentes de casos de utilização, desde os equipamentos aos respectivos processos de manufatura. Um aspecto essencial poderia ser um processo de canalisação de dados incluindo vários normas de comunicação, protocolos e tecnologias para gravar e transferir dados. Uma solução para suporte deste processo, proposto por esta tese, é a aplicação de um modelo semântico que descreva os próprios recursos de manufactura inteligente e o acesso aos seus dados ao longo do seu ciclo de vida. Muitas das contribuições de investigação em manufatura inteligente já produziram arquitecturas de referência como a RAMI 4.0 ou normas para a descrição semântica de meta dados ou classificação de recursos. Esta investigação baseia-se nestas fontes externas e introduz um novo modelo semântico baseado no Modelo de Arquitectura de Referência para Indústria 4.0 (RAMI 4.0), em conformidade com a abordagem de canalisação de dados no domínio da produção inteligente como caso exemplar de utilização para permitir uma fácil exploração, compreensão, descoberta, selecção e extracção de dados

An Architecture-based Approach for Change Impact Analysis of Software-intensive Systems

A main property of software-intensive technical systems is sustainability. Sustainable systems need to change continuously. A change to a system element can result in further changes to other system elements. If these elements originate from different domains, the change can also propagate between several domains. This book presents an architecture-based approach to change propagation analysis of software-intensive technical systems that considers heterogeneous elements from different domain

Mensch und Technik in der digitalen Transformation

Die digitale Transformation gilt als zentraler gesellschaftlicher Megatrend. Unklar ist jedoch, welche Veränderungen konkreter Arbeitsmerkmale mit der Digitalisierung einhergehen und welche Bedingungen für Beschäftigte belastend oder unterstützend wirken können. Darüber hinaus wird die Technikakzeptanz aufseiten der Beschäftigten bisher kaum empirisch adressiert. Hier knüpft die vorliegende Forschung an und verfolgt folgende Forschungsfrage: Wie bewerten Beschäftigte die Veränderungen am Arbeitsplatz, die mit der Digitalisierung einhergehen in Bezug auf die Förderung und Belastung ihrer Arbeitsfähigkeit und welche Bedeutung hat dabei die Bewertung der digitalen Technik? Die gewonnenen Erkenntnisse zeigen deutlich die Effekte der Digitalisierung auf: Über die Veränderung der Arbeitsbedingungen steigert die Digitalisierung die Arbeitszufriedneheit und die Motivation. Gleichzeitig werden höhere Anforderungen zur Komplexitätsbewältigung und Leistungsdruck erzeugt. Die positiven und negativen Effekte der Digitalisierung gleichen sich aus. Voraussetzung dazu ist jedoch eine adäquate Gestaltung des soziotechnischen Systems. Der Vergleich zwischen den beiden Fallbeispielen Logistik und IT deutet zudem darauf hin, dass sich die Gesamteffekte der Digitalisierung verstärken, wenn die digitale Transformation voranschreitet

Kennzahlenbasierte Steuerung, Koordination und Aktionsplanung in Multiagentensystemen

To be of practical use, the implementation of flexible and modular agent-based cyber-physical systems (CPS) for real-world autonomous control applications in Industry 4.0 oftentimes requires the domain-specific software agents to adhere to the organization's overall qualitative and quantitative business goals, usually expressed in terms of numeric key performance indicators (KPI). In this thesis, a general software framework for multi-agent systems (MAS) and CPS is developed that facilitates the integration and configuration of KPI-related objectives into the agents' individual decision processes. It allows the user of an agent system to define new KPIs and associated multi-criteria goals and supports inter-agent coordination as well as detailed KPI-based action planning, all at runtime of the MAS. The domain-independent components of the proposed KPI framework are implemented as a Java programming library and evaluated in a simulated production planning and control scenario