Search

In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector's requirements. To better describe these requirements, base use cases of industrial transfer learning are introduced., 12 pages, 4 figures, 2 tables. Accepted for publication by IEEE Industrial Electronics Magazine

The utilization of deep learning in the field of industrial automation is hindered by two factors: The amount and diversity of training data needed as well as the need to continuously retrain as the use case changes over time. Both problems can be addressed by industrial deep transfer learning allowing for the performant, continuous and potentially distributed training on small, dispersed datasets. As a specific example, a dual memory algorithm for computer vision problems is developed and evaluated. It shows the potential for state-of-the-art performance while being trained only on fractions of the complete ImageNet dataset at multiple locations at once.

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear time-variant system dynamics. The present paper delivers a comprehensive overview of existing LSTM cell derivatives and network architectures for time series prediction. A categorization in LSTM with optimized cell state representations and LSTM with interacting cell states is proposed. The investigated approaches are evaluated against defined requirements being relevant for an accurate time series prediction. These include short-term and long-term memory behavior, the ability for multimodal and multi-step ahead predictions and the according error propagation. Sequence-to-sequence networks with partially conditioning outperform the other approaches, such as bidirectional or associative networks, and are best suited to fulfill the requirements.

The system development process is becoming increasingly complex as the systems themselves become more and more complex. At the same time, the various disciplines such as mechanical engineering, electrical engineering and software engineering are becoming increasingly intermixed, so that companies in one discipline are experiencing leaps and bounds in the complexity of their systems and their development. For this reason, this publication presents a concept of a virtual assistant that leads through a development phase. It shows that the software supporting the development needs an information model to store the data of the developed system and to connect it with the existing knowledge. This knowledge can either be available internally or on the web. The development process should therefore support cooperation so that the assistance software and engineers interact with each other.

Future automation systems, operating under increasing dynamic conditions need to be equipped with methods to enhance their adaptability accordingly. Context-aware systems model and apply contextual parameters influencing the interpretation of situations to adapt their operation accordingly. Designing context-aware automation systems requires methods to acquire and process heterogeneous and distributed data in a dynamic and continuous manner. In this contribution, a methodology for processing multimodal data in a flexible and reusable context-model is demonstrated, using concepts of data aggregation and integration, semantic labeling and graph modeling. Based on this approach, the use case of a context-aware medication assistance system is subsequently presented.

Autonomous systems are increasingly discussed in the domain of industrial automation. However, there is no consensus in the literature about the definition of industrial autonomous systems, what characteristics do they possess and how to distinguish them from intelligent industrial automation systems. Another important aspect is the comparison between the degree of automation and autonomy. Addressing this confusion, this paper starts with an analysis of the historical development of the term autonomy. Based on the similarities in the definitions, that were identified in a literature review, a comprehensive definition of industrial autonomous systems is presented, and four main characteristics of an industrial autonomous system are derived. Consequently, the characteristics for the realization of an industrial autonomous system (1) systematic process execution, (2) adaptability, (3) self-governance and (4) self-containedness are described in detail. Another objective of this contribution is to assist the developers of industrial autonomous systems to take the above-mentioned characteristics into account. Furthermore, the developer is advised which abilities can be used to achieve the corresponding essential characteristics of industrial autonomous systems. To illustrate the characteristics and abilities of an industrial autonomous system, this paper gives a detailed description of three realized cases of application in industry.

The frequency of changes in production requirements is continuously increasing due to economic volatility, shorter innovation cycles and product life cycles. Therefore, a prediction of all goals of a production system at development time is impossible, which is leading to an increased reconfiguration demand during operation. Currently, there are still some weaknesses concerning the reconfiguration of production systems, which are highlighted in this article. The future of industrial automation will be dominated by Cyber-Physical Production Systems, which offer many promising potentials. Hence, this contribution discusses the Cyber-Physical Production Systems and some of their potentials regarding the reconfiguration issues. Corresponding concepts are required to utilize these theoretical potentials. Therefore, this research provides a basic concept for self-organized reconfiguration management.

A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.

Developing consistently well performing visual recognition applications based on convolutional neural networks, e.g. for autonomous driving, is very challenging. One of the obstacles during the development is the opaqueness of their cognitive behaviour. A considerable amount of literature has been published which describes irrational behaviour of trained CNNs showcasing gaps in their cognition. In this paper, a methodology is presented that creates worstcase images using image augmentation techniques. If the CNN's cognitive performance on such images is weak while the augmentation techniques are supposedly harmless, a potential gap in the cognition has been found. The presented worst-case image generator is using adversarial search approaches to efficiently identify the most challenging image. This is evaluated with the well-known AlexNet CNN using images depicting a typical driving scenario., to be published in 16th CASE 2021: Lyon

The heterogeneity and dynamics of IoT systems pose new challenges for their simulation, especially for hardware-in-the-loop simulations, which can be met by modular co-simulation. Therefore, several existing co-simulation approaches are presented and their suitability for hardware-in-the-loop co-simulations is evaluated. A new concept for dynamic hardware-in-the-loop co-simulation of IoT systems with a multi-agent system is presented, where each IoT component is simulated in its own simulation tool. Each individual simulation is represented by an agent and is therefore able to dynamically enter a co-simulation at run-time, which enables a "plug-and-simulate" behavior. The presented concept is evaluated by a prototypical implementation.

Zusammenfassung Durch zunehmende Agilität im Entwicklungsprozess, kürzere Lebenszyklen und sich ändernde Kunden- und Gesetzgeberanforderungen müssen Produktionssysteme wandlungsfähig sein. Aber jede Veränderung des Systemverhaltens muss anschließend auch erneut abgesichert werden. Zur Absicherung sicherheitskritischer Funktionen eignen sich formale Verifikationsmethoden. Allerdings ist hierzu ein hoher Modellierungsaufwand notwendig, was den Einsatz formaler Verifikationsmethoden in der Praxis hemmt. Zudem empfiehlt sich eine Überprüfung der zur formalen Verifikation eingesetzten Modelle. Zur Bewältigung der Herausforderungen für die Absicherung, die eine Rekonfiguration während des Betriebs nach sich zieht, werden in diesem Beitrag zwei vielversprechende und sich gegenseitig ergänzende Ansätze für die lebenszyklusübergreifende Absicherung von Produktionssystemen vorgestellt.

The demand of customer-specific products leads to a fundamental change to manufacturing facilities. To adapt the facilities to new product types, frequently occurring functionality changes in industrial automation systems are expected. Functionality changes are primarily implemented by software changes. These software changes within the operation phase can be implemented, for instance, by over-the-air software updates or ad hoc integration of new components. The effects of these changes are often difficult to estimate, especially in distributed automation systems. This mainly poses a challenge on production line operators, who are required to validate their automation systems after functionality changes have been executed. The goal of this contribution is to assist production line operators in the validation process of their automation systems after software changes. Formal verification methods can support the operators, due to its fully automated execution. However, the creation process of the behavior models needed for the formal verification is complex and error-prone. This is why formal verification is usually not used. Hence, a model-based technique is presented to automate this creation process. By means of this, the subsystem affected by the software change is automatically identified and subsequently a suitable input to a model-based verification tool is generated. The concept is based on the generation of a system model by composing the Petri net models of components within the automation system. In order to identify affected components, an impact analysis is performed, starting from the component in which a modification occurred. Followingly, a tailored subsystem is composed using the component models necessary for verification. This subsystem is applied to verify the system requirements for the affected components. To evaluate the applicability of the concept in the field of industrial automation, a distributed automation system was implemented. A service-oriented, OPC-UA-based, control network is thereby used to implement a technical process. Furthermore, a configuration interface enables change of the components at runtime. This emulates over-the-air updates and ad hoc networking. The concept is implemented with the demonstrator “TestIAS.” This test device detects software changes within the automation system and verifies them automatically according to the model-based approach presented. An empirical evaluation was performed with ten different reconfiguration scenarios showing functional changes. In addition, based on the time measurements of the time saving due to the impact analysis, the efficiency enhancement is substantiated.

Cyber-Physical Systems, characterized by networking capabilities and digital representations, offer many promising potentials for industrial automation. In an attempt to further enrich the system's digital representation by incorporating interdisciplinary models and considering a continuous and synchronized representation of it within the cyber layer, the concept of the Digital Twin emerged, enabling system monitoring, virtual commissioning, failure diagnosis and simulations by managing the Cyber-Physical Systems data along its lifecycle. To add further intelligence into the Digital Twin, the architecture of the intelligent Digital Twin was proposed. Nevertheless, managing and relating the complex and dynamic digital models as well as the heterogeneous data of the intelligent Digital Twin present open challenges. Due to their inherent extensibility and adaptability as well as their semantic expressiveness, Knowledge Graphs are a suitable concept to overcome these challenges and enable reasoning to gain new insights. Prominent applications of Knowledge Graphs are recommendation systems and exploratory search within the semantic web. However, there seems to be a lacking yet potential applicability for Knowledge Graphs in the industrial domain. Therefore, this contribution proposes a Knowledge Graph enhanced architecture of the intelligent Digital Twin, offering capabilities, which are internal linking and referencing, knowledge completion, error detection, collective reasoning and semantic querying. Based on the proposed concept, potential application fields for Knowledge Graph enhanced intelligent Digital Twin are addressed.

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset., Comment: 6 pages, 5 figures, 3 tables, submitted to the CIRP Conference on Manufacturing Systems 2021

Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks. Deep transfer learning offers mitigation by letting algorithms built upon previous knowledge from different tasks or locations. In this article, a modular deep learning algorithm for anomaly detection on time series datasets is presented that allows for an easy integration of such transfer learning capabilities. It is thoroughly tested on a dataset from a discrete manufacturing process in order to prove its fundamental adequacy towards deep industrial transfer learning - the transfer of knowledge in industrial applications' special environment., Comment: 8 pages, 5 figures, 5 tables. Accepted for publication at IEEE ETFA 2021

Ensuring high availability despite the growing frequency of changes in production requirements leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by Cyber-Physical Production Systems (CPPS) in the future. Therefore, a concept, covering a methodology containing four steps, is utilized to answer the research question how to enhance CPPS with a self-organized reconfiguration management. This contribution focuses on the first two steps of this concept: the identification of reconfiguration demand and the generation of alternative configurations. A condensed presentation of the related work reveals that an interface-oriented formalized process description depicts an appropriate conceptual basis. Building upon this, the presented concept contains a capability model of the CPPS and the reasoning to determine a potential reconfiguration demand, as well as a procedure for the generation of alternative configurations. To evaluate the concept, an agent-based implementation is given, which uses an OPC-UA controlled simulated modular production system as a substitute CPPS in discrete manufacturing.

Increased flexibility and improved resilience in production and manufacturing processes are goals that are becoming more and more important in the context of Industry 4.0 and from the experience of the Covid-19 pandemic. At the same time, efficient operation of production must be guaranteed to achieve economic as well as ecological objectives. Intelligent assistance systems follow the idea to support stakeholders of production systems in their decisions and can thus be useful applications for helping to master the various challenges and to meet these goals. In this paper, we describe functional components of these decision support systems: data provision and data extraction, knowledge base, simulation models, model execution and analytics, and application and user interaction. We show the underlying technologies and illustrate why these assistant systems are valuable for several stakeholders.

Nowadays, formal methods are used in various areas for the verification of programs or for code generation from models in order to increase the quality of software and to reduce costs. However, there are still fields in which formal methods have not been widely adopted, despite the large set of possible benefits offered. This is the case for the area of programmable logic controllers (PLC). This article aims to evaluate the potential of formal methods in the context of PLC development. For this purpose, the general concepts of formal methods are first introduced and then transferred to the PLC area, resulting in an engineering-oriented description of the technology that is based on common concepts from PLC development. Based on this description, PLC professionals with varying degrees of experience were interviewed for their perspective on the topic and to identify possible use cases within the PLC domain. The survey results indicate the technology's high potential in the PLC area, either as a tool to directly support the developer or as a key element within a model-based systems engineering toolchain. The evaluation of the survey results is performed with the aid of a demo application that communicates with the Totally Integrated Automation Portal from Siemens and generates programs via Fastsynth, a model-based open source code generator. Benchmarks based on an industry-related PLC project show satisfactory synthesis times and a successful integration into the workflow of a PLC developer., Comment: 8 pages, 6 figures, 1 table. Accepted for publication at IEEE INDIN 2021

In response to an aging society, the development and application of medical assistance systems is increasing and becoming an integral part of various daily life activities. To enable effective assistance, designing intuitive and elderly-appropriate user interfaces is necessary and represents a challenge due to the lack of standardized design processes. A possible approach for the design is to apply the usability engineering process based on the IEC 62366 standard for medical devices. In this contribution, the design process of a user interface for an automated pill dispenser is shown based on applying the standard, a literature review and a user evaluation.

Networked and intelligent cyber-physical systems are being increasingly applied in automation technology. Surrounding parameters representing the ambient environment and the users interacting with these systems can greatly influence their operation and tend to be heterogeneous and dynamic. There is insufficient consideration for these parameters and how they relate to the system and can contribute to optimizing its analysis and operation. Enabling context-awareness for cyber-physical systems presents an approach to consider surrounding and situational factors. In this contribution, the design of context-aware cyber-physical systems is addressed with the goal to first introduce a fitting definition for context-aware cyber-physical systems as well as propose an approach to dynamically model the system’s context in relation to its user and environment. The concept is shown for a user-centric pill dispenser, based on which the role as well as applicability of the context model is presented.

Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.

Smart Manufacturing adopts an extensive amount of enabling technologies. Progress in ICT constantly drives innovations. Ubiquitous connectivity enabled by IoT technologies, increasing compute capacity and efficiency, and the subsequent push of digitalization in industry enabled new data-driven applications and the re-emergence of AI-technologies over the last decade. At the same time, the deficiencies of current infrastructures and system architectures are revealed when companies fail to adopt data-driven technologies extensively. This paper discusses how modern IT-architectures and infrastructure for cyber-physical production systems can enable their inherent self-x capabilities to pave the road towards higher levels of autonomy facilitated by data-driven technologies.

Modern production systems often form Internet-of-Things-systems (IoT). Because of the flexibility those systems are dynamic, meaning the entering of components during runtime, and heterogeneous. For the simulation of such systems, those challenges of dynamic and heterogeneity have to be met by a dynamic co-simulation. An important aspect of a co-simulation is the synchronization of the used simulations. In this contribution, challenges of synchronizing a co-simulation of IoT-systems are introduced and existing co-simulation synchronization concepts examined with regard to their usability for simulating IoT-systems. Afterwards a synchronization concept is presented, which can be used in the presented agent-based co-simulation concept.

The heterogeneity and dynamic of IoT-systems pose new challenges for their simulation, which can be met by a modular co-simulation. Therefore several existing co-simulation approaches are presented and evaluated. A new concept for a dynamic co-simulation of IoT-systems utilizing a multi-agent-system is presented, wherein each IoT-component is simulated in a separate simulation tool. Each separate simulation is represented by an agent, and therefore able to enter a running co-simulation dynamically during runtime, which allows for a “Plug-and-Simulate” behavior. The connection between agents and simulation tools is realized by an interface concept. The presented concept is evaluated by a prototypical implementation.

Manufacturing individual products requires individual production. To implement the production of unique products with traditional engineering approaches is thoroughly complex. Therefore, decentralized production systems on the basis of cyber physical systems (CPS) promise a realization schema and thereby, the reduction of the overall systems’ complexity. Cyber physical systems are rather more complex in communication, time requirements and coordination, but are less complex from a modularization point of view and enable new ways of diagnosis and maintenance. The digitalization of production and new concepts of Industry 4.0 enable direct machine to machine communication, exchange of information and making of common decisions. An approach for future manufacturing in which individual products are scheduled incrementally is presented in this paper. In the presented approach, the product invites itself production resources to offer process steps in an auction. The product automatically ranks the offers according to requirements which are predefined by the customer. The best bids are asked to optimize their offers within a negotiation process. To evaluate our approach, a software-agent based demonstrator was implemented.

Challenges on safeguarding distributed automation systems arise due to their increasing complexity and changeability. Functional changes in automation systems are mainly conducted by software modifications. Especially in distributed automation systems, the impacts of software modifications are difficult to estimate. Mainly, this will challenge plant operators who have to safeguard their automation systems after functionality changes were executed. If behaviour models of the automation systems are available, model-based techniques are suitable to estimate the impacts of software modifications on other system components. In fact, behaviour models of distributed automation systems are seldom available or maintained, due to the high complexity of the overall system and the changing structure caused by reconfigurations or software modifications. This often prevents the application of model-based techniques. This contribution presents a model-based approach with which the impacts of software modifications can be recognized and affected subsystems can be safeguarded efficiently by model-based verification methods. To achieve this an impact analysis is performed, identifying requirements which are affected by software modifications. As the behaviour models that are necessary to verify the identified requirements are seldom available, the necessary models are generated automatically. The approach is evaluated with modification to a large scale automation system.

Process anomalies and fluctuations in product quality are widespread problems in discrete manufacturing. There have been various control approaches to tackle the challenge. This paper presents a cross-process control approach that combines engineering knowledge and data analytics techniques. An initial rule basis is generated by experts using simulation models. To achieve a data driven enhancement concerning process and product quality, a PLC-based connector is developed to record and unify real process data from heterogeneous data sources. The data is processed in the cloud and inferred using online modeling techniques. Neural networks with autoencoder structure are applied to extract unknown features, to iteratively refine the knowledge base and thus to optimize quality control.

A Digital Twin, an always in sync digital model of existing manufacturing cells, can be used to reduce time and risk of reconfiguration by early detection of design or process sequence flaws of the system in virtual commissioning and simulation. In this paper the need of Digital Twins in future production plants as well as the structure of the Digital Twin is presented. The engineering process of production systems is a cross-domain challenge between mechanics, electrics and software, but a lack of collaboration and universal information transfer between the domains leads to a high investment volume by synchronization the digital model from the time of commissioning. To synchronize cross-domain mechatronic data models of mechatronic components in the digital world during the life cycle of existing production systems this paper presents the anchor point method to firstly detect variances of cross-domain mechatronic data structure between the digital model and the real system in the specific domains electrics, mechanics and software and update the virtual models to have a consistent data model of the Digital Twin.

The increasing complexity of distributed automation systems requires new methods to verify the correct functionality. Model-based verification is an established approach to test the behavior of the system under test, before going into operation. To apply model-based techniques the overall system model of the automation system is needed. Due to the high complexity of the overall system and the changing dependencies caused by reconfigurations or software modifications an overall system model is seldom available or maintained. In this paper, we propose a modeling technique to manage the complexity of the overall system by modularization which is dedicated to distributed systems. This is presented in a formal way. Thereby, the modeling techniques regard the requirements of a service-oriented-architecture and the properties of automation systems, like interfaces to the technical process and parallelism caused by the distribution. In addition, we present calculation rules how to build up the overall system model automatically which can be used to verify system requirements.

Increasing product variety and the shortening of product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing automation systems. To face this challenge one solution is a Digital Twin, which can be used to reduce the complexity and time of reconfiguration by early detection of design or process sequence errors of the system with a cross-domain simulation. For engineering the Digital Twin and systemically synchronizing the data of mechatronic components in the interdisciplinary engineering models of a Digital Twin during the life cycle of manufacturing automation systems, this paper presents a concept for the engineering of a Digital Twin based on model integration in a PLM IT-Platform and an Anchor-Point method to systematically detect variances of the mechatronic data structure between the digital models and the physical system. The data of a mechatronic component from interdisciplinary domains, developed by the corresponding engineering tools are referred to as anchor points. This paper analyses domain-specific challenges in automation software-code to develop an assistance system for rule-based consistency check and for synchronizing the engineering models of the Digital Twin of the manufacturing automation system based on the Anchor-Point method.

In the age of the Industrial Internet of Things, distributed subsystems are established in many domains, which bring added value through their networking. In the field of industrial automation this means a distribution of the control system. As the distribution involves an increase in complexity, new approaches are needed that address this problem. This paper presents a model-driven development approach that involves a Service Oriented Architecture.

Zusammenfassung Bei der Bestimmung der Zuverlässigkeit von cyber-physischen Systemen müssen neue Ansätze und Konzepte entwickelt werden, die den Anforderungen an die neuen Technologien gerecht werden. Neben der Möglichkeit, zur Laufzeit erfasste Daten, die durch die Vernetzung von Komponenten zur Verfügung stehen, zur genaueren Berechnung der Zuverlässigkeit zu verwenden, stellt insbesondere die dynamische Systemstruktur von cyber-physischen Systemen, die zum Zeitpunkt der Systementwicklung noch nicht bekannt ist, Herausforderungen und nutzbare Potenziale für die Zuverlässigkeitsanalyse dar. Der vorliegende Beitrag präsentiert ein Konzept zur dynamischen Zuverlässigkeitsberechnung mithilfe eines Agentensystems. Dabei wird die klassischerweise oft als konstant angenommene Ausfallrate in drei zeitabhängige Bestandteile zerlegt, um beispielsweise den Einfluss der menschlichen Zuverlässigkeit in die Berechnung mit aufzunehmen. Mithilfe von Software-Agenten werden die erforderlichen Informationen zur Berechnung der Teilausfallraten ermittelt. Anhand eines Prototyps wird der Einfluss dynamischer Einflussgrößen auf die Zuverlässigkeit eines Systems gezeigt.

Sharing knowledge and experience between test and diagnosis of production systems is a key to reduce faults and increase the availability. In distributed productions with several factories on different locations, the knowledge sharing becomes an important issue because direct communication between the test and maintenance teams is hardly possible. Based on test and diagnosis of an industrial quality assurance system we propose a combined decision support system for test and diagnosis that automatically shares the knowledge between both support functions. Using a decision support system motivates users to properly formalize their knowledge and reduces communication overhead. Maintainers are supported by an automatic fault diagnosis based on fault symptoms. Testers are supported by a test progress overview and a test case prioritization. By linking test cases to fault symptoms, the diagnosis is enriched by a test case recommendation for further fault isolation and the test case prioritization is expanded by a fault symptom coverage as a criterion that addresses in-field faults. As demonstrated with the industrial quality assurance system, both leads to synergy effects that improve the support functions and reduce overhead communication.

Due to different trends in manufacturing, existing mechatronic systems, such as automated manufacturing machines, are obliged to evolve in order to cope with a quickly changing environment. The evolution of mechatronic systems is achieved by adaptation processes, where changes in the physical structure of a system and/or its automation software are performed. An important phase of an adaptation process is the modeling and analysis of existing interdependencies between system elements which serve as a basis for subsequent adaptation phases. However, current modeling approaches mostly focus on single system element types and do not regard the holistic system, i.e., products, processes and resources. Accordingly, in this contribution a concept for modeling interdependencies between products, processes and resources is proposed with the aim of providing a solid data base for the generation of adaption options. The application of the concept is demonstrated by an illustrative example of a pick-and-place unit.

The increasing complexity of modern automation systems leads to inevitable faults. At the same time, structural variability and untrivial interaction of the sophisticated components makes it harder and harder to apply traditional fault detection methods. Consequently, the popularity of Deep Learning (DL) fault detection methods grows. Model-based system design tools such as Simulink allow the development of executable system models. Besides the design flexibility, these models can provide the training data for DL-based error detectors.

A requirement of future industrial automation systems is the application of intelligence in the context of their optimization, adaptation and reconfiguration. This paper begins with an introduction of the definition of (artificial) intelligence to derive a framework for artificial intelligence enhanced industrial automation systems: An artificial intelligence component is connected with the industrial automation system's control unit and other entities through a series of standardized interfaces for data and information exchange. This framework is then put into context of the intelligent Digital Twin architecture, highlight the latter as a possible implementation of such systems. Concluding, a prototypical implementation on the basis of a modular cyber-physical production system is described. The intelligent Digital Twin realized this way provides the four fundamental sub-processes of intelligence, namely observation, analysis, reasoning and action. A detailed description of all technologies used is given.

Digital Twins have been described as beneficial in many areas, such as virtual commissioning, fault prediction or reconfiguration planning. Equipping Digital Twins with artificial intelligence functionalities can greatly expand those beneficial applications or open up altogether new areas of application, among them cross-phase industrial transfer learning. In the context of machine learning, transfer learning represents a set of approaches that enhance learning new tasks based upon previously acquired knowledge. Here, knowledge is transferred from one lifecycle phase to another in order to reduce the amount of data or time needed to train a machine learning algorithm. Looking at common challenges in developing and deploying industrial machinery with deep learning functionalities, embracing this concept would offer several advantages: Using an intelligent Digital Twin, learning algorithms can be designed, configured and tested in the design phase before the physical system exists and real data can be collected. Once real data becomes available, the algorithms must merely be fine-tuned, significantly speeding up commissioning and reducing the probability of costly modifications. Furthermore, using the Digital Twin's simulation capabilities virtually injecting rare faults in order to train an algorithm's response or using reinforcement learning, e.g. to teach a robot, become practically feasible. This article presents several cross-phase industrial transfer learning use cases utilizing intelligent Digital Twins. A real cyber physical production system consisting of an automated welding machine and an automated guided vehicle equipped with a robot arm is used to illustrate the respective benefits., 6 pages, 7 figures, submitted to the CIRP Design Conference 2021

Increasing product variety and shortening product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing systems. A synchronized Digital Twin of the manufacturing system is one solution to face these challenges. In order to use the Digital Twin for reconfiguration, a major challenge is to keep the developed Digital Twin of a manufacturing system, which was created during the engineering process, synchronized with the real system after commissioning. To automatically synchronize the cross-domain models of a Digital Twin after the commissioning of a manufacturing system, the authors introduced the Anchor-Point-Method in their previous papers. In this paper, the realization of the Anchor-Point-Method based on an assistance system is presented and the functionality is evaluated. This assistance system enables having an up-to-date Digital Twin of a manufacturing system available during the entire life-cycle of a system. Finally, a qualitative and quantitative evaluation of the advantages of a synchronized Digital Twin for the reconfiguration of a manufacturing system is presented. For this purpose, an automated system has been digitally and physically designed and built. On this system, a reconfiguration using the synchronized Digital Twin was performed and compared with another reconfiguration without Digital Twin collected through a survey. The results show that the Digital Twin can reduce the time of the reconfiguration process by up to 58 percent.

This paper highlights the necessity for a digital twin of production systems in the automotive industry. The survey on the use case of a digital twin of the production line is conducted with the production planning department of a major automobile manufacturer. The central part of the paper describes the current and future challenges for production planners in integration planning, which are determined by a survey. There are new trends in the automotive industry identified as a result of this survey. The study identifies exactly the information required by a digital twin of the production plant to improve the integration planning process. Furthermore, this survey provides answers to the benefits of integration planning by an automatically generated digital twin.

This contribution describes a novel approach for developing a medication assistance system, namely a pill dispenser. The approach supports mobile and stationary use and applies IoT features for the system to be interconnected and remain synchronized. Based on a user survey and a literature review, functional requirements for the pill dispenser have been derived and use case scenarios conceptualized, followed by a rudimentary software and hardware development.

The demographic change facing Germany and Europe leads to a growing importance of user-centered products for the elderly. Especially the increase of patient multimorbidity leads to a growing necessity of future healthcare solutions. In terms of medication management, a lack of reliable pill intake support for the elderly can be observed. This research project develops an approach for a fully automated pill management system. Special focus in this contribution lies on the user-centered design process for the elderly.

The communication protocol OPC UA is one of the most important IIoT enablers within the automation domain. OPC UA not only aims to provide interoperability on the transport layer, but also interoperability of the semantic layer shall be addressed based on so-called Companion Specifications. However, the lack of OPC UA formal semantics makes automatic validation of OPC UA data models impossible. Another drawback is the shortage of available tools for OPC UA, such as an implementation of the query engine for the specified OPC UA query language. In this paper we provide a formal translation of OPC UA models to the Semantic Web standard OWL, thus making OPC UA implicit semantics, that is described in the documentation, explicit, by means of OWL axioms. Moreover, we outline how this mapping can be used to offer validation and querying of OPC UA data models based on already existing Semantic Web technology.

Intelligent industrial automation devices based on cyber-physical systems can establish ad-hoc networks of manufacturing systems to produce individual products. To realize those, all network-participants, with their offered services and properties need to be described in an explicit semantic. A cloud-based concept is conceived in this paper which includes a semantic to describe the manufacturing systems, their characterization as well as the specified user requirements. A cloud-based concept called “e-Production” maps the abilities of the manufacturing systems with the customer requirements and identifies the best collaborative units for production regarding to energy, costs and other quality characteristics.

Wheeled walkers are recommended to improve walking performance in older persons and to encourage and assist participation in daily life. Nevertheless, using a wheeled walker can cause serious problems in the natural environment. This study aimed to compare uphill and downhill walking with walking level in geriatric patients using a wheeled walker. Furthermore, we investigated the effect of using a wheeled walker with respect to dual tasking when walking level. A total of 20 geriatric patients (median age 84.5 years) walked 10 m at their habitual pace along a level surface, uphill and downhill, with and without a standard wheeled walker. Gait speed, stride length and cadence were assessed by wearable sensors and the walk ratio was calculated. When using a wheeled walker while walking level the walk ratio improved (0.58 m/[steps/min] versus 0.57 m/[steps/min], p = 0.023) but gait speed decreased (1.07 m/s versus 1.12 m/s, p = 0.020) when compared to not using a wheeled walker. With respect to the walk ratio, uphill and downhill walking with a wheeled walker decreased walking performance when compared to level walking (0.54 m/[steps/min] versus 0.58 m/[steps/min], p = 0.023 and 0.55 m/[steps/min] versus 0.58 m/[steps/min], p = 0.001, respectively). At the same time, gait speed decreased (0.079 m/s versus 1.07 m/s, p < 0.0001) or was unaffected. The use of a wheeled walker improved the quality of level walking but the performance of uphill and downhill walking was worse compared to walking level when using a wheeled walker.