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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.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.

Der Systementwicklungsprozess nimmt immer mehr an Komplexität zu, da die Systeme selbst immer komplexer werden. Gleichzeitig Vermischen sich die verschiedenen Disziplinen wie Maschinenbau, Elektrotechnik und Softwaretechnik zunehmend, so dass Unternehmen einer Disziplin sprunghafte Komplexitätszuwächse bei ihren Systemen und in ihrer Entwicklung haben. Deshalb wird in dieser Veröffentlichung ein Konzept eines Sprachassistenten erarbeitet, der durch eine Entwicklungsphase führt. Daraus geht hervor, dass die Software zur Unterstützung der Entwicklung ein Informationsmodell benötigt, um die Daten des entwickelten Systems zu speichern und diese mit dem vorhandenen Wissen zu verbinden. Dieses Wissen kann entweder intern oder im Web vorhanden sein. Der Entwicklungsprozess soll daher Kooperation unterstützen, so dass die Assistenzsoftware und Ingenieure miteinander interagieren.

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.

Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. Current data analytics approaches show decent results concerning the optimization of single processes but lack in extensibility to plants with high-dimensional data spaces. This paper presents and compares two data-driven self-learning approaches that are used to detect anomalies within large amounts of machine and process data. Models of the machine behavior are generated to capture complex interdependencies and to extract features that represent anomalies. The approaches are tested and evaluated on the basis of real industrial data from metal forming processes.

The increasing change of production leads to differences between the current shop floor and the state of planning. This difference causes significant challenges for production planners while integrating new products into existing production systems. To tackle this issue, this paper presents a concept for the automated creation of a digital twin of a body-in-white production system based on current resources, products as well as process information from the cyber-physical system. The paper focuses on the different data sources and information in cyber-physical systems necessary for integration planning. Furthermore, major parts of the concept are evaluated in a real body-in-white production system. The resulting digital twin enables faster product integration and Industry 4.0 concepts.

Automated manufacturing machines in the discrete manufacturing domain frequently face changes in requirements, such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical, and software changes. In industrial practice, experts individually perform these adaptation processes without methodological support, which is time-consuming and highly error-prone. This article proposes a systematic approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process information facilitates root-cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on aluminium and steel forgings for harsh drop-forging technology. Furthermore, a framework for streaming and processing large amounts of real-time data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch-tracking are presented.

Zusammenfassung Änderungen der Funktionalität von Automatisierungssystemen werden zunehmend an der Software durchgeführt. Insbesondere bei verteilten Automatisierungssystemen lassen sich die Auswirkungen von Software-Änderungen oftmals nur schwer abschätzen. Dies stellt insbesondere Anlagenbetreiber vor große Herausforderungen bei der Absicherung von Funktions-Änderungen in der Betriebsphase. Dieser Beitrag beschreibt einen modellbasierten Ansatz, mit dem Auswirkungen von Software-Änderungen erkannt werden können und das betroffene Teilsystem automatisiert über modellbasierte Verifikationsverfahren abgesichert werden kann. Das präsentierte Konzept beruht auf der Komposition eines Systemmodells aus diskreten Verhaltensmodellen der Komponenten des Automatisierungssystems. Abschließend wird das Konzept anhand unterschiedlicher Änderungsszenarien evaluiert.

Based on autonomous software agents capable of calculating individual numerical field problems, a distributed method for solving transient field problems is presented. The software agents are running on distributed resources connected via a network and represent a dynamic calculation environment. Communication and data exchange between multiple agents enables their collaboration and allows decisions based on distributed overall knowledge. As unique characteristics, no central unit influences the solution process at any time. The presented simulation example and its evaluated calculation process proves the method to benefit from redundant resources.

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.

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes., 14 pages, 6 figures, 4 tables. Accepted for publication by Computers in Industry

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.

Die zunehmende Produktvielfalt und die Verkürzung der Produktlebenszyklen erfordern eine schnelle und kostengünstige Rekonfiguration bestehender Produktionssysteme. Dafür ist ein aktuelles digitales Modell der bestehenden Fertigungszelle, ein digitaler Zwilling, eine geeignete Lösung. Der digitale Zwilling verringert die Kosten und verkürzt die Umrüstzeiten durch virtuelle Planung und Simulation – basierend auf dem aktuellen Zustand der realen Produktionsanlage und durch eine frühzeitige Erkennung von Konstruktions- oder Prozessablauffehlern in der Produktionsanlage. Voraussetzung ist allerdings, dass ein aktuelles (virtuelles) Anlagenmodell von den mechatronischen Bestandteilen der realen Anlage während der verschiedenen Phasen ihres Lebenszyklus existiert. Im Beitrag wird die domänenübergreifende, mechatro­nische Datenstruktur virtueller Fertigungszellen in der Automobilindustrie diskutiert. Es wird eine systematische Ankerpunktmethode vorgestellt, mit Hilfe derer sich die Abweichungen zwischen den virtuellen Modellen und der Realität ermitteln lassen. Basierend darauf wird eine regelbasierte Konsistenzprüfung zur durchgängigen, domänenübergreifenden Synchronisierung der aktuellen mechatronischen Ressourcen­komponenten der Produktionssysteme mit deren virtuellem Anlagemodell vorgestellt.

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.

Die Häufigkeit von Änderungen der Produktionsanforderungen nimmt aufgrund wirtschaftlicher Volatilität, kürzerer Innovationszyklen und Produktlebenszyklen kontinuierlich zu. Daher ist eine Vorhersage aller möglichen Ziele eines Produktionssystems zur Entwurfszeit unmöglich und es ergibt sich erhöhter Rekonfigurationsbedarf zur Betriebszeit. Derzeit weist die Rekonfiguration von Produktionssystemen jedoch einige Schwachstellen auf, die in diesem Beitrag aufgezeigt werden. Außerdem wird die Zukunft der industriellen Automatisierung von Cyber-Physischen Produktionssystemen dominiert werden, welche vielversprechende Potentiale bieten. Folglich werden die Cyber-Physischen Produktionssysteme und einige ihrer Potentiale im Hinblick auf Rekonfiguration diskutiert. Um diese theoretischen Potentiale tatsächlich nutzen zu können, sind allerdings entsprechende Konzepte erforderlich, weshalb dieser Forschungsbeitrag ein grundlegendes Konzept für ein selbstorganisiertes Rekonfigurationsmanagement präsentiert.

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.

OPC UA is one of the most important communication protocols for IIoT applications in the automation domain. One important requirement for Industry 4.0 scenarios is standardized semantics, which is introduced into OPC UA through Companion Specifications. Nevertheless, OPC UA still lacks a comfortable interface for accessing these huge graphs, especially on the edge- and cloud-layer. Of course, there is a query language specified for OPC UA. However, to the best of our knowledge, there is no publicly available implementation.In this paper, we will present how SPARQL can be used to query OPC UA graphs natively and also will present rules to transform OPC UA queries into SPARQL queries. Furthermore, we will highlight some issues of OPC UA Query and how they can be bypassed by native SPARQL. Finally, we were able to correctly execute all nine example queries of OPC UA Part 4 Annex B (complex examples) with both approaches.

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.

In a mobile industrial fleet application different machinery is powered by each its own reciprocating internal combustion engine. EKU Power Drives (EKU) is focusing on increasing the energy efficiency of these fleet applications.

Increasing competition in the automotive industry makes cost-saving integration of more and more vehicle derivatives and variants such as sedans, coupes, cabriolets as well as electrical and combustion engine models into existing production systems necessary. At the same time, the production planners face a major challenge while integrating vehicle bodies-in-white. In contrast to the original concept and rough planning state, the automated production plants are continuously optimized during the detail planning phase as well as after the start of production as a result of improved processes and model upgrading. For fast integration of new vehicles, a current digital image of the real production plant - the so-called Digital Twin - is groundbreaking. This Digital Twin of a factory consists of a current bill of resources for cost calculation and a current layout planning state. The paper describes a concept for creating a Digital Twin of a body-in-white production system for the concept and rough planning projects. In the internal concept planning phase, planners do cost calculations and layouts for ordering factory suppliers. However, for integration planning, the original concept and rough planning project have to be updated. Therefore, a new concept has been developed which uses current information from the cyber-physical system and a current 3D scan to update the bill of resources and the layout planning on demand.

The paradigm of multi-agent systems is used in various application scenarios like manufacturing control or multi-agent assistance systems that aim at providing assistance to the user of the system in a specific task or situation. With an increasing number of applications of multi agent assistance systems in the industrial automation domain, the different architectures of these applications can be compared and differences and similarities can be identified. This paper presents the results of the literature review of 18 agent-based assistance systems from different domains. Within the reviewed architectures, seven different agent types could be identified. In a further step, design patterns based on the application domain and the level of automation have been identified and discussed. These design patterns can be used to facilitate the development of future multi agent assistance systems.

This paper presents a method to describe and increase the software quality of automation systems by a quantitative determination of the software reliability. Therefore, the software development process has been analyzed to identify the essential factors influencing the software reliability. These are the usage of a procedure model, the test factor and the human factor of the software developers themselves. These factors have been considered to create a neuro-fuzzy-based concept, which characterizes and consolidates them to realize an estimation of the software quality. The possibility to estimate the software quality, especially dynamically already during the software development process, will entail a huge benefit to programmers and manufacturers. Effects, methods or circumstances influencing the software quality in a negative or positive way can be numerically described and rated. On basis of that, necessary optimizations in the development process can be performed in time to guarantee a high quality of the software.

The experiment carried in this paper aims to study the feasibility of controlling an industrial robot to carry Peg-in-Hole assembling task using what called a Force/Torque Map. This type of control is based on real-time F/T sensor data during contact between the peg and the hole. The F/T Map presents the data acquired during previous attempts of the assembly task.

Wheeled walkers (WWs) are used to improve mobility and for fall prevention in older persons, but not all users are satisfied with the usability of WWs. Intelligent WWs are being developed to improve the usability. The aim of this study was to support the development of intelligent WWs by investigating possible problems of using a WW. This study investigated 22 geriatric in-patients (median age 82 years) with and without their WW while opening a door against the direction of walking and passing through. Other possible problems when using WWs were identified by interview. Walking through the door was faster without than with using the WW (8.71 versus 12.86 s, p

Automated peg-in-hole assembling approach using Force/Torque sensor and vision system is presented in this research. The complicity of the assembling task due to parts’ geometries or position uncertainty in work area are the main factors for the lack of fully automated assembling approaches nowadays in industry and specially in the automotive sector. Based on human operator handling for the assembling of peg-in-hole, an algorithm consists of six phases is developed. In one of the phases a model is created to determine the location of the hole's center during the insertion by using Force/Torque sensor data. The contact forces and torques in each step of the proposed assembling algorithms that was carried by a six degree-of-freedom (DOF) industrial robot is presented.