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

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

A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing

In manufacturing, many use cases of Industry 4.0 require vendor-neutral and machine-readable information models to describe, implement and execute resource functions. Such models have been researched under the terms capabilities and skills. Standardization of such models is required, but currently not available. This paper presents a reference model developed jointly by members of various organizations in a working group of the Plattform Industrie 4.0. This model covers definitions of most important aspects of capabilities and skills. It can be seen as a basis for further standardization efforts

Towards a real-time capable plug & produce environment for adaptable factories

Industrial manufacturing is currently undergoing a transformation from mass production with inflexible production systems to individual production with adaptable cells. In order to ensure this adaptability of these systems, technologies such as plug & produce are needed, to integrate, modify and remove devices at runtime. Therefor an exact description of the system, the products and the capabilities / skills of the devices is essential as well as a network for communication between the devices. Deterministic data transmission is particularly important for distributed control systems. We propose an architecture for plug & produce mechanisms with hard real-time capable communication paths between the cyber-physical components using OPC UA PubSub over TSN and the ability to load and execute real-time critical tasks at runtime

Kommunikation und Bildverarbeitung in der Automation

In diesem Open Access-Tagungsband sind die besten Beiträge des 11. Jahreskolloquiums "Kommunikation in der Automation" (KommA 2020) und des 7. Jahreskolloquiums "Bildverarbeitung in der Automation" (BVAu 2020) enthalten. Die Kolloquien fanden am 28. und 29. Oktober 2020 statt und wurden erstmalig als digitale Webveranstaltung auf dem Innovation Campus Lemgo organisiert. Die vorgestellten neuesten Forschungsergebnisse auf den Gebieten der industriellen Kommunikationstechnik und Bildverarbeitung erweitern den aktuellen Stand der Forschung und Technik. Die in den Beiträgen enthaltenen anschauliche Anwendungsbeispiele aus dem Bereich der Automation setzen die Ergebnisse in den direkten Anwendungsbezug

When Operation Technology Meets Information Technology: Challenges and Opportunities

Industry 4.0 has revolutionized process innovation while facilitating and encouraging many new possibilities. The objective of Industry 4.0 is the radical enhancement of productivity, a goal that presupposes the integration of Operational Technology (OT) networks with Information Technology (IT) networks, which were hitherto isolated. This disruptive approach is enabled by adopting several emerging technologies in Enterprise processes. In this manuscript, we discuss what we believe to be one of the main challenges preventing the full employment of Industry 4.0, namely, the integration of Operation Technology networking and Information Technology networking. We discuss the technical challenges alongside the potential tools while providing a state-of-the-art use case scenario. We showcase a possible solution based on the Asset Administration Shell approach, referring to the use case of camera synchronization for collaborative tasks

Semantic Driven Approach for Rapid Application Development in Industrial Internet of Things

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated.We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.Die Entwicklung des Internet der Dinge (IoT) hat die industrielle Automatisierung revolutioniert. Industrielle Geräte auf allen Ebenen wie Feldgeräte, Steuergeräte, Geräte auf Unternehmensebene usw. sind mit dem Internet verbunden, wodurch problemlos auf sie zugegriffen werden kann. Es hat die Art und Weise, wie Anwendungen auf industriellen Automatisierungssystemen entwickelt werden, deutlich verändert. Es führte zum Paradigmenwechsel, wo neuartige IoT Anwendungsentwicklungstools, wie Node-RED, verwendet werden können, um komplexe industrielle Anwendungen als IoT-Orchestrierungen zu entwickeln. Aktuell sind diese Anwendungen jedoch ausschließlich an Geräte bestimmter Anbieter und Ökosysteme gebunden. Sie können nicht mit Geräten anderer Anbieter und Plattformen verbunden werden, da die Anwendungen nicht semantisch interoperabel sind. Daher ist es wünschenswert, plattformunabhängige, herstellerneutrale Anwendungsvorlagen für allgemeine Automatisierungsaufgaben zu verwenden. Im aktuellen Status von Node-RED können solche wiederverwendbaren und interoperablen Anwendungsvorlagen jedoch nicht entwickelt werden. Diese Interoperabilitätsprobleme auf Datenebene können im IoT mithilfe von Semantic Web (SW) -Technologien behoben werden. Für Ingenieure oder Entwickler von IoT-Anwendungen sind SW-Technologien nicht sehr einfach zu verwenden. Zur Erstellung interoperabler IoT-Anwendungen sind daher neuartige IoT-Tools erforderlich. Zu diesem Zweck schlagen wir eine neuartige semantische Erweiterung des weit verbreiteten Node-RED-Tools vor, indem wir semantische Definitionen wie iot.schema.org in die semantischen Modelle von NODE-Red einführen. Das Tool leitet einen Gerätehersteller oder Maschinebauer, die keine Experten in semantische Technologien sind, an um die Semantik eines Geräts konsistent zu konfigurieren. Darüber hinaus ermöglicht es auch einem Ingenieur oder IoT-Anwendungsentwickler, semantische, interoperable IoT-Anwendungen mit minimalem Aufwand zu entwerfen und entwicklen Unser Ansatz beschleunigt die Anwendungsentwicklungsprozesse durch Einführung neuartiger semantischer Anwendungsvorlagen namens Rezepte für Node-RED. Durch die Verwendung von Rezepten können komplexe Anwendungsentwicklungsaufgaben wie das Abgleichen von Funktionen zwischen Rezepten und vorhandenen Strukturen automatisiert werden. Wir demonstrieren Skill-Matching in der Cloud oder am Industrial Edge eines Automatisierungssystems. Wir haben dafür quantitative und qualitative Bewertung unseres Ansatzes durchgeführt, um die Machbarkeit und Skalierbarkeit des Ansatzes in realen Szenarien zu testen. Die Ergebnisse der Bewertung werden in dieser Arbeit vorgestellt und diskutiert

Smart industrial IoT monitoring and control system based on UAV and cloud computing applied to a concrete plant

Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and helping to make decisions. UAVs are a highly consistent technological platform for efficient and cost-effective data collection and event monitoring. The industrial Internet of things (IIoT) sends data from systems that monitor and control the physical world to data processing systems that cloud computing has shown to be important tools for meeting processing requirements. In fog computing, the IoT gateway links different objects to the internet. It can operate as a joint interface for different networks and support different communication protocols. A great deal of effort has been put into developing UAVs and multi-UAV systems. This paper introduces a smart IIoT monitoring and control system based on an unmanned aerial vehicle that uses cloud computing services and exploits fog computing as the bridge between IIoT layers. Its novelty lies in the fact that the UAV is automatically integrated into an industrial control system through an IoT gateway platform, while UAV photos are systematically and instantly computed and analyzed in the cloud. Visual supervision of the plant by drones and cloud services is integrated in real-time into the control loop of the industrial control system. As a proof of concept, the platform was used in a case study in an industrial concrete plant. The results obtained clearly illustrate the feasibility of the proposed platform in providing a reliable and efficient system for UAV remote control to improve product quality and reduce waste. For this, we studied the communication latency between the different IIoT layers in different IoT gateways.The authors would like to thank the Seneca Foundation as also FRUMECAR S.L., for their support and the opportunity to implement and test the proposed approach on their facilities. This work was partially supported by FRUMECAR S.L. and Seneca Foundation's "Murcia Regional Scientific Excellence Research Program" (Murcia Science and Technology Agency-19895/GERM/15)

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions