Industrial internet of things: Recent advances, enabling technologies and open challenges

The industrial communication market is dominated by Ethernet-based fieldbus systems. Although they share similar requirements and market segments, their implementations and ecosystems differ considerably. As a result, end customers and device manufacturers are faced with a multitude of technologies that need to be produced, run, diagnosed, maintained, and kept in stock. Although the availability of products and services is largely satisfactory, dealing with multiple solutions generates high costs and limits IoT capability. This paper introduces Open Platform Communication Unified Architecture Time-Sensitive Networking (OPC UA TSN) as a new technology and presents the current view. This time, the industrial prospects of fulfilling industrial communication requirements while leveraging the cost benefits of standard Ethernet hardware in the midterm are in reach. We anticipate that OPC UA TSN will reveal itself as a game changer in the field of industrial automation, being a candidate for establishing a holistic communication infrastructure from the sensor to the cloud.

An Introduction to OPC UA TSN for Industrial Communication Systems

In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are challenging to acquire using classical control and motion planning approaches. Consequently, robot controllers for assembly domains are presently engineered to solve a particular task, and cannot easily handle variations in the product or environment. Reinforcement learning (RL) is a promising approach for autonomously acquiring robot skills that involve contact-rich dynamics. However, RL relies on random exploration for learning a control policy, which requires many robot executions, and often gets trapped in locally suboptimal solutions. Instead, we posit that prior knowledge, when available, can improve RL performance. We exploit the fact that in modern assembly domains, geometric information about the task is readily available via the CAD design files. We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data. We show that our approach effectively improves over traditional control approaches for tracking the motion plan, and can solve assembly tasks that require high precision, even without accurate state estimation. In addition, we propose a neural network architecture that can learn to track the motion plan, thereby generalizing the assembly controller to changes in the object positions.

Learning Robotic Assembly from CAD

Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applicat...

A Survey on Industrial Information Integration 2016–2019

Smart manufacturing that involves tight integration of the physical system and cyber system is a hot topic in both industry and academia in the era of the Internet and big data. However, the dynami...

Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing

The industrial internet of things (IIoT) is growing at an exponential rate generating massive amounts of industrial data. This data must be leveraged to support business and operational goals. As a result, there is an urgent need for adopting big data technologies to enable data analytics in industrial automation. This paper explores interrelations between IIoT and big data technologies and how they work together to generate business insights from industrial data. Additionally, requirements for cloud-based solutions are derived from the Industrie 4.0 use case scenario value-based-services, focusing on condition monitoring and predictive maintenance services. A survey of selected cloud-based platforms is conducted to examine how these platforms meet the requirements derived from the use case. Results show that existing general cloud platforms should adopt more IIoT applications and platforms, while existing industrial cloud platforms should add big data frameworks to their portfolio. Finally, an architecture for integrating cloud-based IIoT and big data solutions is introduced and issues regarding the use of public cloud for IIoT applications are discussed.

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A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0

The Smart Factory Web (SFW) is a platform that connects Smart Factories over a network to enable flexible sharing and management of resources, assets and inventory to maximize production and efficiency. In order to become part of the Smart Factory Web, network participants describe not only their products, but also the factory capabilities to order to improve factory-to-factory collaboration. A Smart Factory Web Portal (SFWP) enables secure data and service integration in cross-site application scenarios as well as ‘plug & work’ functions for devices, machines, and data analytics software by applying industrial standards, Open Platform Communications Unified Architecture (OPC UA), and Automation Markup Language (AutomationML or AML for short). OPC UA serves as a comprehensive and secure communication protocol from the machine level into the cloud. A Cloud Coupler on the shop floor publishes the availability of a factory and selected process data in the SFWP. Customers or even smart machines can use this information to make decisions for placing an order and track their orders in real time. In order to simplify the setup of the connection to the cloud and minimize the commissioning time the cloud coupler also aggregates all OPC UA servers on devices in the factory into a single OPC UA aggregated server address space. In this paper an architecture is proposed which uses cloud coupler and plug and work techniques to make a new or retrofit factory available in a SFW to share capability information towards a new marketplace for manufacturing. It is shown that the integration efforts are decreased but also the use of standards reduces the effort to define interfaces.

Cloud-based Plug and Work architecture of the IIC Testbed Smart Factory Web

In the industrial automation a paradigm shift from centralized, static automation structures to reconfigurable manufacturing systems (RMS) might be lie ahead. RMS are seen as a key enabler for the required changeability of future production companies since they can reduce the engineering effort needed for the reconfiguration of existing or the construction of new production systems. However, it is not clear how the companies can benefit from RMS in detail. For example, the reduction of engineering effort cannot be expressed in figures by today. But such information is necessary to convince the industry of the advantages of the new production principle. Indeed, existing RMS paradigms like Service-Oriented Architectures are rarely used in the practice of automation. The basis for analyzing the advantages of RMS is an analysis of the status quo in the industrial automation. Regarding the engineering effort of current automation systems, this paper will present a case study considering the effort occurring during the commissioning process of a production system constructed by state-of-the-art components. The evaluation of the study can serve as a reference when comparing the engineering effort of RMS with today's systems.

Analyzing the engineering effort for the commissioning of industrial automation systems

Smart Factory Web is an interconnection of factories all over the globe with the goal to form an easy-to-use manufacturing platform. To handle heterogeneous and changing demands of the participating users, the factory has not only to flexibilize its production assets but its network in regards to management and configuration as well. This contribution provides an insight into how this can be achieved by utilizing Software-defined networking for the concrete use case Smart Factory Web. An architecture proposal is given extending the current design of a factory which participates at Smart Factory Web. This proposal is furthermore discussed leading to a step-by-step process how a factory can transform its current traditional network infrastructure to a more flexible SDN based one.

Utilizing SDN Infrastructure to provide Smart Services from the Factory to the Cloud

The paper describes a reference architecture for open marketplaces to be used for networked stakeholders in industrial production ecosystems. The motivation for such an endeavor comes from the idea to apply the basic principle of the platform economy to offer functions of an asset “as a service” to industrial production, including the associated supply chain networks. Currently, commercial offers of “production as a service” usually lead to proprietary systems with the risk of platform vendor lock-ins. Hence, there is a need for an open approach that relies upon international (emerging) standards, especially those from IETF, IEC, the Plattform Industrie 4.0 and the International Data Spaces Association (IDSA). The presented approach enables federation of marketplaces according to well-defined interfaces. This article proposes a technology-independent open architecture derived from functional and non-functional system requirements and driven by the idea of the Smart Factory Web, a testbed of the Industrial Internet Consortium (IIC). Furthermore, the architecture of the Smart Factory Web (SFW) platform is presented and assessed against the current and future demands of open federated marketplaces for industrial production ecosystems.

Sascha Heymann

Papers

A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0

Cloud-based Plug and Work architecture of the IIC Testbed Smart Factory Web

Analyzing the engineering effort for the commissioning of industrial automation systems

Utilizing SDN Infrastructure to provide Smart Services from the Factory to the Cloud

Smart Factory Web—A Blueprint Architecture for Open Marketplaces for Industrial Production