Industrial Internet of Things

The Internet of Things (IoT) is an information network of physical objects (sensors, machines, cars, buildings, and other items) that allows interaction and cooperation of these objects to reach common goals [2]. While the IoT affects among others transportation, healthcare, or smart homes, the Industrial Internet of Things (IIoT) refers in particular to industrial environments. In this context Cyber Manufacturing Systems (CMS) evolved as a significant term. This opening chapter gives a brief introduction of the development of IIoT introducing also the Digital Factory and cyber-physical systems. Furthermore, the challenges and requirements of IIoT and CMS are discussed as well as potentials regarding the application in Industry 4.0 are identified. In this process aspects as economic impact, architectural pattern and infrastructures are taken into account. Besides, also major research initiatives are presented. In addition to that, an orientation to the reader is given in this chapter by providing brief summaries of the chapters published in this book. Hereby, the following research areas are addressed: “Modeling for CPS and CPS”, “Architectural Design Patterns for CMS and IIoT”, “Communication and Networking”, “Artificial Intelligence and Analytics”, and “Evolution of Workforce and Human-Machine-Interaction”. The chapter closes with a discussion about future trends of IIoT and CMS within Industry 4.0.

The potential transformation cyber-physical systems can bring to a broad variety of domains is widely discussed in academia and industry. Despite the expected benefits in the industrial domain of further automatization of production processes and the possibility to produce “batch size one” at large-scale production costs, the majority of organizations hesitate in the implementation of cyber-physical systems. This can be attributed to uncertainty decision makers feel, about how to choose right applications of cyber-physical systems and if chosen how to implement these applications to the unique and specific needs of their organization. To address this problem this chapter introduces an application map which includes the spheres smart factory, industrial smart data, industrial smart services, smart products, product-related smart data and product-related smart services. Based on this model, the decision makers are provided a scheme of application fields for utilizing cyber-physical architectures adjusted to their unique business situation.

Cyber-physical manufacturing networks bear the chance to change the face of tomorrow’s electronic and mechatronic products as well as their production systems. The ability to integrate miniaturized sensors and printed communication technologies into materials, machines, and products leads to autonomous cyber-physical systems with an image in the virtual world and a real-world counterpart down on the shop floor. An efficient sensor data consolidation is in the position to establish self-learning control loops across global production networks in order to increase process robustness as well as process flexibility and thus allowing for instant product changes with an ideal lot size of one. Low-cost solutions for smart autonomous vehicles enable the breakup of classical production lines. One of the major challenges of these disruptive changes is securing the manageability of the possible data and information overflow. Novel socio-cyber-physical assistance systems will ensure the operation of these smart factories.

There is a convergence of interests in cyber-physical systems, systems engineering, and manufacturing innovation in the United States. The U.S. National Institute of Standards and Technology (NIST) has undertaken research programs in smart manufacturing systems to address many of the standards and measurement science issues that arise from this convergence. This chapter describes the convergence, the progress made thus far in the NIST programs in smart manufacturing systems, and the challenges that drive further research.

Engineering of supervisory controllers for cyber-physical systems is a challenging task in practice, amongst others because of the high complexity of the uncontrolled system. A supervisory controller coordinates the behaviour of a (cyber-physical) system based on discrete-event observations of its state. It uses these observations to decide which activities the uncontrolled system can safely perform or to determine activities that (are more likely to) lead to acceptable system behaviour. In model-based engineering, models are used in the design process to show the correctness of a solution before it is actually implemented. The engineering of supervisory controllers for large and complex cyber-physical systems such as cyber-physical manufacturing and production systems requires dedicated engineering support. The Compositional Interchange Format language and toolset have been developed for this purpose. We illustrate a model-based engineering framework for supervisory control on a case study involving the coordination of autonomously navigating vehicles. We discuss the engineering steps involved in this framework such as modelling, supervisory control synthesis, and validation through simulation-based visualization, verification, real-time testing, and code generation, and illustrate some of these. We explain how the CIF language and supporting tools can be used for these typical activities.

Cyber-Physical Systems (CPS) integrate physical and cyber components, where the latter are responsible for the computation part. Due to their steadily increasing complexity, these cyber components have to be modeled at high level of abstraction when creating a new CPS. Therefore, the Electronic System Level (ESL) emerged and a widely accepted ESL design language is SystemC. The main driver for abstraction in SystemC is Transaction Level Modeling (TLM) which allows describing complex communication without all the details. Since the SystemC TLM models are used for early software development and as reference for hardware implementation their correct functional behavior is crucial. Admittedly, the best possible verification quality can be achieved with formal approaches. However, formal verification of TLM models is a hard task. Existing methods basically consider local properties or have extremely high run-time. In contrast, the proposed approach can efficiently verify true TLM properties, for instance the effect of a transaction can be formally checked which has not been possible before. Our approach transforms the SystemC model to C, embeds the TLM property in form of assertions into the C model and finally uses a novel induction to check the validity of the property. The induction method is essentially a lifting of inductive bounded model checking to C. In experiments we show the efficiency of the approach.

Cyber-physical production systems based on technologies such as machine to machine communication, the Internet of Things and other cutting edge technologies are going to advance manufacturing automation and industrial production. Information technology seems once again to be the driving force for change in manufacturing automation. But what are the characteristics of such systems in comparison to the existing approaches? In this article we recommend an evaluation model for cyber-physical production systems is proposed based on a set of system characteristics, which defines specific abilities and performance indicators. Furthermore, an analysis and verification of that model is presented sketching the typical pattern and impact of cyber-physical production systems. As a result a refined evaluation model is available, suitable for the characterization of cyber-physical technologies and thereby enabling a technological assessment.

In this chapter we present current work about the Internet of Things (IoT) as a general building block for industrial production. The basic idea is to provide each physical entity with a virtual representation and a storage space, named the digital object memory. Such memories can be used for produced goods as well as the production line components themselves and contain all production-relevant data. Moreover, in smart production lines a centralized orchestration service coordinates “the needs” of each good and triggers the necessary actuators. For each object an individual production plan is created, based on the object’s memory and external requirements and conditions. Based on this concept, actuators can be replaced without stopping the production line and all collected data is available in the object’s and in the production line’s memory and can be further displayed or processed.

In this chapter the integration of a knowledge database and machine vision within a robot-based CPS is picked out as a central theme. Three examples show the differences of implementing a robot-based CPS within large or small and medium-sized enterprises. The use cases describe the implementation of prototypes in different surroundings. It can either be an automated basic process in hazardous surroundings like the load of petrochemical liquids, one of several automated processes ongoing parallel in a classical production plant like the assembling process or an implementation to connect different automated process steps using an overall RFID system in the laundry process.

Interoperability is a progressive issue in smart automation. To tackle the problem of missing interoperability, the standardization of interfaces and transfer protocols is the established solution, endorsed by many organizations like DIN ISO, IEEE, or the OPC UA foundation. However, many machine tools and robots already exist and are used along diverse manufacturing processes. These tools lack common interfaces for interconnection. Also a common standard depicts the status quo at a certain time of development. That means future developments and technologies are not considered. Due to the increase in complexity of production networks a standard carries the risk to be a least common multiple. Hence, a different approach is needed. In this chapter, we discuss approaches that provide adaptive interfaces. Therefore, the technical systems need to interpret data and derive meaning of the data. This requires a new type of standardization that facilitates exchange of information between heterogeneous systems.

Cyber Physical Systems (CPS) in production offer the chance of enhancing the resiliency of factories as depicted by the three main pillars adaptability, robustness and efficiency. Simultaneously, they will have a large impact on the way production facilities are organized and structured. This chapter thus reviews the basic concept of CPS in factories and their three dedicated specificities—systems for production, transportation and assistance—as well as the future role of production planning systems in an integrated, digitalized production environment. In the second part, two examples covering Cyber Physical Production and Assistance Systems will be provided to familiarize the reader with the industrial application of these concepts in actual facilities. An energy-oriented manufacturing planning and control system as well as the application of smart glasses in an industrial assembly task are used for demonstration. In addition to a depiction of the structure and composition of CPS in these show cases, their improvements compared to the current state-of-the-art will be quantified to highlight the underlying potentials for resiliency.

In the past, communication in industrial monitoring, automation, and control was mostly realized locally, often relying on wired solutions, restricting communication and control to single factory environments. To overcome this limitation, the Industrial Internet of Things (IIoT) envisions the integration of these local communication structures into larger systems, such as the interconnection between factories and suppliers, or even the Internet. Moreover, to achieve flexibility with regard to automation processes and to save costs in deployment and maintenance, wireless solutions more and more find their way into factories. In this chapter, we present recent efforts and standardized solutions to realize wireless communication for local industrial automation and ultimately identify the requirements and mechanisms for connecting these setups to globally accessible communication infrastructures. To this end, we focus on special requirements unique to the IIoT, e.g., the use of highly constraint devices and the resulting effects on the use of standardized protocols.

Communication networks are an essential part of any cyber-physical system (CPS) as they interconnect the CPS subsystems and components. In this chapter, we first introduce CPSs and the major role of networked control in such systems. Then, data communication networks are outlined in general and the different types of communication networks for CPSs are presented. The chapter goes on to describe some of the deficiencies of data networks and their influences on control loops. Thereafter, we highlight the need for improved communication reliability to realize CPSs and describe the existing general approaches to improve it. The potential benefits and the challenges to use the Internet are discussed after that. This is followed by considering a prominent communication standard for CPSs in the domain of smart grids. Then, the importance of pattern-based development is indicated and a description of common communication patterns for CPSs is provided. Finally, we conclude this chapter.

The growing importance of Internet of Things gives rise to various possibilities for the manufacturing industry. Benefiting from the numerous potentials that cyber-physical systems (CPS) offer, the relevance for manufacturing companies generally lies in the idea of becoming smart instead of fast. In line with this idea, two different use cases related to the development and integration of CPS in the machine tools. The first example involves a milling tool with integrated sensing capabilities for the purpose of measuring the forces during the process. The second use case focuses on a smart clamping device from a turning machine. An intelligent chuck is developed by using an electronic clamping system. This CPS controls and regulates its clamping force on the basis of sensor values that are measured directly on the clamped product. Finally, the approach is generalized and potentials for integration into current processes used in the machine industry are discussed.

The Smart Factory is able to receive production orders and to control its value streams by communication between all involved elements. The whole production process is monitored by sensor systems generating a seamless blend of data that is condensed to information and key values using process models. Unknown states and critical situations are presented with full transparency to the worker that acts as the final decision maker. The required ability to gather and process information and to communicate this information to other entities is granted by Cyber-Physical Production Systems (CPPS). The structure of this article represents the pathway from data to knowledge and the subsequent knowledge exploitation. In the first part, the concepts of contemporary sensors and sensor systems are highlighted. The integration and fusion of single metering elements to measuring systems with suitable data pre-processing ensures the direct utilizability of high complex measuring data by CPPS. Actual examples of application demonstrate the implementation in production systems. The second part deals with the CPPS as the architecture for smart applications. Therefore, models are introduced as carriers of technology knowledge for the digital production. The interpretation of the measurement data in an adequate manner will empower the CPPS to adapt manufacturing processes and make the right decisions. By this, safety buffers may be reduced or quality requirements increased, as the system “knows” more about its state and boundaries. Since the final controller of the smart factory will still be a human being, CPPS also need a sound interface to the real, human world. The article closes with the introduction of tech apps that provide on the one hand an added value to the user by presenting machine states and key value and on the other hand enriches the model qualities by requesting expert knowledge from the machine operator. This finally makes the production system “smart”, as it enables the hardware of a factory to blend with the software and the human worker into one, seamless system.

Loading stations for flammable liquids are still handled manually. In this chapter handling of tank wagon manhole covers using industrial robots is presented, which will be used to automate the process. Currently a number of non-ergonomic and hazardous tasks have to be done manually. The heterogeneity of the processed tank wagons and manhole covers is the main obstacle for the automation of the process. The presented approach supplies a mechanical setup providing the required flexibility for the actual handling process and a procedure for identification of key geometric features of the wagons. The Human-Machine-Interface and the control structure had to be developed, as the facility operators have to interfere with the system to verify and adapt the targets given to the robot system but have no robotic experience. At the current state the developed system has been validated. The prototype setup is to be transferred to the industrial facility.

Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.

The increasing heterogeneity of automation systems coupled with the demand of more flexibility leads to a rising complexity of production plants. For this reason, production plants are becoming highly error-prone, and error detection is getting more difficult. Additionally, a great amount of expert knowledge is required to optimize production plants. Although machine learning approaches help to cope with these problems, these approaches are reaching their limits fast. As a result, approaches using Big Data are getting more popular, which help in finding relationships within the data, reliably detect anomalies (in advance), and allow an automatic optimization of the system. This contribution presents some approaches in the context of Big Data analyses in discrete continuous and hybrid production plants. This enables services in smart factories such as condition monitoring diagnosis and optimization. The contribution is rounded off with the presentation of practical examples of how the smart services can be applied.

Real (physical) objects melt together with information-processing (virtual) objects to create Cyber-Physical Production Systems (CPPS). Through embedding of intelligent, self-optimizing CPPS in process chains, productivity of manufacturing companies and quality of goods can be increased. Textile producers especially in high-wage countries have to cope with the trend towards smaller lot sizes in combination with the demand for increasing product variations. One possibility to cope with these changing market trends consists in manufacturing with CPPS and cognitive machinery. This chapter presents a method for multi-objective self-optimization of the weaving process. Multi-objective self-optimization assists the operator in setting weaving machine parameters according to objective functions. The implementation of a self-optimization routine in a software-based Programmable Logic Controller (soft-PLC) is presented. The routine enables a weaving machine to calculate the optimal parameter settings autonomously. Set-up time is reduced by 75 % and objective functions are improved by at least 14 % compared to manual machine settings.

The importance of flexibility in modern production and manufacturing systems increases in face of a constant rising degree of individualization in manufacturing industry and simultaneously shorter product life cycles. A result is a high number of variants which has to be produced with existing manufacturing systems, namely flow production systems. These are highly efficient for a large number of ideally similar products (cf. modular transverse matrix) which leads to an area of conflict where individualism and high throughput are competing. This chapter presents a new concept for assembling of small batch series with a high number of variants in a smart factory based on Cyber-Physical Systems (CPS). The concept involves a CPS-based semi central production program planning and a decentralized control system. This smart factory is scalable and operates at high flexibility through decentralized control strategies. The presented work describes an extract of the research project SMART FACE (Smart Micro Factory für Elektrofahrzeuge mit schlanker Produktionsplanung) where detailed concepts and prototypical implementations of Industry 4.0 methods and technologies within the application domain of the automotive industry are evaluated.

The idea of interlinked, sensor-augmented, self-governing Cyber Physical System (CPS) production processes is gaining momentum. At the same time, the impact of this concept on the workforce remains surprisingly vague. This can be explained by the fact that man is not at the centre of these developments and—even more importantly—automation is geared towards eliminating human activity. Based on the first views in the 1980s, the label of the “process worker” assumed an almost exclusive focus on control and maintenance tasks for this new type of worker in the automated factory. Recent experiences show, however, that as long as robots are not self-learning, man will be the template for these machines and we can already notice the emerging parallelism of fully automated production lines and human workshops. The changing nature of the workforce will gain further momentum if one depicts new, open formats of production. Here, the worker will have to assume new roles in reconfiguring the production processes.

The provisioning of information and capabilities of a Cyber-Physical System (CPS) can be performed directly in the CPS or in external applications and services. A Cyber Physical Production System (CPPS) can be seen to be an example of an evolving CPS, where new elements such as machines can be added to produce desired products. Human interaction with the CPPS can be reorganized among users that participate in the constantly evolving production tasks. This all requires solutions that enable the users to affect provisioning of information and capabilities of CPPS in different kinds of tasks. This chapter outlines an example of a user-driven CPPS and strategies for enabling user intervention in the behaviour of cyber-physical systems and in the behaviour of applications and services that are based on cyber-physical systems. In addition, we outline an architecture for user-driven CPSs that offers support for the proposed user intervention strategies.

To maintain industrial competitiveness in times of Cyber Physical Systems (CPS), organizations need to invest in sets of individual competencies. We show how competence management can synchronize individual and organizational competencies. We categorize different types of competencies which enable firms to master the technological and contextual complexity of CPS. Furthermore, we introduce a measurement instrument for these competencies, which includes aspects of technological and contextual complexity.

This chapter presents an overview of the current applications and future potentials of Cyber-Physical Systems (CPS) in the agricultural and construction machinery sectors. The different challenges in both sectors are explained and a typical CPS structure for mobile machines is described. This is followed by a categorization of data in mobile machinery and a description of the future impact on communication strategies. Six key technologies enabling the creation of CPS are defined and discussed in the context of agricultural and construction machinery. In addition, ten key algorithms are presented, which enable the definition of strategies by smartly and efficiently processing and combining data. At the end of this chapter, two typical processes, a street construction process and a crop growth cycle are analyzed in detail. In this context, the applications of the key technologies and key algorithms are exemplified.

Wind energy is a growing market and features high development potential on many levels in the future. In order to make use of the manifold potential, available data of wind turbines needs to be analyzed and advanced measuring technologies and communication networks have to be implemented. Hence, application of Cyber Physical Systems (CPS) can be a powerful approach to further develop the wind energy sector. Currently applied forms of CPS in wind energy are CMS or SCADA systems. However, these systems are not sufficient enough as a more extensive data analysis and communication network is required. Therefore, a future CPS in wind energy is presented in this article, which is the proposed next step in wind energy development. This advanced CPS is capable to reduce O&M costs and considers all relevant parameters of the power grid in order to optimize the operation and increase the efficiency of wind turbines and farms.

As a versatile tool, transfer printing provides routes to assemble micro- and nano-structures onto functional substrates, with promising applications ranging from stretchable electronics in diagnostic/therapeutic platforms and human-machine interfaces to dissolvable devices in bio-implants and environmentally benign sensors. The conventional process involves pickup of micro-devices from their fabricated substrates, followed by delivery onto the target substrate of interest. This chapter summarizes the fundamental mechanics and materials aspects of transfer printing, as well as recent developments in advanced techniques that allow for applications in systems with varying levels of complexity. The opportunities and challenges on emerging use for cyber-manufacturing systems are also discussed.

This chapter looks at manufacturing companies’ innovation capacity as it relates to cyber-physical production systems from a broader innovation ecosystem perspective. This contribution is guided by the research question which systemic measures need to be implemented to ensure a proper and wider diffusion of such complex systems by the example of Massachusetts and its advanced manufacturing companies that are poised to embrace cyber-physical production systems.