Evolving technology in wireless telecommunication, such as 5G, provides opportunities to utilize wireless communication more in an industrial setting where reliability and predictability are of great concern. More capable Industrial Internet of Things devices (IIoT) are, indeed, a catalyst for Industry 4.0. Still, before the IIoT devices can be deemed capable enough, a method to evaluate the IIoT systems unobtrusively—so that the evaluation does not affect the performance of the systems—must be established. This thesis aims to answer how the performance of a distributed control system can be unobtrusively evaluated, and also determine what the state-of-the-art is in latency measurements in distributed control systems. To answer the question, a novel diagnostics method for time-critical control systems in cloud-fog automation is proposed and extensively evaluated on real-life testbeds that use 5G, WiFi 6, and Ethernet in an edge-computing topology with real control systems. The feasibility of the proposed method was verified by experiments conducted with a diagnostics framework prototype developed in this thesis. In the proposed diagnostics framework, the controller application is monitored by a computing probe based on an extended Berkeley Packet Filter program. Network communication between the controller and control target is evaluated with a multi-channel Ethernet probe and custom-made software that computes several metrics related to the performance of the distributed system. The data from the unobtrusive probes are sent to a time-series database that is used for further analysis and real-time visualization in a graphical interface created with Grafana. The proposed diagnostics method together with the developed prototype can be used as a research infrastructure for future evaluations of distributed control systems.

This thesis aims to investigate a digital, emerging business model, which just enjoys the highest attention in many industrial sectors. The industry 4.0 changes how original equipment manufacturers (OEMs) retain their competitiveness and offer innovative solutions to their customers. Therefore, this master thesis investigates the diverse opportunities of usage-based business models – namely Pay-per-X / Equipment-as-a-Service – from the perspective of a sensor-tech start-up. The company, which can act as a technical enabler for Pay-per-X, is located in Germany and has various markets leading machine and component manufacturers as customers. This projects’ goal is to get an in-depth understanding of the business model and how the company (with their technology) can participate in the market, which is estimated to have a size of 131.2m USD in 2025. Therefore, mainly qualitative research methods have been applied – however, quantitative sections enrich the analysis part of the thesis. Nine expert interviews were conducted, and a calculation tool was developed, which aims to easily convince OEMs of the new business models through modeling a business case, by incorporation of the most relevant parameters. Two use cases were analyzed in the fields of production machines and commercial vehicles. This is followed by a short excursus to a required Pay-per-X cloud software, where requirements were defined based on machine users’ needs. Based on these insights, a positioning strategy for the case company within this field is proposed later-on, which puts emphasis on how the firm can act as a technical enabler for Pay-per-X business models. / Syftet med denna avhandling är att undersöka en ny digital affärsmodell som just nu får stor uppmärksamhet inom många industrisektorer. Industri 4.0 förändrar hur tillverkare av originalutrustning behåller sin konkurrenskraft och erbjuder innovativa lösningar till sina kunder. Därför undersöker denna masteruppsats de olika möjligheterna med användningsbaserade affärsmodeller - nämligen Pay-per-X / Equipment-as-a-Service - ur ett startupföretag inom sensorteknik. Företaget, som kan fungera som en teknisk möjliggörare för Pay-per-X, är beläget i Tyskland och har olika marknadsledande maskin- och komponenttillverkare som kunder. Projektets mål är att få en djupgående förståelse för affärsmodellen och hur företaget (med sin teknik) kan delta på marknaden, som beräknas ha en storlek på 131,2 miljoner US-dollar år 2025. Därför har huvudsakligen kvalitativa forskningsmetoder tillämpats - men kvantitativa avsnitt berikar analysdelen av avhandlingen. Nio expertintervjuer genomfördes och ett beräkningsverktyg utvecklades, som syftar till att enkelt övertyga OEMs om de nya affärsmodellerna genom att modellera ett affärscase, genom att införliva de mest relevanta parametrarna. Två användningsfall analyserades inom områdena produktionsmaskiner och kommersiella fordon. Detta följs av en kort utflykt till en nödvändig Pay-per-X-molnmjukvara, där kraven definierades utifrån maskinanvändarnas behov. På grundval av dessa insikter föreslås senare en positioneringsstrategi för fallföretaget inom detta område, som lägger tonvikten på hur företaget kan fungera som en teknisk möjliggörare för Pay-per-X-affärsmodeller.

Low-power wireless technology is a part and parcel of our daily life, shaping the way in which we behave, interact, and more generally live. The ubiquity of cheap, tiny, battery-powered devices augmented with sensing, actuation, and wireless communication capabilities has given rise to a ``smart" society, where people, machines, and objects are seamlessly interconnected, among themselves and with the environment. Behind the scenes, low-power wireless protocols are what enables and rules all interactions, organising these embedded devices into wireless networks, and orchestrating their communications. The recent years have witnessed a persistent increase in the pervasiveness and impact of low-power wireless. After having spawned a wide spectrum of powerful applications in the consumer domain, low-power wireless solutions are extending their influence over the industrial context, where their adoption as part of feedback control loops is envisioned to revolutionise the production process, paving the way for the Fourth Industrial Revolution. However, as the scale and relevance of low-power wireless systems continue to grow, so do the challenges posed to the communication substrates, required to satisfy ever more strict requirements in terms of reliability, responsiveness, and energy consumption. Harmonising these conflicting demands is far beyond what is enabled by current network stacks and control architectures; the need to timely bridge this gap has spurred a new wave of interest in low-power wireless networking, and directly motivated our work. In this thesis, we take on this challenge with a main conceptual and technical tool: concurrent transmissions (CTX), a technique that, by enforcing nodes to transmit concurrently, has been shown to unlock unprecedented fast, reliable, and energy efficient multi-hop communications in low-power wireless networks, opening new opportunities for protocol design. We first direct our research endeavour towards industrial applications, focusing on the popular IEEE 802.15.4 narrowband PHY layer, and advance the state of the art along two different directions: interference resilience and aperiodic wireless control. We tackle radio-frequency noise by extensively analysing, for the first time, the dependability of CTX under different types, intensities, and distributions of reproducible interference patterns, and by devising techniques to push it further. Specifically, we concentrate on CRYSTAL, a recently proposed communication protocol that relies on CTX to rapidly and dependably collect aperiodic traffic. By integrating channel hopping and noise detection in the protocol operation, we provide a novel communication stack capable of supporting aperiodic transmissions with near-perfect reliability and a per-mille radio duty cycle despite harsh external interference. These results lay the ground towards the exploitation of CTX for aperiodic wireless control; we explore this research direction by co-designing the Wireless Control Bus (WCB), our second contribution. WCB is a clean-slate CTX-based communication stack tailored to event-triggered control (ETC), an aperiodic control strategy holding the capability to significantly improve the efficiency of wireless control systems, but whose real-world impact has been hampered by the lack of appropriate networking support. Operating in conjunction with ETC, WCB timely and dynamically adapts the network operation to the control demands, unlocking an order-of-magnitude reduction in energy costs w.r.t. traditional periodic approaches while retaining the same control performance, therefore unleashing and concretely demonstrating the true ETC potential for the first time. Nevertheless, low-power wireless communications are rapidly evolving, and new radios striking novel trade-offs are emerging. Among these, in the second part of the thesis we focus on ultra-wideband (UWB). By providing hitherto missing networking primitives for multi-hop dissemination and collection over UWB, we shed light on the communication potentialities opened up by the high data throughput, clock precision, and noise resilience offered by this technology. Specifically, as a third contribution, we demonstrate that CTX not only can be successfully exploited for multi-hop UWB communications but, once embodied in a full-fledged system, provide reliability and energy performance akin to narrowband. Furthermore, the higher data rate and clock resolution of UWB chips unlock up to 80% latency reduction w.r.t. narrowband CTX, along with orders-of-magnitude improvements in network-wide time synchronization. These results showcase how UWB CTX could significantly benefit a multitude of applications, notably including low-power wireless control. With WEAVER, our last contribution, we make an additional step towards this direction, by supporting the key functionality of data collection with an ultra-fast convergecast stack for UWB. Challenging the internal mechanics of CTX, WEAVER interleaves data and acknowledgements flows in a single, self-terminating network-wide flood, enabling the concurrent collection of different packets from multiple senders with unprecedented latency, reliability, and energy efficiency. Overall, this thesis pushes forward the applicability and performance of low-power wireless, by contributing techniques and protocols to enhance the dependability, timeliness, energy efficiency, and interference resilience of this technology. Our research is characterized by a strong experimental slant, where the design of the systems we propose meets the reality of testbed experiments and evaluation. Via our open-source implementations, researchers and practitioners can directly use, extend, and build upon our contributions, fostering future work and research on the topic.

The manufacturing industry is competitive and is constantly striving to improve OEE. In the transition to smart production, digital technologies such as IIoT are highlighted as important. IIoT platforms enable real-time monitoring. In this sense, digital technologies such as IIoT are expected to improve OEE by enabling the analysis of real-time data and production availability.  A qualitative study with an abductive approach has been conducted. The empirical material has been collected through a case study of a heavy-duty vehicle industry and the theoretical framework is based on a literature study. Lastly, a thematic analysis has been used for the derivation of appropriate themes for analysis. The study concluded that challenges and enablers related to the implementation of IIoT to improve OEE can be divided into technical and cultural factors. Technical challenges and enablers mainly consider the achievement of interoperability, compatibility, and cyber security, while cultural factors revolve around digital acceptance, competence, encouragement of digital curiosity, and creating knowledge and understanding towards OEE. Lastly, conclusions can be drawn that implementation of IIoT has a positive effect on OEE since it ensures consistent and accurate data, which lies a solid foundation for production decisions. Also, digitalization of production enhances lean practices which are considered a key element for improving OEE.

This diploma thesis is focused on the study of concept of Internet of Things, concept of Industry 4.0 and on current conditions in smart factories. Based on these studies there was designed and implemented hardware and software adjustment for industrial machines and connection of these industrial machines to communication network. There was designed and programmed the application in the C# language. This application provides a data collection from industrial machines, provides various services for machines and humans and this application is processing and viewing necessary data. For these purposes the application is using a database system based on the SQLite. These tasks of designing and implementation are summarized to system, which is determined for smart factory implementation. This implementation is created according to Industry 4.0 concept.

It was analyzed if instructions on a head-worn Augmented Reality display (AR-HWD) are better for manual industrial assembly tasks than instructions on a stationary screen. A prototype was built which consisted of virtual instruction screens for two example assembly tasks. In a comparison study participants performed the tasks with instructions through an AR-HWD and alternatively through a stationary screen. Questionnaires, interviews and observation notes were used to evaluate the task performances and the user experience. The study revealed that the users were excited and enjoyed trying the technology. The perceived usefulness at the current state was diverse, but the users saw a huge potential in AR-HWDs for the future. The task accuracy with instructions on the AR-HWD was equally good as with instructions on the screen. AR-HWDs are found to be a better approach than a stationary screen, but technological limitations need to be overcome and workers need to train using the new technology to make its application efficient.

The oil and gas industry faces a lack of compact drilling devices capable of performing horizontal drilling maneuvers in depleted or abandoned wells in order to enhance oil recovery. The purpose of this project was to design and develop a smart sensing system that can be later implemented in compact drilling devices used to perform horizontal drilling to enhance oil recovery in wells. A smart sensor is the combination of a sensing element (sensor) and a microprocessor. Hence, a smart sensing system is an arrangement that consists of different sensors, where one or more have smart capabilities. The sensing system was built and tested in a laboratory setting. For this, a test bench was used as a case study to simulate the operation from a micro-drilling device. The smart sensing system integrated the sensors essential for the direct operational measurements required for the robot. The focus was on selecting reliable and sturdy components that can handle the operation Down the Hole (DTH) on the final lateral micro-drilling robot. The sensing system's recorded data was sent to a microcontroller, where it was processed and then presented visually to the operator through a User Interface (UI) developed in a cloud-based framework. The information was filtered, processed, and sent to a controller that executed commands and sent signals to the test bench’s actuators. The smart sensing system included novel modules and sensors suitable for the operation in a harsh environment such as the one faced in the drilling process. Furthermore, it was designed as an independent, flexible module that can be implemented in test benches with different settings and early robotic prototypes. The outcome of this project was a sensing system able to provide robotic drilling devices with flexibility while providing accurate and reliable measurements during their operation.

The term Industry 4.0, sometimes referred to as a buzzword, is today on everyone’s tongue and the benefits undeniably seem to be promising and have potential to revolutionize the manufacturing industry. But what does it really mean? From a high-level business perspective, the concept of Industry 4.0 most often demonstrates operational efficiency and promising business models but studies show that many companies either lack understanding for the concept and how it should be implemented or are dissatisfied with progress of already implemented solutions. Further, there is a perception that it is difficult to implement the concept without interference with the current production system.The purpose of this study is to interpret and outline the main characteristics and key components of the concept Industry 4.0 and further break down and conclude the potential benefits and enablers for a manufacturing company within the heavy automotive industry. In order to succeed, a case study has been performed at a manual final assembly production unit within the heavy automotive industry. Accordingly, the study intends to give a deeper understanding of the concept and specifically how manual assembly within an already existing manual production system will be affected. Thus outline the crucial enablers in order to successfully implement the concept of Industry 4.0 and be prepared to adapt to the future challenges of the industry. The case study, performed through observations and interviews, attacks the issue from two perspectives; current state and desired state. A theoretical framework is then used as a basis for analysis of the result in order to be able to further present the findings and conclusion of the study. Lastly, two proof of concept are performed to exemplify and support the findings. The study shows that succeeding with implementation of Industry 4.0 is not only about the related technology itself. Equally important parts to be considered and understood are the integration into the existing production system and design and purpose of the manual assembly process. Lastly the study shows that creating understanding and commitment in the organization by strategy, leadership, culture and competence is of greatest importance to succeed. / Begreppet Industri 4.0, ibland benämnt som modeord, är idag på allas tungor och fördelarna verkar onekligen lovande och tros ha potential att revolutionera tillverkningsindustrin. Men vad betyder det egentligen? Ur ett affärsperspektiv påvisar begreppet Industri 4.0 oftast ökad operativ effektivitet och lovande affärsmodeller men flera studier visar att många företag antingen saknar förståelse för konceptet och hur det ska implementeras eller är missnöjda med framstegen med redan implementerade lösningar. Vidare finns det en uppfattning att det är svårt att implementera konceptet utan störningar i det nuvarande produktionssystemet. Syftet med denna studie är att tolka och beskriva huvudegenskaperna och nyckelkomponenterna i konceptet Industri 4.0 och ytterligare bryta ner och konkludera de potentiella fördelarna och möjliggörarna för ett tillverkande företag inom den tunga bilindustrin. För att lyckas har en fallstudie utförts vid en manuell slutmonteringsenhet inom den tunga lastbilsindustrin. Studien avser på så sätt att ge en djupare förståelse för konceptet och specifikt hur manuell montering inom ett redan existerande manuellt produktionssystem kommer att påverkas. Alltså att kartlägga viktiga möjliggörare för att framgångsrikt kunna implementera konceptet Industri 4.0 och på så sätt vara beredd att ta sig an industrins framtida utmaningar. Fallstudien, utförd genom observationer och intervjuer, angriper frågan från två perspektiv; nuläge och önskat läge. Ett teoretiskt ramverk används sedan som underlag för analys av resultatet för att vidare kunna presentera rön och slutsats från studien. Slutligen utförs två experiment för att exemplifiera och stödja resultatet. Studien visar att en framgångsrik implementering av Industri 4.0 troligtvis inte bara handlar om den relaterade tekniken i sig. Lika viktiga delar som ska beaktas och förstås är integrationen i det befintliga produktionssystemet och utformningen och syftet med den manuella monteringsprocessen. Slutligen visar studien att det är av största vikt att skapa förståelse och engagemang i organisationen genom strategi, ledarskap, kultur och kompetens.

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>