An architecture to predict anomalies in industrial processes

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internet of Things (IoT) and machine learning algorithms (ML) are enabling a revolutionary change in digitization in numerous areas, benefiting Industry 4.0 in particular. Predictive maintenance using machine learning models is being used to protect assets in industry. In this paper, an architecture for predicting anomalies in industrial processes was proposed in which SMEs can be guided in implementing an IIoT architecture for predictive maintenance (PdM). This research was conducted to understand what machine learning architectures and models are generally used by industry for PdM. An overview of the concepts of the Industrial Internet of Things (IIoT), machine learning (ML), and predictive maintenance (PdM) was provided, and through a systematic literature review, it was possible to understand their applications and which technologies enable their use. The survey revealed that PdM applications are increasingly common and that there are many studies on the development of new ML techniques. The survey conducted confirmed the usefulness of the artifact and showed the need for an architecture to guide the implementation of PdM. This research can be a contribution for SMEs, allowing them to become more efficient and reduce both production and maintenance costs in order to keep up with multinational companies

Building a Simple Smart Factory

This thesis describes (a) the search and findings of smart factories and their enabling technologies (b) the methodology to build or retrofit a smart factory and (c) the building and operation of a simple smart factory using the methodology. A factory is an industrial site with large buildings and collection of machines, which are operated by persons to manufacture goods and services. These factories are made smart by incorporating sensing, processing, and autonomous responding capabilities. Developments in four main areas (a) sensor capabilities (b) communication capabilities (c) storing and processing huge amount of data and (d) better utilization of technology in management and further development have contributed significantly for this incorporation of smartness to factories. There is a flurry of literature in each of the above four topics and their combinations. The findings from the literature can be summarized in the following way. Sensors detect or measure a physical property and records, indicates, or otherwise responds to it. In real-time, they can make a very large amount of observations. Internet is a global computer network providing a variety of information and communication facilities and the internet of things, IoT, is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. Big data handling and the provision of data services are achieved through cloud computing. Due to the availability of computing power, big data can be handled and analyzed under different classifications using several different analytics. The results from these analytics can be used to trigger autonomous responsive actions that make the factory smart. Having thus comprehended the literature, a seven stepped methodology for building or retrofitting a smart factory was established. The seven steps are (a) situation analysis where the condition of the current technology is studied (b) breakdown prevention analysis (c) sensor selection (d) data transmission and storage selection (e) data processing and analytics (f) autonomous action network and (g) integration with the plant units. Experience in a cement factory highlighted the wear in a journal bearing causes plant stoppages and thus warrant a smart system to monitor and make decisions. The experience was used to develop a laboratory-scale smart factory monitoring the wear of a half-journal bearing. To mimic a plant unit a load-carrying shaft supported by two half-journal bearings were chosen and to mimic a factory with two plant units, two such shafts were chosen. Thus, there were four half-journal bearings to monitor. USB Logitech C920 webcam that operates in full-HD 1080 pixels was used to take pictures at specified intervals. These pictures are then analyzed to study the wear at these intervals. After the preliminary analysis wear versus time data for all four bearings are available. Now the ‘making smart activity’ begins. Autonomous activities are based on various analyses. The wear time data are analyzed under different classifications. Remaining life, wear coefficient specific to the bearings, weekly variation in wear and condition of adjacent bearings are some of the characteristics that can be obtained from the analytics. These can then be used to send a message to the maintenance and supplies division alerting them on the need for a replacement shortly. They can also be alerted about other bearings reaching their maturity to plan a major overhaul if needed

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

Reducing risk in pre-production investigations through undergraduate engineering projects.

This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

Developing digital transformative capabilities of industrial businesses by leveraging the industrial internet of things

Industrial businesses are going through a period of digital disruption and firms are under severe pressure to undertake Digital Transformation and leverage the Industrial Internet of Things (IIoT). Yet, there is next to no scholarly guidance for such an endeavour. Most industrial firms are developing their Digital Transformation strategies, however, they are not sure what kind of capabilities they should develop for such transformation. Though there is limited academic literature about Digital Transformation and how firms are developing digital transformative capabilities, a systematic literature review was performed to disentangle capability transformation processes and how firms are developing dynamic capabilities to remain competitive in a high-velocity environment. The current study extended dynamic capability theory and proposed digital transformative capabilities (DTCs) for Digital Transformation. To understand the IIoT landscape and how it influences Digital Transformation, an industry review was performed. The research was conducted in two phases. Based on the literature review and industry review, in the first phase, two qualitative exploratory studies were performed. The preliminary exploratory study was conducted to get an understanding of the IIoT landscape and how firms were developing capabilities for transformation. Based on the insights from preliminary exploratory study, a detailed exploratory study was performed which revealed critical themes for Digital Transformation and, based on these themes, a conceptual framework for Digital Transformation was derived. The conceptual framework was divided into two models. The front-end model viii identified three DTCs (Business Model Transformation, Operating Model Transformation and Cultural Transformation), three inputs (Digital Twin, Digital Thread and Digital Mindset) and the factors influencing the DTCs. The back-end model examined the influence of DTCs on dynamic capabilities, which may be indicative of digital transformation in a company. In the second phase, these two models were tested through a quantitative analysis, utilizing data generated from 107 respondents from 87 industrial companies via a self-reported online questionnaire and the application of multiple linear regression analysis. The Digital Twin is widely touted as an important input for DTC but the result did not support that. Digital Thread as an input for DTC was supported and Digital Mindset as an input for DTC was partially supported. Using moderator analysis, important insights were identified. The moderators, Technology Turbulence, Market Turbulence, Competitor Turbulence and Path Dependency had some positive moderation effects. The positive influence of ‘DTC – Business Model Transformation’ on dynamic capabilities which may be indicative of digital transformation in a company was not supported. However, the positive influence of ‘DTC – Operating Model Transformation’ was supported and ‘DTC – Cultural Transformation’ was partially supported. The moderation effects of ecosystem partnership and resource scarcity and constraints were partially supported, and the moderation effects of customer and market demands and digital commitment were not supported or refuted.</div

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas