Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario

A Semantic Interoperability Model Based on the IEEE 1451 Family of Standards Applied to the Industry 4.0

The Internet of Things (IoT) has been growing recently. It is a concept for connecting billions of smart devices through the Internet in different scenarios. One area being developed inside the IoT in industrial automation, which covers Machine-to-Machine (M2M) and industrial communications with an automatic process, emerging the Industrial Internet of Things (IIoT) concept. Inside the IIoT is developing the concept of Industry 4.0 (I4.0). That represents the fourth industrial revolution and addresses the use of Internet technologies to improve the production efficiency of intelligent services in smart factories. I4.0 is composed of a combination of objects from the physical world and the digital world that offers dedicated functionality and flexibility inside and outside of an I4.0 network. The I4.0 is composed mainly of Cyber-Physical Systems (CPS). The CPS is the integration of the physical world and its digital world, i.e., the Digital Twin (DT). It is responsible for realising the intelligent cross-link application, which operates in a self-organised and decentralised manner, used by smart factories for value creation. An area where the CPS can be implemented in manufacturing production is developing the Cyber-Physical Production System (CPPS) concept. CPPS is the implementation of Industry 4.0 and CPS in manufacturing and production, crossing all levels of production between the autonomous and cooperative elements and sub-systems. It is responsible for connecting the virtual space with the physical world, allowing the smart factories to be more intelligent, resulting in better and smart production conditions, increasing productivity, production efficiency, and product quality. The big issue is connecting smart devices with different standards and protocols. About 40% of the benefits of the IoT cannot be achieved without interoperability. This thesis is focused on promoting the interoperability of smart devices (sensors and actuators) inside the IIoT under the I4.0 context. The IEEE 1451 is a family of standards developed to manage transducers. This standard reaches the syntactic level of interoperability inside Industry 4.0. However, Industry 4.0 requires a semantic level of communication not to exchange data ambiguously. A new semantic layer is proposed in this thesis allowing the IEEE 1451 standard to be a complete framework for communication inside the Industry 4.0 to provide an interoperable network interface with users and applications to collect and share the data from the industry field.A Internet das Coisas tem vindo a crescer recentemente. É um conceito que permite conectar bilhões de dispositivos inteligentes através da Internet em diferentes cenários. Uma área que está sendo desenvolvida dentro da Internet das Coisas é a automação industrial, que abrange a comunicação máquina com máquina no processo industrial de forma automática. Essa interligação, representa o conceito da Internet das Coisas Industrial. Dentro da Internet das Coisas Industrial está a desenvolver o conceito de Indústria 4.0 (I4.0). Isso representa a quarta revolução industrial que aborda o uso de tecnologias utilizadas na Internet para melhorar a eficiência da produção de serviços em fábricas inteligentes. A Indústria 4.0 é composta por uma combinação de objetos do mundo físico e do mundo da digital que oferece funcionalidade dedicada e flexibilidade dentro e fora de uma rede da Indústria 4.0. O I4.0 é composto principalmente por Sistemas Ciberfísicos. Os Sistemas Ciberfísicos permitem a integração do mundo físico com seu representante no mundo digital, por meio do Gémeo Digital. Sistemas Ciberfísicos são responsáveis por realizar a aplicação inteligente da ligação cruzada, que opera de forma auto-organizada e descentralizada, utilizada por fábricas inteligentes para criação de valor. Uma área em que o Sistema Ciberfísicos pode ser implementado na produção manufatureira, isso representa o desenvolvimento do conceito Sistemas de Produção Ciberfísicos. Esse sistema é a implementação da Indústria 4.0 e Sistema Ciberfísicos na fabricação e produção. A cruzar todos os níveis desde a produção entre os elementos e subsistemas autónomos e cooperativos. Ele é responsável por conectar o espaço virtual com o mundo físico, permitindo que as fábricas inteligentes sejam mais inteligentes, resultando em condições de produção melhores e inteligentes, aumentando a produtividade, a eficiência da produção e a qualidade do produto. A grande questão é como conectar dispositivos inteligentes com diferentes normas e protocolos. Cerca de 40% dos benefícios da Internet das Coisas não podem ser alcançados sem interoperabilidade. Esta tese está focada em promover a interoperabilidade de dispositivos inteligentes (sensores e atuadores) dentro da Internet das Coisas Industrial no contexto da Indústria 4.0. O IEEE 1451 é uma família de normas desenvolvidos para gerenciar transdutores. Esta norma alcança o nível sintático de interoperabilidade dentro de uma indústria 4.0. No entanto, a Indústria 4.0 requer um nível semântico de comunicação para não haver a trocar dados de forma ambígua. Uma nova camada semântica é proposta nesta tese permitindo que a família de normas IEEE 1451 seja um framework completo para comunicação dentro da Indústria 4.0. Permitindo fornecer uma interface de rede interoperável com utilizadores e aplicações para recolher e compartilhar os dados dentro de um ambiente industrial.This thesis was developed at the Measurement and Instrumentation Laboratory (IML) in the University of Beira Interior and supported by the portuguese project INDTECH 4.0 – Novas tecnologias para fabricação, que tem como objetivo geral a conceção e desenvolvimento de tecnologias inovadoras no contexto da Indústria 4.0/Factories of the Future (FoF), under the number POCI-01-0247-FEDER-026653

Unleash narrowband technologies for industrial Internet of Things services

As the industrial market grows, it is becoming noticeable that there are many industrial Internet of things (IIoT) use cases for which existing technology cannot meet the huge demand of machine connectivity. For example, in the utility market, there is a strong trend to adopt new technology that can support positive business use case scenarios for efficient system operation and elaborate the dramatic increase of the services demands. Apart from this, most utility grid applications required long-range, low-power, secure, and reliable communications, which means narrowband (NB) technology can be the dominant choice. To address these challenges, this article provides a new framework architecture to enable technical decision makers to plan for NB-IIoT. Moreover, we highlight the key aspects of NB technology by focusing on the challenges, standardization, and requirements to facilitate the IIoT connectivity for industry revolutions. The motivation behind employing NB is to provide a high level of reliability, and better quality of service, and coverage. In particular, the article addresses the main applications of utility use cases under the NB umbrella, which can perform as a good bridge between utility services and the fundamental communication infrastructure. The utility use cases based on emerging technology can support the full array of smart grid services that are required for both central and distributed operation systems. Finally, the article provides connectivity solutions for potential IIoT deployment aiming to define a new roadmap for NB technology on specific industrial use cases

Intelligent Embedded Vision for Summarization of Multi-View Videos in IIoT

Nowadays, video sensors are used on a large scale for various applications including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging to process such heterogeneous nature of Big Data in real time. Multi-view video summarization (MVS) enables us to suppress redundant data in distributed video sensors settings. The existing MVS approaches process video data in offline manner by transmitting it to the local or cloud server for analysis, which requires extra streaming to conduct summarization, huge bandwidth, and are not applicable for integration with industrial internet of things (IIoT). This paper presents a light-weight CNN and IIoT based computationally intelligent (CI) MVS framework. Our method uses an IIoT network containing smart devices, Raspberry Pi (clients and master) with embedded cameras to capture multi-view video (MVV) data. Each client Raspberry Pi (RPi) detects target in frames via light-weight CNN model, analyzes these targets for traffic and crowd density, and searches for suspicious objects to generate alert in the IIoT network. The frames of each client RPi are encoded and transmitted with approximately 17.02% smaller size of each frame to master RPi for final MVS. Empirical analysis shows that our proposed framework can be used in industrial environments for various applications such as security and smart transportation and can be proved beneficial for saving resources

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

Time series database in Industrial IoT and its testing tool

Abstract. In the essence of the Industrial Internet of Things is data gathering. Data is time and event-based and hence time series data is key concept in the Industrial Internet of Things, and specific time series database is required to process and store the data. Solution development and choosing the right time series database for Industrial Internet of Things solution can be difficult. Inefficient comparison of time series databases can lead to wrong choices and consequently to delays and financial losses. This thesis is improving the tools to compare different time series databases in context of the Industrial Internet of Things. In addition, the thesis identifies the functional and non-functional requirements of time series database in Industrial Internet of Things and designs and implements a performance test bench. A practical example of how time series databases can be compared with identified requirements and developed test bench is also provided. The example is used to examine how selected time series databases fulfill these requirements. Eight functional requirements and eight non-functional requirements were identified. Functional requirements included, e.g., aggregation support, information models, and hierarchical configurations. Non-functional requirements included, e.g., scalability, performance, and lifecycle. Developed test bench took Industrial Internet of Things point of view by testing the database in three scenarios: write heavy, read heavy, and concurrent write and read operations. In the practical example, ABB’s cpmPlus History, InfluxDB, and TimescaleDB were evaluated. Both requirement evaluation and performance testing resulted that cpmPlus History performed best, InfluxDB second best, and TimescaleDB the worst. cpmPlus History showed extensive support for the requirements and best performance in all performance test cases. InfluxDB showed high performance for data writing while TimescaleDB showed better performance for data reading.Aikasarjatietokanta teollisuuden esineiden internetissä ja sen testipenkki. Tiivistelmä. Teollisuuden esineiden internetin ytimessä on tiedon keruu. Tieto on aika ja tapahtuma pohjaista ja sen vuoksi aikasarjatieto on teollisuuden esineiden internetin avainkäsitteitä. Prosessoidakseen tällaista tietoa tarvitaan erityinen aikasarjatietokanta. Sovelluskehitys ja oikean aikasarjatietokannan valitseminen teollisuuden esineiden internetin ratkaisuun voi olla vaikeaa. Tehoton aikasarjatietokantojen vertailu voi johtaa vääriin valintoihin ja siten viiveisiin sekä taloudellisiin tappioihin. Tässä diplomityössä kehitetään työkaluja, joilla eri aikasarjatietokantoja teollisuuden esineiden internetin ympäristössä voidaan vertailla. Diplomityössä tunnistetaan toiminnalliset ja ei-toiminnalliset vaatimukset aikasarjatietokannalle teollisuuden esineiden internetissä ja suunnitellaan ja toteutetaan suorituskykytestipenkki aikasarjatietokannoille. Työ tarjoaa myös käytännön esimerkin kuinka aikasarjatietokantoja voidaan vertailla tunnistetuilla vaatimuksilla ja kehitetyllä testipenkillä. Esimerkkiä hyödynnetään tutkimuksessa, jossa selvitetään kuinka nykyiset aikasarjatietokannat täyttävät tunnistetut vaatimukset. Diplomityössä tunnistettiin kahdeksan toiminnallista ja kahdeksan ei-toiminnallista vaatimusta. Toiminnallisiin vaatimuksiin sisältyi mm. aggregoinnin tukeminen, informaatiomallit ja hierarkkiset konfiguraatiot. Ei-toiminnallisiin vaatimuksiin sisältyi mm. skaalautuvuus, suorituskyky ja elinkaari. Kehitetty testipenkki otti teollisuuden esineiden internetin näkökulman kolmella eri testiskenaariolla: kirjoituspainoitteinen, lukemispainoitteinen ja yhtäaikaiset kirjoitus- ja lukemisoperaatiot. Käytännön esimerkissä ABB:n cpmPlus History, InfluxDB ja TimescaleDB tietokannat olivat arvioitavina. Sekä vaatimusten arviointi että suorituskykytestit osoittivat cpmPlus History:n suoriutuvan parhaiten, InfluxDB:n toiseksi parhaiten ja TimescaleDB:n huonoiten. cpmPlus History tuki tunnistettuja vaatimuksia laajimmin ja tarjosi parhaan suorituskyvyn kaikissa testiskenaarioissa. InfluxDB antoi hyvän suorituskyvyn tiedon kirjoittamiselle, kun vastaavasti TimescaleDB osoitti parempaa suorituskykyä tiedon lukemisessa

Monitoring Of Remote Hydrocarbon Wells Using Azure Internet Of Things

Remote monitoring of hydrocarbon wells is a tedious and meticulously thought out task performed to create a cyber-physical bridge between the asset and the owner. There are many systems and techniques on the market that offer this solution but due to their lack of interoperability and/or decentralized architecture they begin to fall apart when remote assets become farther away from the client. This results in extreme latency and thus poor decision making. Microsoft\u27s Azure IoT Edge was the focus of this writing. Coupled with off-the-shelf hardware, Azure\u27s IoT Edge services were integrated with an existing unit simulating a remote hydrocarbon well. This combination successfully established a semi-autonomous IIoT Edge device that can monitor, process, store, and transfer data locally on the remote device itself. These capabilities were performed utilizing an edge computing architecture that drastically reduced infrastructure and pushed intelligence and responsibility to the source of the data. This application of Azure IoT Edge laid a foundation from which a plethora of solutions can be built, enhancing the intelligence capability of this asset. This study demonstrates edge computing\u27s ability to mitigate latency loops, reduce network stress, and handle intermittent connectivity. Further experimentation and analysis will have to be performed at a larger scale to determine if the resources implemented will suffice for production level operations

An integrative framework for cooperative production resources in smart manufacturing

Under the push of Industry 4.0 paradigm modern manufacturing companies are dealing with a significant digital transition, with the aim to better address the challenges posed by the growing complexity of globalized businesses (Hermann, Pentek, & Otto, Design principles for industrie 4.0 scenarios, 2016). One basic principle of this paradigm is that products, machines, systems and business are always connected to create an intelligent network along the entire factory\u2019s value chain. According to this vision, manufacturing resources are being transformed from monolithic entities into distributed components, which are loosely coupled and autonomous but nevertheless provided of the networking and connectivity capabilities enabled by the increasingly widespread Industrial Internet of Things technology. Under these conditions, they become capable of working together in a reliable and predictable manner, collaborating among themselves in a highly efficient way. Such a mechanism of synergistic collaboration is crucial for the correct evolution of any organization ranging from a multi-cellular organism to a complex modern manufacturing system (Moghaddam & Nof, 2017). Specifically of the last scenario, which is the field of our study, collaboration enables involved resources to exchange relevant information about the evolution of their context. These information can be in turn elaborated to make some decisions, and trigger some actions. In this way connected resources can modify their structure and configuration in response to specific business or operational variations (Alexopoulos, Makris, Xanthakis, Sipsas, & Chryssolouris, 2016). Such a model of \u201csocial\u201d and context-aware resources can contribute to the realization of a highly flexible, robust and responsive manufacturing system, which is an objective particularly relevant in the modern factories, as its inclusion in the scope of the priority research lines for the H2020 three-year period 2018-2020 can demonstrate (EFFRA, 2016). Interesting examples of these resources are self-organized logistics which can react to unexpected changes occurred in production or machines capable to predict failures on the basis of the contextual information and then trigger adjustments processes autonomously. This vision of collaborative and cooperative resources can be realized with the support of several studies in various fields ranging from information and communication technologies to artificial intelligence. An update state of the art highlights significant recent achievements that have been making these resources more intelligent and closer to the user needs. However, we are still far from an overall implementation of the vision, which is hindered by three major issues. The first one is the limited capability of a large part of the resources distributed within the shop floor to automatically interpret the exchanged information in a meaningful manner (semantic interoperability) (Atzori, Iera, & Morabito, 2010). This issue is mainly due to the high heterogeneity of data model formats adopted by the different resources used within the shop floor (Modoni, Doukas, Terkaj, Sacco, & Mourtzis, 2016). Another open issue is the lack of efficient methods to fully virtualize the physical resources (Rosen, von Wichert, Lo, & Bettenhausen, 2015), since only pairing physical resource with its digital counterpart that abstracts the complexity of the real world, it is possible to augment communication and collaboration capabilities of the physical component. The third issue is a side effect of the ongoing technological ICT evolutions affecting all the manufacturing companies and consists in the continuous growth of the number of threats and vulnerabilities, which can both jeopardize the cybersecurity of the overall manufacturing system (Wells, Camelio, Williams, & White, 2014). For this reason, aspects related with cyber-security should be considered at the early stage of the design of any ICT solution, in order to prevent potential threats and vulnerabilities. All three of the above mentioned open issues have been addressed in this research work with the aim to explore and identify a precise, secure and efficient model of collaboration among the production resources distributed within the shop floor. This document illustrates main outcomes of the research, focusing mainly on the Virtual Integrative Manufacturing Framework for resources Interaction (VICKI), a potential reference architecture for a middleware application enabling semantic-based cooperation among manufacturing resources. Specifically, this framework provides a technological and service-oriented infrastructure offering an event-driven mechanism that dynamically propagates the changing factors to the interested devices. The proposed system supports the coexistence and combination of physical components and their virtual counterparts in a network of interacting collaborative elements in constant connection, thus allowing to bring back the manufacturing system to a cooperative Cyber-physical Production System (CPPS) (Monostori, 2014). Within this network, the information coming from the productive chain can be promptly and seamlessly shared, distributed and understood by any actor operating in such a context. In order to overcome the problem of the limited interoperability among the connected resources, the framework leverages a common data model based on the Semantic Web technologies (SWT) (Berners-Lee, Hendler, & Lassila, 2001). The model provides a shared understanding on the vocabulary adopted by the distributed resources during their knowledge exchange. In this way, this model allows to integrate heterogeneous data streams into a coherent semantically enriched scheme that represents the evolution of the factory objects, their context and their smart reactions to all kind of situations. The semantic model is also machine-interpretable and re-usable. In addition to modeling, the virtualization of the overall manufacturing system is empowered by the adoption of an agent-based modeling, which contributes to hide and abstract the control functions complexity of the cooperating entities, thus providing the foundations to achieve a flexible and reconfigurable system. Finally, in order to mitigate the risk of internal and external attacks against the proposed infrastructure, it is explored the potential of a strategy based on the analysis and assessment of the manufacturing systems cyber-security aspects integrated into the context of the organization\u2019s business model. To test and validate the proposed framework, a demonstration scenarios has been identified, which are thought to represent different significant case studies of the factory\u2019s life cycle. To prove the correctness of the approach, the validation of an instance of the framework is carried out within a real case study. Moreover, as for data intensive systems such as the manufacturing system, the quality of service (QoS) requirements in terms of latency, efficiency, and scalability are stringent, an evaluation of these requirements is needed in a real case study by means of a defined benchmark, thus showing the impact of the data storage, of the connected resources and of their requests