IIoT Data Ness: From Streaming to Added Value

In the emerging Industry 4.0 paradigm, the internet of things has been an innovation driver, allowing for environment visibility and control through sensor data analysis. However the data is of such volume and velocity that data quality cannot be assured by conventional architectures. It has been argued that the quality and observability of data are key to a project’s success, allowing users to interact with data more effectively and rapidly. In order for a project to become successful in this context, it is of imperative importance to incorporate data quality mechanisms in order to extract the most value out of data. If this goal is achieved one can expect enormous advantages that could lead to financial and innovation gains for the industry. To cope with this reality, this work presents a data mesh oriented methodology based on the state-of-the-art data management tools that exist to design a solution which leverages data quality in the Industrial Internet of Things (IIoT) space, through data contextualization. In order to achieve this goal, practices such as FAIR data principles and data observability concepts were incorporated into the solution. The result of this work allowed for the creation of an architecture that focuses on data and metadata management to elevate data context, ownership and quality.O conceito de Internet of Things (IoT) é um dos principais fatores de sucesso para a nova Indústria 4.0. Através de análise de dados sobre os valores que os sensores coletam no seu ambiente, é possível a construção uma plataforma capaz de identificar condições de sucesso e eventuais problemas antes que estes ocorram, resultando em ganho monetário relevante para as empresas. No entanto, este caso de uso não é de fácil implementação, devido à elevada quantidade e velocidade de dados proveniente de um ambiente de IIoT (Industrial Internet of Things)

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

PIS: IoT & Industry 4.0 Challenges

International audienceIn the era of Industry 4.0, digital manufacturing is evolving into smart manufacturing. This evolution impacts companies in three main areas: organization, people, and technologies. This chapter analyzes the Internet of Things (IoT) and Cyber-Physical Systems (CPS)—key technologies transforming the physical world into a digitalized physical world. IoT and CPS provide factories with sensing capabilities, perform data and context capture and allow them to act/react to optimize the value chain. We survey the recent state-of-the-art development of the Industrial Internet of Things (IIoT)—also known as IoT and CPS in the context of Industry 4.0, from a protocol, architecture, and standard point-of-view. We also explore key challenges and future research directions for extensive industrial adoption of these technologies

A cost estimation approach for IoT modular architectures implementation in legacy systems

Industry 4.0 has encouraged manufacturing organisations to update their systems and processes by implementing Internet of Things (IoT) technology in legacy systems to provide new services such as autonomous condition monitoring and remote maintenance. However, there is still no literature that guides in realizing the advantages and disadvantages of the fourth industry revolution in terms of complexity, data security, and cost. This paper lays the foundation for the creation of an innovative conceptual model to estimate the cost for implementation of new architectures for legacy systems. The proposed approach considers aspects that impact the cost of different IoT architectures such as: complexity, data gathering and sharing protocols, and cyber security. The authors suggest a further implementation of the cost model, in order to guide the organisations in the most cost-effective architecture for modernisation of their legacy systems

Arquitectura Inteligente CPPS Integrada en el Uso de la Norma IEC-61499, con Bloque de Funciones Altamente Adaptables en la Industria 4.0

Today, we are experiencing what it is being labelled as the Fourth Industrial Revolution (Industry 4.0) in terms of automation and control systems of cyber - physical production environments. These systems not only allow access to many innovative features based on network connections, but they also provide access to the world of the Internet of Things (IoT).  It is in this context that IoT changes the ways to link new technologies in order to obtain more efficient, intelligent, flexible and adaptable production systems; thus becoming an interdependence of the product itself that the companies wish to commercialize. Cyber- Physical Production Systems (CPPS) have the advantages of granular communications, common electric bandwidth for all users regardless of data speeds, compatibility in connection with free or guided space communication links and with the major compatibility with intensity modulation. IEC 61499 is generally based on a generic architecture, with specific software requirements, development rules that allow for portability and device configuration.Keywords: IoT, configuration, communication, CPPS, Industry 4.0, smart

Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Retrofitting of legacy machines in the context of Industrial Internet of Things (IIoT)

In the context of Industry 4.0 (I 4.0), one of the most important aspects is data, followed by the capital required to deploy advanced technologies. However, most Small and Medium Enterprises (SMEs) are neither data ready nor have the capital to upgrade their existing machinery. In SMEs, most of the legacy machines do not have data gathering capabilities. In this scenario, the concept of retrofitting the existing machinery with sensors and building an Industrial Internet of Things (IIoT) is more beneficial than upgrading the equipment to newer machinery. The current research paper proposes a simple architecture on retrofitting a legacy machine with external sensors for data collection and feeding the cloud-based databases for analysis/monitoring purposes. The design and functional aspects of the architecture are then tested in a laboratory environment on a drilling machine with no embedded sensors. Data related to the speed of the drill head and the bore depth are collected using newly retrofitted sensors to validate the proposed architecture

mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0

In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x