Study Case in Mining Industry: Monitoring Rollers using Embedded LoRaWan

Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, and communicates with heterogeneous devices, is important to industrial sites and is now indispensable. To ensure the fairness and quality of the IIoT with limited network resources, the network connection of the IIoT needs to be constructed more intelligently. Many studies are being conducted on the efficient use of the resources that are imposed on IIoT devices. Therefore, in this paper, we propose a collaboration optimization method for heterogeneous devices that is based on cloud–fog–edge architecture. First, this paper proposes a knowledge distillation-based algorithm that can collaborate on cloud–fog–edge computing on the basis of distributed control. Second, to compensate for the shortcomings of knowledge distillation, we propose a framework for combining a soft-label-based alarm level. Finally, the method that is proposed in this paper was verified through several experiments, and it is shown that this method can effectively shorten the response time and solve the problems of existing IIoT networks, and that it can be efficiently applied to heterogeneous devices.

Intelligent manufacturing is the future development direction of manufacturing industry. There are still some problems lurk in the traditional mode of production, such as “information island” and “network security”, which result in low productivity but high-cost production, and information leakage. With application of the new generation information technology in manufacturing field is expanded, the deep integration of information technology and manufacturing industry promotes the development of production towards intellectualization, networking, and service. Single mode of production is gradually replaced by the way of large-scale collaborative production, so the traditional industrial internet of things (IoT) and manufacturing architecture are not suitable for the needs of actual production any more. For instance, the great challenges of compatibility and coordination of interface protocols, huge data, cost, and safety. In order to solve these problems, a new model of industrial IoT with security mechanism used in intelligent workshop is proposed. Firstly, this article analyzes the current situation of manufacturing industry and information technology, a secure transmission model of industrial IoT was designed based on the new generation information technology. Secondly, combined with the structure of the IoT and the real production demands, a six-tier industrial IoT architecture based on the new generation information technology is proposed. Finally, the three dimensions of the intelligent manufacturing model are expanded according to the architecture of the IoT. Not only a six-tier intelligent manufacturing system framework based on the new generation of information technology is obtained, but also the network security mechanism is introduced. On one hand, it realizes the coordinated operation of industrial chain, supply chain, and value chain of production process, on the other hand, it also improves the networking, intellectualization, and service level of industrial production. What is more the system can also provide the reference model for the various application of IoT.

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

This paper presents a study on the use of industrial internet of things (IIoT), the use of IIot in the current Brazilian industry context, its basic differences from internet of things (IoT) and its expansion possibilities pointing out some challenges related to a new approach within the industry. The complex interconnection which is made possible through the IIoT is able to optimise resources and reduce exponentially the costs of production processes in most stages and is gradually changing the direction of society in labour relations. These advances in manufacturing processes are feasible as the internet of things is not simply inserting intelligence in equipment, but allowing interconnection, reconfiguring functions and anticipating loss of productivity or failures that might occur in real-time. Within this context, the IIoT can be understood as a broad and complex concept that encompasses asset and performance management areas, availability of increased data and intelligent corporate.

Introduction. Industry 4.0 is a concept that is considered a new phase in the Industrial Revolution, closely related to the application of information technologies and the digital transformation of manufacturing. The main purpose is to be created a more holistic and more connected ecosystem, focused on supply chain management in industrial companies. Implementation of solutions in Industry 4.0 is mostly related to the concept of the Internet of Things (IoT). Mass deployment of this type of technology in industrial enterprises is the basis of the so-called Industrial Internet of Things (IIoT). Achieving interoperability in the IIoT requires the combination of two technologies: the Internet of Things and the Internet of People.
 Aim and tasks. This article describes the implementation of the concept of the Internet of Things in industrial enterprises, as a key technology factor for developing Industry 4.0.
 Results. A brief overview of the evolution of industrial production - from the beginning of the Industrial Revolution to the emergence of Industry 4.0 is made. The main principles for implementing Industry 4.0 solutions ensure that the entire production process is computerized. Industry 4.0 solutions are mostly associated with the concept of the Internet of Things (IoT) whose definition and essence are obtained in this article. Based on the various concepts of the IoT are presented solutions that can be used in the industry, namely: in consumer devices in technology used in public organizations in infrastructure applications in industrial applications, also called the Industrial Internet of Things (IIoT). Therefore, we can say that there is a significant potential for improving production processes as regards: optimization of operations, forecasting equipment support, inventory optimization, improving workers' security, shipping chain optimization, etc.
 Conclusions. The application of the Internet of Things in enterprises is an important and decisive step in the process of their digital transformation and transition to Industry 4.0. The interaction between humans and machines, carried out through Internet technologies, leads to the emergence of the Internet of Everything, which will be a basic concept in industrial production in the coming years. However, the role of man in the production process should not be completely eliminated, but solutions should be sought that support and intellectualize his work.

The growing requirement for the rise of production, quality, and efficiency of industrial products drives humans to jointly develop novel technologies capable of following up with the exponential technology growth they are facing today in production processes. In the 21st century, industrial internet of things (IIoT) is dedicated to endorsing the internet of things (IoT) to allow the interconnection of anywhere, any time, and anything to enhance efficiency, productivity, intelligence, and safety in the manufacturing system. Consequently, the combined IoT and cyber physical system (CPS) in IIoT will have an influence on industrial techniques through the industrial 4.0 confluence to encourage connected factories. CPS effectively combines physical and cyber components employing modern components such as sensors, network, and computing technologies. But various securities challenges rising in the CPS, like increasing cyberattacks due to the increase in IoT devices, modelling of security threads, CPS vulnerabilities assessment development techniques, and designing of fault-tolerant architectures, motivate new technologies to meet CPS requirements in order to offer security, confidentiality, and reliability of users’ data. Thus, this chapter presents the classification of various existing studies based on CPS security in terms of IIoT applications. Also, it provides the limitations, shortcomings, CPS architectures, and future potential applications to help researchers in the context of practical work.

The fundamental process of predictive maintenance is prognostics and health management, and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment. A new data-driven predictive maintenance and an architectural impulse, based on a regularized deep neural network using predictive analytics, are proposed successfully for ring spinning technology. The paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages. The sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them. We established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning system. Results show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of data. This integrated system aims to prognosticate abnormalities, disturbances, and failures by providing condition-based monitoring for each component, which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network. A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic, high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.

Failure analysis of the rubber-textile conveyor belts using classification models

Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.

Of all the causes of accidents to ships, 14% pertains to damage due to ship equipment. Accordingly, the maritime industry is currently considering state-of-the-art maintenance and inspection processes, an example of which is condition-based maintenance (CBM). This is a strategy that hinges on the condition monitoring (CM) of assets. CM has proven to increase efficiency, reliability, profitability, and performance of vessel. To enable this maintenance strategy, sensors need to be installed along the most critical ship components and around the environment where these assets are operating through the application of Internet of Ships (IoS). IoS has demonstrated to be effective for collecting data in real time as well as performing diagnosis and prognosis to assess the current and future health of machinery to assist instant decision-making. The employment of IoS presents several challenges, an example of which is the imputation of missing values. Data imputation is a compelling preprocessing step, the aim of this is to estimate identified missing values to avoid underutilization of data. This data preparation step has gained popularity over the last few years due to its importance when dealing with Industrial Internet of Things (IIoT) sensor data. Although some articles presented new methodologies to impute missing values from sensor data of marine machinery based on machine learning methodologies, deep learning models have not yet been considered. For this reason, variational autoencoders for imputing missing values from sensor data of marine systems are analyzed in this article. To assess the performance of variational autoencoders as imputation methods, a comparative study is performed with widely implemented imputation techniques. Mean imputation, Forward Fill and Backward Fill, and k-Nearest Neighbors are considered. To that end, a case study on marine machinery system parameters obtained from sensors installed on a diesel generator of a tanker ship is performed. Results demonstrate the applicability of variational autoencoders when dealing with missing values of marine machinery systems sensor data, achieving a coefficient of determination of 0.99 when imputing missing values of the diesel generator power parameter.

Leveraging patent classification based on deep learning: The case study on smart cities and industrial Internet of Things

In this rapidly changing world, a new buzzword has lodged in our agenda for the last five years: Industry 4.0. A wide variety of definitions have been provided in order to specify this notion, but simply, Industry 4.0 could be defined as the adoption of automation and data exchange in manufacturing. When it comes to how to provide this automation and data flow, the Internet of things (IoT) stands at the centre of the system. It is possible to describe the IoT as the intelligent connectivity of smart devices where each item in the system can reach out and sense one another so they can provide decisions about our physical world. The intersection of the IoT and Industrial 4.0 composes an area of computational technology that is called the Industrial Internet of Things (IIoT), and it is applicable for industries to deal with connectivity for each element in the system. The IIoT increases efficiency and productivity while minimizing human-based errors in business processes. Companies can provide effective reporting by integrating this technology with their internal systems By creating an interface between devices connected to the Internet, the applicability of the data collected by IoT will increase and more flexibility and agility will be provided.Besides the applications in the manufacturing sector, the oil and gas, petrochemical, logistics, mining, aerospace, and pharmaceutical sectors also benefit from the IIoT since it provides real-time monitoring and data collection. The IoT continues to evolve and signals that business processes and operations in the industry will be smarter and more efficient in the future.

The ‘Internet of Things’ (IoT) is at times mythologized and its purpose mistaken, and often people can become confused about what it means, does or aims to achieve; moreover, without a financial appraisal of the differences IoT can make to their own enterprise, business leaders may be content to watch and wait rather than to take a lead. This issue of Textile Progress aims to provide a guide to help navigation beyond general statements about IoT and to help those involved with the textile industry to make an informed decision about its potential value to them. The definitions of the Internet of Things (IoT) available in the literature, and those put forth by organizations working on IoT standards development, are reviewed along with its architecture, elements, standards and protocols to help provide an understanding of the concepts and goals of IoT. A broad overview of impediments challenging the progress of IoT, especially in relation to cybersecurity, is provided. This review also compares ongoing work in the application of IoT in the textile industry to that in other manufacturing sectors whilst focussing on the interpretation of IoT technologies and their potential application to the textile industry according to both technological and business perspectives. A specific case study for the spinning industry is conducted to help with evaluation of its IoT solutions and to help to address how other parts of the textile industry might benefit from its application. The case study includes evaluation of IoT solutions in the spinning industry spanning the period between ITMA 2011 to 2019, and takes information from interviews with industry executives to inform future directions of IoT in the spinning industry.

When IoT meets Industry 4.0, we talk about Industrial Internet of Things (IIoT) or the use of typical Internet of Things (IoT) solutions in the industrial sector, integrating different technologies such as machine learning, big data and analytics, sensor data, Machine to Machine communication (M2M) and automation. Its application within the fourth industrial revolution has initiated a profound change within companies making them increasingly connected and exploiting data to optimize their production processes. IIoT is revolutionizing sectors such as manufacturing, automotive and healthcare. Collecting and analyzing all the data coming from the production sensors and drawing the information for your business through business analytics tools is the new key to competitiveness.

A number of heavy oil and oil sand reservoirs in Alberta have been successfully produced by natural depletion at rates far in excess of the predictions based on radial Darcy flow. Production rates can exceed 20 m3/d and con be maintained at such a high level for several years. What exactly causes the high primary production rates is not known, but several mechanisms on the primary production of heavy oil and bitumen have been proposed by various authors. These include solution gas drive, influx of bottomwater, sand production, wormholes, fractures and channels. Laboratory measurements which have been reported on the rheological properties of bitumen indicate that at low temperatures (e.g. 20 °Cj a mixture of bitumen or heavy oil with a small amount of sand behaves as a non-Newtonian fluid. The flow characteristics of a non Newtonian fluid in a reservoir and their effects on the primary production of heavy oil have been investigated in this study using ana/ysica/ solutions of the diffusivity equation. It was found that di/atant (shear thickening) effects can provide an explanation for low apparent viscosities at a distance from the wellbore where shear rates are low. This mechanism is consistent with the pressure buildup test results and with field production data where high production rates can be sustained over an extended period of time. Introduction A number of heavy oil and oil sand reservoirs in Alberta and Saskatchewan have been successfully produced by natural depletion. Primary recovery at the Lloydminster heavy oil deposit, which began over 40 years ago, has been significantly higher than the predictions based on radical Darcy flow. The heavy oil deposits in this area are relatively thin (3 m to 6 m) and occurring at depths from 300 m to 1000 m. The formations consist of unconsolidated sands with a relatively high porosity (29% to 35%) and high permeability (3500 mD). The oil gravity is quite low ranging from 10 to 20 degrees API with an oil viscosity of 400 mPa.s to 2000 mPa.s at initial reservoir conditions. Since 1985, Amoco have been producing significant amounts of bitumen from the Lindbergh Elk Point(1) area on primary production. The Clearwater formation at this location is 25 m to 30 m thick and consists of a moderately well sorted sand. The bitumen has a gravity of about 12 degrees API and a very high viscosity of 60 000 mPa.s at reservoir conditions (3900 kPa and 2O °C). The primary production rates average 15 m3/day 00 20 m3/day for over 3 years. These production rates are greater than can be predicted with radial Darcy flow calculations by a factor of more than 30. Potential Mechanisms It is not known what exactly causes these high primary production rates but a number of potential mechanisms have been suggested. in the literature: solution gas drive; influx of bottomwater; sand production; wormholes, fractures and channels; and rock compressibility. Smith(2) suggested that a possible production mechanism could be solution gas drive which was assisted occasionally by the influx of water.