scholarly journals OPAL—The Toolbox for the Integration and Analysis of IoT in a Semantically Annotated Way

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4002
Author(s):  
Philipp Hertweck ◽  
Tobias Hellmund ◽  
Jürgen Moßgraber
Keyword(s):  
Data Storage ◽  
Heterogeneous Data ◽  
Software Components ◽  
Industrial Internet Of Things ◽  
Digital Services ◽  
Industrial Internet ◽  
Broad Variety ◽  
Execution Environment ◽  
Access Data ◽  
Software Toolbox

Industrial Internet of Things (IIoT) applications are being used more and more frequently. Data collected by various sensors can be used to provide innovative digital services supporting increasing efficiency or cost reduction. The implementation of such applications requires the integration and analysis of heterogeneous data coming from a broad variety of sensors. To support these steps, this paper introduces OPAL, a software toolbox consolidating several software components for the semantically annotated integration and analysis of IoT-data. Data storage is realized in a standardized and INSPIRE-compliant way utilizing the SensorThings API. Supporting a broad variety of use cases, OPAL provides several import adapters to access data sources with various protocols (e.g., the OPC UA protocol, which is often used in industrial environments). In addition, a unified management and execution environment, called PERMA, is introduced to allow the programming language independent integration of algorithms.

scholarly journals Blockchain-Based Data Storage Mechanism for Industrial Internet of Things

2020 ◽  
Vol 26 (5) ◽  
pp. 1157-1172
Author(s):  
Jin Wang ◽  
Wencheng Chen ◽  
Lei Wang ◽  
Yongjun Ren ◽  
R. Simon Sherratt
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Industrial Internet Of Things ◽  
Storage Mechanism ◽  
Industrial Internet

scholarly journals Process-Driven and Flow-Based Processing of Industrial Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5245
Author(s):  
Klaus Kammerer ◽  
Rüdiger Pryss ◽  
Burkhard Hoppenstedt ◽  
Kevin Sommer ◽  
Manfred Reichert
Keyword(s):  
Sensor Data ◽  
Technical Solution ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Capture Process ◽  
Processing Pipeline ◽  
Digital Services ◽  
Industrial Internet ◽  
Sensor Processing ◽  
Recorded Data

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.

scholarly journals Mitigating Self-Heating in Solid State Drives for Industrial Internet-of-Things Edge Gateways

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1179
Author(s):  
Cristian Zambelli ◽  
Lorenzo Zuolo ◽  
Luca Crippa ◽  
Rino Micheloni ◽  
Piero Olivo
Keyword(s):  
Solid State ◽  
Internet Of Things ◽  
Data Storage ◽  
Storage Temperature ◽  
Nand Flash ◽  
Solid State Drives ◽  
Industrial Internet Of Things ◽  
Self Heating ◽  
Industrial Internet ◽  
Protection Mechanisms

Data storage in the Industrial Internet-of-Things scenario presents critical aspects related to the necessity of bringing storage devices closer to the point where data are captured. Concerns on storage temperature are to be considered especially when Solid State Drives (SSD) based on 3D NAND Flash technology are part of edge gateway architectures. Indeed, self-heating effects caused by oppressive storage demands combined with harsh environmental conditions call for proper handling at multiple abstraction levels to minimize severe performance slow downs and reliability threats. In this work, with the help of a SSD co-simulation environment that is stimulated within a realistic Industrial Internet-of-Things (IIoT) workload, we explore a methodology orthogonal to performance throttling that can be applied in synergy with the operating system of the host. Results evidenced that by leveraging on the SSD micro-architectural parameters of the queuing system it is possible to reduce the Input/Output operations Per Second (IOPS) penalty due to temperature protection mechanisms with minimum effort by the system. The methodology presented in this work opens further optimization tasks and algorithmic refinements for SSD and system designers not only in the IIoT market segment, but generally in all areas where storage power consumption is a concern.

Domestic Engineering - Industry 4.0 Technology Transition Problems

2019 ◽  
pp. 11-20
Author(s):  
E. N. Lapteva ◽  
O. V. Nasarochkina
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Technology Development ◽  
Engineering Industry ◽  
Problem Analysis ◽  
Industrial Internet Of Things ◽  
Technology Transition ◽  
Industrial Internet ◽  
Qualified Personnel ◽  
Virtual And Augmented Reality

The paper deals with problem analysis due to domestic engineering transition to the Industry 4.0 technology. It presents such innovative technologies as additive manufacturing (3D-printing), Industrial Internet of Things, total digitization of manufacturing (digital description of products and processes, virtual and augmented reality). Among the main highlighted problems the authors include a lack of unification and standardization at this stage of technology development; incompleteness of both domestic and international regulatory framework; shortage of qualified personnel.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

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