scholarly journals Complete Automation of an Energy Consumption Reduction Strategy from a Water Treatment and Distribution Facility, Inside an Industrial Internet of Things-Compliant Proactive Historian Application

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2569
Author(s):  
Andrei Nicolae ◽  
Adrian Korodi ◽  
Ioan Silea
Keyword(s):  
Energy Consumption ◽  
Water Treatment ◽  
Internet Of Things ◽  
Information Exchange ◽  
Software Tool ◽  
Industrial Internet Of Things ◽  
External Interference ◽  
Industrial Landscape ◽  
Industrial Internet ◽  
Complete Automation

The Industrial Internet of Things and Industry 4.0 paradigms are steering the industrial landscape towards better connected entities, superior interoperability and information exchange, which lays the basis for developing more intelligent solutions that are already starting to bring numerous benefits. The current research aligns to this course, in an attempt to build an automated and autonomous software tool, capable of reducing the energy consumption of a water treatment and distribution facility, by optimizing the water sources usage. Based on several previous researches, the present paper details both the complete automation of the optimizing strategy inside a proactive historian application and the tests executed with the finished solution. Possessing the abilities to directly influence the monitored system in a non-invasive manner, and to link all the sequences of the algorithm automatically, the solution is now ready for long-term functioning without any external interference.

scholarly journals Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 282 ◽  
Author(s):  
Adrian Korodi ◽  
Ruben Crisan ◽  
Andrei Nicolae ◽  
Ioan Silea
Keyword(s):  
Drinking Water ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Non Invasive ◽  
Data Accumulation ◽  
Industrial Internet ◽  
Reduce Energy Consumption ◽  
And Control

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

scholarly journals Automated and Controlled Data Collection Using Industrial IoT System for Smart Maintenance

2021 ◽  
Vol 15 (3) ◽  
pp. 401-409
Author(s):  
Martin Curman ◽  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Tihomir Opetuk
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Information Exchange ◽  
Industrial Internet Of Things ◽  
Collection System ◽  
Automated Data Collection ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Potential Improvement ◽  
Internet Of Things Technology

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.

scholarly journals Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian You ◽  
Bing Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Industrial Internet ◽  
Task Offloading

AbstractAs a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

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

Multi-source fusion for weak target images in the Industrial Internet of Things

2021 ◽  
Vol 173 ◽  
pp. 150-159
Author(s):  
Keming Mao ◽  
Gautam Srivastava ◽  
Reza M. Parizi ◽  
Mohammad S. Khan
Keyword(s):  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Weak Target
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