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Journal articles on the topic 'Industrial automation'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 January 2025

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1

Khaire, Prof Prajkta. "Industrial Plant Automation." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 623–31. http://dx.doi.org/10.22214/ijraset.2020.5097.

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2

Navale, Shreyas. "Industrial IoT Automation." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 2848–57. http://dx.doi.org/10.22214/ijraset.2024.60368.

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Abstract: The IoT Industrial Automation System, incorporating a MERN (MongoDB, Express.js, React, Node.js) website with REST API implementation, represents a transformative leap in the realm of industrial automation. This innovative system leverages the power of the internet to remotely monitor and control sensor data and relay actuators. The NodeMCU ESP8266 hardware, complemented by the DHT11 sensor for temperature and humidity measurements, and the BMP180 sensor for atmospheric pressure and altitude data, form the backbone of this comprehensive data acquisition system. In addition, a 4- channel relay actuator enables real-time control and responsiveness within the industrial setting.

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3

Beran, Jan. "Virtual automation network challenge in industrial automation." IFAC Proceedings Volumes 39, no. 21 (February 2006): 473–78. http://dx.doi.org/10.1016/s1474-6670(17)30234-3.

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4

Dr Kannappan S, Kanchana K, Haritha B, Bhavana K, Madhu Priya T, and Lokesh G. "Iot based industrial automation." South Asian Journal of Engineering and Technology 12, no. 3 (July 8, 2022): 9–19. http://dx.doi.org/10.26524/sajet.2022.12.34.

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The number of industries in a certain region is increasing year by year due to the increasing needs of people, and therefore intelligent industrialization automation can be used. Here the air quality in the industry and the number of products manufactured in the industry are monitored using gas detectors (to detect toxic or toxic gases) and IR sensors (to count the number of products manufactured) where there is no need for labor. Also to see estimated time of arrival of delivery vehicle GPS is used to get latitude, longitude and estimated time of delivery vehicle in real time. The branch of the delivery vehicle is displayed via the APP that can be built with Android Studio, this GPS is integrated in the NODE MCU. In the case of industrial fire accidents, this can be detected using the industry existing flame sensors built into the NODE MCU and the LCD screen where sprinklers are later activated.

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5

Mamodiya, Udit, and Priyanka Sharma. "Review in Industrial Automation." IOSR Journal of Electrical and Electronics Engineering 9, no. 3 (2014): 33–38. http://dx.doi.org/10.9790/1676-09343338.

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6

Chandrasekaran, M., R. Saravana Saran, and J. Yasararafath. "RTOS Based Industrial Automation." i-manager's Journal on Embedded Systems 3, no. 2 (July 15, 2014): 15–19. http://dx.doi.org/10.26634/jes.3.2.3204.

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7

Chandrasekaran, M., R. Saravana Saran, and J. Yasararafath. "RTOS Based Industrial Automation." i-manager's Journal on Embedded Systems 3, no. 2 (July 15, 2014): 15–19. http://dx.doi.org/10.26634/jit.3.2.3204.

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8

Davies, S. "Winning combination [industrial automation]." Engineering & Technology 1, no. 2 (May 1, 2006): 46–48. http://dx.doi.org/10.1049/et:20060206.

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9

Aristova, N. I. "Intelligence in industrial automation." Automation and Remote Control 77, no. 6 (June 2016): 1071–76. http://dx.doi.org/10.1134/s0005117916060102.

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10

Segee, B. E., and Kevin S. LeBlanc. "Industrial automation using OLE." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 435. http://dx.doi.org/10.1016/s0920-5489(99)90880-2.

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11

Murugesan, R. "Evolution of industrial automation." International Journal of Computer Applications in Technology 25, no. 4 (2006): 169. http://dx.doi.org/10.1504/ijcat.2006.009389.

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12

Raj, C. Mohan, M. Sundaram, and M. Anand. "Automation of industrial machinerie." Scientific Temper 14, no. 02 (June 6, 2023): 500–503. http://dx.doi.org/10.58414/scientifictemper.2023.14.2.41.

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In past two decades, the automation of industrial machines is proliferating. Fourth industrial revolution (Industry 4.0) also focuses on industrial automation and artificial intelligence. As every technology has its ebb and flow, industrial automation also has huge scope in future and its own limitations. One of the major issues is in troubleshooting the virtually controlled machine. The biscuit making and baking machines are controlled using PLC I/O and HMI. The errors are rectified by taking feedback from machine’s component connections and supply connections. This helps us in troubleshooting the faults occurring in the connections and components used.

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13

Ayadi, Rami, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Hanan Aljuaid, Nadir O. Hamed, and Moaiad A. Khder. "Deep Learning–Based Soft Sensors for Improving the Flexibility for Automation of Industry." Wireless Communications and Mobile Computing 2022 (April 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/5450473.

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Automation in industries offers the benefits of enhancing quality and productivity while minimizing waste and errors, raising safety and adds stability to the production process. Industrial automation offers high profitability, reliability, and safety. It is beneficial to employ machine learning in the field of industrial automation as it helps in monitoring and performing maintenance on industrial machinery. Rational industrial development is closely associated with efforts for automating industrial techniques in all existing ways. Latest improvements in the automation of industrial systems resulted in decrease in cost of energy consumption and hardware. The proposed system is dealt with deep learning–based soft sensors for automation of industrial processes. The eminent benefits of soft sensors are versatility, flexibility, and low cost. With deep learning, many number of features could be processed. Thus, deep learning–based soft sensor encapsulates the above benefits. Soft sensors offer another way for the measurement of process variables, which are measured offline. Deep learning techniques are famous in the design of soft sensors for tough nonlinear systems due to the robustness and accuracy. The work depicted here designs a soft sensor based on deep learning algorithm for automation of industry. In the proposed system, a soft sensor contemplated on deep learning such as the deep neural network (DNN) is presented. The application of deep learning–based soft sensors in the automation of some industrial processes is also discussed here. The proposed system is tested on automatic control on solar power plants and in the measurement of reactive energy in industries. It was found that the proposed system yielded better results with its application in the automated industrial processes.

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14

Barbosa, Raul, João Fonseca, Marco Araújo, and Daniel Corujo. "Vinia: Voice-enabled intent-based networking for industrial automation." Computer Science and Information Systems, no. 00 (2024): 2. http://dx.doi.org/10.2298/csis230213002b.

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Intent Based Networking (IBN) is a promising approach for automating and managing large and complex networks. Integrating Voice-enabled Virtual Assistants (VVAs) with IBN and Software Defined Networking (SDN) has improved network management efficiency and flexibility. However, there is still room for optimization improvement in installing intents in industrial scenarios. Network Orchestration Automation plays an important role within the Beyond 5G and 6G Networks, considering existing practices for orchestrating 5G Network Functions. This work presents an extended preliminary architecture for a voice-enabled IBN system called VINIA for industrial network automation. The new approach allows the configuration of more network assets (e.g., 5G networks), leveraging Network Orchestrators and Network Slice Managers, thus improving the system?s capabilities. The results provide insights into this solution?s potential benefits and limitations to enhance the automation of industrial networks? management and orchestration procedures.

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15

Wu, Q. H., D. P. Buse, J. Q. Feng, P. Sun, and J. Fitch. "e-Automation, an architecture for distributed industrial automation systems." International Journal of Automation and Computing 1, no. 1 (October 2004): 17–25. http://dx.doi.org/10.1007/s11633-004-0017-6.

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16

Ionel, Danut Sorin, and Constantin Gheorghe Opran. "TRANSFORMING STRATEGY FROM INDUSTRIAL AUTOMATION TO ADVANCED LEAN AUTOMATION." International Journal of Modern Manufacturing Technologies 14, no. 2 (December 20, 2022): 83–89. http://dx.doi.org/10.54684/ijmmt.2022.14.2.83.

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Industrial automation refers to the control of machines and processes in order to manage variables. Automated manufacturing systems have evolved as advanced technologies have been incorporated, and theoretical approaches have evolved from mass production to intelligent manufacturing, each step introducing superior manufacturing concepts and models, which allowed increasing processes, organization and work. One of the newest approaches refers to distributive automation, in which the system allows reconfiguration and self-organization of autonomous subsystems in a distribute environment. By incorporating integrative Industry 4.0 technologies, an automated industrial system becomes an intelligent, fully digitized and reconfigurable manufacturing applications platform. At the same time, through digitization and virtualization, the automated manufacturing system can employ the Advanced Lean Manufacturing conceptual support. This paper addresses the transforming challenges of an existing automated manufacturing system to the Advanced Lean adaptive automated manufacturing level, taking into account the limits due to attributes, properties and capabilities of physical manufacturing assets. The proposed solution describes a versatile and dynamic architecture, which allows the organization / reorganization of manufacturing flows, scalability and connection with the external environment, according to advanced cyber manufacturing requests.

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17

Alfarhan, Eng Ahmad Abdulhamid, and Housam Ghanim Alhazeem. "The Impact of Technology on Industrial Process Automation." International Journal of Engineering Research and Applications 14, no. 12 (December 2024): 20–27. https://doi.org/10.9790/9622-14122027.

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This article explores the transformative role of technology in automating industrial processes. By leveraging advanced systems such as Artificial Intelligence (AI), Internet of Things (IoT), and Programmable Logic Controllers (PLCs), industries are achieving unprecedented levels of efficiency, precision, and operational safety. This paper provides a comprehensive overview of the key technologies, their applications, benefits, challenges, and potential future developments in industrial automation

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18

Ragavan, S. Veera, Velappa Ganapathy, and Ibrahim Kusumah Kusnanto. "Rapid Automation Application Deployment Framework for Real Time Process and Industrial Automation Systems." International Journal of Computer Theory and Engineering 6, no. 6 (December 2014): 515–20. http://dx.doi.org/10.7763/ijcte.2014.v6.920.

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19

Patil, Prof Mrs S. S. "Industrial Plant Automation Using SCADA." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 911–15. http://dx.doi.org/10.22214/ijraset.2018.4155.

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20

TAVAS, Bekir. "ROBOTIC PROCESS AUTOMATION: INDUSTRIAL MODEL." International Journal of Disciplines In Economics and Administrative Sciences Studies (IDEAstudies) 7, no. 27 (January 1, 2021): 167–74. http://dx.doi.org/10.26728/ideas.407.

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21

Mishchuk, Yevhen, and Dmytro Mishchuk. "IoT-based industrial automation systems." Gіrnichі, budіvelnі, dorozhnі ta melіorativnі mashini, no. 96 (December 31, 2020): 42–50. http://dx.doi.org/10.32347/gbdmm2020.96.0501.

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"Internet of Things" approaches in comparison with classical industrial automation allow to create system architectures which appear more economical, flexible, productive and effective that is reached at the expense of communication and interaction with industrial devices of automation (industrial controllers), sensors. , actuators, drives, machine vision systems, video, robotic systems. The basis of the "Internet of Things" (IoT) is the technology of interaction of machines (M2M), when machines use mobile networks to exchange information with each other or transmit it to data processing and storage systems. M2M technology is effectively used in health and safety systems, in manufacturing, housing and communal services, energy, and the banking sector. The active development of IoT technology requires research and analysis of the mechanisms of their effective implementation in industry, in particular, construction, and the development of concepts of industrial automation and management, which will contain a set of rules defining appropriate control actions for each important set of events. real-time data reported by IoT devices.

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22

Ciucias, Michał, Waldemar Nowakowski, and Daniel Pietruszczak. "Safety of industrial automation systems." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 24, no. 6 (June 30, 2019): 50–55. http://dx.doi.org/10.24136/atest.2019.124.

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In order to minimize the risks associated with the automation of industrial processes, it is necessary to unify standards of safety assessment. The aim of this article is the comparative analysis of safe-ty assessment methods of industrial automation systems. Authors presented two techniques of ensuring safety based on risk analysis, i.e. Performance Level (PL) and Safety Integrity Level (SIL) in relation to the applicable standards and regulations.

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23

Balbudhe, Priyadarshi. "Industrial Automation Based on IOT." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 4055–56. http://dx.doi.org/10.22214/ijraset.2019.5681.

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24

Thomesse, J. P. "Fieldbus Technology in Industrial Automation." Proceedings of the IEEE 93, no. 6 (June 2005): 1073–101. http://dx.doi.org/10.1109/jproc.2005.849724.

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25

Moore, Glenis. "Industrial automation. Mr. Curran's baby." Electronics and Power 32, no. 3 (1986): 232. http://dx.doi.org/10.1049/ep.1986.0147.

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26

Ahern, Mal. "Cinema’s Automatisms and Industrial Automation." Diacritics 46, no. 4 (2018): 6–33. http://dx.doi.org/10.1353/dia.2019.0001.

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27

De Carli, Alessandro, and Paolo Di Giamberardino. "Data processing for industrial automation." IFAC Proceedings Volumes 36, no. 12 (July 2003): 183–88. http://dx.doi.org/10.1016/s1474-6670(17)32533-8.

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28

Ernst, D., and B. Phillipson. "Modern Developments in Industrial Automation." IFAC Proceedings Volumes 20, no. 5 (July 1987): 1–8. http://dx.doi.org/10.1016/s1474-6670(17)55532-9.

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29

Timoney, Conal F., Maria E. DiLorenzo, and Robin A. Felder. "Pioneers in Industrial Automation Solutions." JALA: Journal of the Association for Laboratory Automation 6, no. 1 (February 2001): 49–51. http://dx.doi.org/10.1016/s1535-5535-04-00114-5.

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30

Bajsic, I., and E. Kranjcevic. "AUTOMATION OF INDUSTRIAL SPRAY DRYER." Instrumentation Science & Technology 29, no. 1 (February 20, 2001): 41–52. http://dx.doi.org/10.1081/ci-100001407.

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31

Bajsic, I., and E. Kranjcevic. "AUTOMATION OF INDUSTRIAL SPRAY DRYER." Instrumentation Science & Technology 29, no. 3 (June 30, 2001): 201–13. http://dx.doi.org/10.1081/ci-100103467.

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32

Bond, A. "Appetite for innovation [industrial automation]." Computing and Control Engineering 17, no. 1 (February 1, 2006): 12–13. http://dx.doi.org/10.1049/cce:20060101.

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33

A, Reshma, and Rajathi G M. "Industrial Automation Based on BLE." International Journal of Electronics and Communication Engineering 5, no. 10 (October 25, 2018): 15–20. http://dx.doi.org/10.14445/23488549/ijece-v5i10p105.

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34

Reid, Tony. "Financial incentives for industrial automation." Assembly Automation 14, no. 2 (June 1994): 35–38. http://dx.doi.org/10.1108/01445159410060811.

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35

De Silva, Clarence W. "Soft automation of industrial processes." Engineering Applications of Artificial Intelligence 6, no. 2 (April 1993): 87–90. http://dx.doi.org/10.1016/0952-1976(93)90024-r.

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36

Kuamar, B. Shiva, B. Satya Satwik, and N. Anil Kumar A. V. Tarun Kumar. "Design and Implementation of Bluetooth Based Industrial Automation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1130–32. http://dx.doi.org/10.31142/ijtsrd23180.

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37

Beck, Chris, and Andrew Allcock. "Disruptive Automation." Manufacturing Management 2020, no. 2 (February 2020): 34–35. http://dx.doi.org/10.12968/s2514-9768(22)90127-9.

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38

Brinkley, Alex. "Designed for Health & Efficiency." New Electronics 56, no. 5 (May 2023): 22–23. http://dx.doi.org/10.12968/s0047-9624(24)60088-x.

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39

Yang, Xiao Qing. "Discussion on Digital Technology in the Application and Innovation of Idustrial Electrical Automation." Advanced Materials Research 1079-1080 (December 2014): 913–17. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.913.

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along with our country economy and the development of technology,the technical development of industrial electrical automation is the irreversible trend. In this context, the digital technology emerge as the times require, is being used more and more widely in industrial electrical automation, play a decisive role in the development of China's industrial electrical automation. This paper expounds the necessity of the application of digital technology in the domestic and industrial electrical automation, analyzes the application value of digitaltechnology in the industrial electronic automation system, and discusses theinnovative application of digital technology in the industry of electrical automation.

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40

Li, Jing Min. "On Current Situation and Development of our Industrial Automation Control Technology." Advanced Materials Research 383-390 (November 2011): 5697–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5697.

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The industrial automation control technology is comprehensive technology, mainly including industrial automation hardware, software and systems which reaches inspect, control and optimization, scheduling, management and decision-making, and increases yield and improve quality, reduce cost, to ensure safety. Industrial automation control technology as the most important technology in modern manufacturing in 20th century, which mainly solve the problem of production efficiency and consistency. Although automation system itself is not directly creating benefit of enterprise production, but it has obvious effect of ascension process. Our industrial automation control development road, mostly in the introduction of complete sets of equipment, and at the same time the digestion and absorption of second development and application. Currently our industrial automation control technology, industry and applications have developed greatly in industrial computer system, has been formed. At present, industrial automation control technology is intelligent, network integration and development direction

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41

Thangaraja, M., K. Suresh Manic, and S. Uma. "Industrial Automation Using Wireless Mesh Network." Applied Mechanics and Materials 367 (August 2013): 417–21. http://dx.doi.org/10.4028/www.scientific.net/amm.367.417.

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The use of wireless technology in industrial automation offers a number of potential benefits, from the obvious cost reduction brought about by the elimination of wiring to the availability of better plant information, improved productivity and better asset management. However, its practical implementation faces a number of challenges: lack of a universally agreed standard, security, noisy environment, reliability. Since there is no IEEE standard for using wireless technology in industrial automation it is not possible to use the conventional industrial automation system. We also need better transmission rate for reliable communication. In this paper we proposed an industrial automation system based on wireless multi hop network using FPGA. The multi hop network will provide the reliable communication to central control unit and FPGA will provide the reconfigurable high speed data communication. The design is implemented in Xilinx Spartan 3E FAGA kit and results shows that it will be the solution for future industrial automation problems.

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42

Malaga, Miroslav, Tomas Broum, Michal Simon, and Michael Fronek. "Industrial robotics as an important part of modern production automation." Acta Mechatronica 7, no. 4 (December 31, 2022): 31–36. http://dx.doi.org/10.22306/am.v7i4.91.

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This paper focuses on industrial robotics, highlighting it as an important part of modern manufacturing automation. First, the paper focuses on manufacturing automation more generally, highlighting its importance, pros and cons. This is followed by a description of fixed, programmable and flexible automation, with a link to industrial robotization. Then, the connection of robotization with other types of automation, i.e., forced and economically justified automation, is highlighted, followed by large and small automation.

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43

Vogel-heuser, Birgit, Gunther Kegel, and Klaus Wucherer. "Global Information Architecture for Industrial Automation." atp edition 51, no. 01-02 (June 19, 2013): 108. http://dx.doi.org/10.17560/atp.v51i01-02.1948.

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To develop a new information model for automation in its different dimensions is a huge challenge with strong influences for standardization in automation. This paper gives only a short motivation and introduction into these requirements and shows that all stakeholders need to be involved to define these requirements and to develop a method to gain the information model layer as the back bone for information integration in multi vendor plants. But the result is promising: a huge improvement in engineering, operation, optimization and management of production processes with much more flexibility during operation

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44

GAYATHRI, S., S. NAVEEN KUMAR, and R. KARTHIK. "INDUSTRIAL AUTOMATION USING PLC AND SCADA." i-manager’s Journal on Instrumentation and Control Engineering 7, no. 4 (2019): 12. http://dx.doi.org/10.26634/jic.7.4.17450.

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45

Langeron, Y., A. Grall, and A. Barros. "Actuator Lifetime Management in Industrial Automation." IFAC Proceedings Volumes 45, no. 20 (January 2012): 642–47. http://dx.doi.org/10.3182/20120829-3-mx-2028.00111.

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46

Mentsiev, A. U., Y. A. Kulpeiis, and K. K. Smagulova. "Cloud computing in industrial automation systems." IOP Conference Series: Materials Science and Engineering 1155, no. 1 (June 1, 2021): 012063. http://dx.doi.org/10.1088/1757-899x/1155/1/012063.

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47

Kumar, K. Praveen. "Industrial Automation using Arduino with-IOT." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 2496–99. http://dx.doi.org/10.22214/ijraset.2019.3457.

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48

Balbudhe, Priyadarshi. "An Overview of Smart Industrial Automation." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 4053–54. http://dx.doi.org/10.22214/ijraset.2019.5680.

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49

Brusso, Barry C. "50 Years of Industrial Automation [History]." IEEE Industry Applications Magazine 24, no. 4 (July 2018): 8–11. http://dx.doi.org/10.1109/mias.2018.2820440.

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50

Wrobel, A., and M. Placzek. "Visualization systems for industrial automation systems." IOP Conference Series: Materials Science and Engineering 400 (September 18, 2018): 062032. http://dx.doi.org/10.1088/1757-899x/400/6/062032.

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