Industrial Automation Technology Research Articles

Development of Teaching Factory Learning Model in Industrial Automation in Vocational High Schools

Vocational High Schools, as schools that aim to prepare students for work in a particular field, need effectiveness in the learning process. Its effectiveness must follow the real conditions required by the industry. With the rapid devel-opment of industry, industry-based learning, often called "Teaching Factory," is one of the right role models of learning. The type of research to be carried out is development research. The purpose of this study is to determine (1) the devel-opment of the teaching factory learning model, (2) the application of the teach-ing factory learning model, (3) user response to the product, and (4) the effec-tiveness of using the developed product. The development model used in this study is the Plomp development model. The Plomp development model includes (1) the initial investigation phase, (2) the design phase, (3) the realiza-tion/construction phase, (4) the testing, evaluation, and revision phase, and (5) the field test/implementation phase. Validity by model experts about the teach-ing factory learning model gets an average score of 95.45 and is included in the very decent category. The material expert gave it a score of 93.41, and it be-longs to the very decent category. The media expert gave it a score of 95.84, which belongs to the decent category. The effectiveness test result shows a sig-nificant difference between the learning outcomes in the Industrial Automation Engineering Expertise Concentration subjects before and after applying the Teaching Factory learning model. This shows the positive influence and effectiveness of using the Teaching Factory learning model on the learning out-comes of the Industrial Automation Engineering Expertise Concentration in In-dustrial Automation Engineering Vocational School students.

Fault detection of wind turbine system based on data-driven methods: a comparative study

AbstractFault detection plays a crucial role in ensuring the safety, availability, and reliability of modern industrial processes. This study focuses on data-driven fault detection methods, which have gained significant attention across various industrial sectors due to the rapid development of industrial automation technologies and the availability of extensive datasets. The objectives of this paper are to comprehensively review and present the theoretical foundations of widely used data-driven fault detection approaches. Specifically, these approaches are applied to fault detection in wind turbine systems, with performance evaluation conducted using multiple statistical measures. The data utilized in this study were collected from a simulated benchmark of a wind turbine system. The data-driven methods are tested under the assumption that the wind turbine operates in a steady-state region. Additionally, a comparative study is conducted to identify and discuss the primary challenges associated with the practical application of these methods in real-world scenarios. Simulation results show the effectiveness and efficacy of data-driven approaches concerning the sensitivity and robustness of wind turbine sensor faults as applied in practical industrial environments.

ЭКОНОМИЧЕСКИЙ ПОТЕНЦИАЛ ПРИМЕНЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В АВТОМАТИЗАЦИИ ПРОИЗВОДСТВА: АНАЛИЗ ПРЕИМУЩЕСТВ И ВЫЗОВОВ

This article examines the impact of artificial intelligence technologies on the economic efficiency of production processes. These technologies are considered as industrial automation technologies. The stages of the introduction of artificial intelligence in industry and how it contributes to improving the efficiency of production processes are determined. It is considered how artificial intelligence is used in various fields, as well as what challenges and barriers exist before its large-scale application.

Digital Twin in Fluid Power: Review- Technology Trends

The Digital Twin (DT) is the most modern technology in Industry 4.0, which combines digital and physical components, accurate visualization, and multifaceted perception to represent the digital-physical world in real-time. The use of simulation, monitoring, optimized performance, and projection are crucial and increasingly popular, particularly in the field of industrial automation. Fluid Power Application (FPA) is an essential industrial automation technology that offers accurate monitoring, immense power, flexibility, reliability, cost-effectiveness, and seamless compatibility with pre-existing automated systems through the utilization of hydraulic and pneumatic power. Nevertheless, certain technological elements and emergent DT technology in fluid power systems have not been extensively explored. Based on the conception and constituents addressed in a companion review paper (1), this paper looks into various technologies that allow the seamless integration of both physical and digital representations in diversified FPAs. This article intends to thoroughly examine the crucial role of technological enablers in the successful implementation of digital twin. The paper provides an in-depth review of the technological enablers that support DT, specifically focusing on software used for the simulation of FPA and programmable logic controllers (PLCs) with the accompanying software. This review paper enhances the academic discourse by emphasizing different emerging technologies and acts as a catalyst for DT in current investigation efforts and breakthroughs in FPAs.

Monitoring exhaust air temperature and detecting faults during milk spray drying using data-driven framework

The advanced data analytics platform bridges the gap between industrial automation technology and new cloud-based technology. The information on the implementation of a data analytics platform to convert huge data into valuable information and use it to serve the scheduled maintenance of the components involved in the food processing industry is rarely reported in the literature. This work reports a data-driven framework for prediction and fault detection in key performance parameters for a milk spray drying process plant. The framework consists of different data analysis methods and it helps to take decisions about the selection of key performance parameters involved in improving the spray drying thermal efficiency. The neural network-based NARX model demonstrates a better performance than the linear models in the prediction of cyclone exit air temperature which is the key performance parameter in spray drying as it governs thermal efficiency. The performance of the predictive model is validated using RMSE. The ML-based classification methods are also used in the present work to classify the different faults and the decisions regarding the maintenance of the components responsible for the faults. The performance of these models was verified using a confusion matrix. It is proposed that the decision tree classifier and random forest classifier are best suitable for fault finding as their accuracy is highest at 99.83%.

Threat modeling in smart firefighting systems: Aligning MITRE ATT&CK matrix and NIST security controls

Industrial automation technologies are envisioned as multi-device systems that are constantly interacting with one another and with enterprise systems. In these industrial systems, the industrial internet of things (IIoT) significantly improves system efficiency, scalability, ease of control, and monitoring. These benefits have been achieved at the cost of greater security risks, thus making the system vulnerable to cyberattacks. Historically, industrial networks and systems lacked security features like authentication and encryption due to intended isolation over the Internet. Lately, remote access to these IIoT systems has made an attempt of holistic security alarmingly critical. In this research paper, a threat modeling framework for smart cyber–physical system (CPS) is proposed to get insight of the potential security risks. To carry out this research, the smart firefighting use case based on the MITRE ATT&CK matrix was investigated. The matrix analysis provided structure for attacks detection and mitigation, while system requirement collection (SRC) was applied to gather generic assets’ information related to hardware, software and network. With the help of SRC and MITRE ATT&CK, a threat list for the smart firefighting system was generated. Conclusively, the generated threat list was mapped on the national institute of standards and technology (NIST) security and privacy controls. The results show that these mapped controls can be well-utilized for protection and mitigation of threats in smart firefighting system. In future, critical cyber–physical systems can be modeled upon use case specific threats and can be secured by utilizing the presented framework.

Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information

With the progress and development of AI technology and industrial automation technology, AI robot dogs are widely used in engineering practice to replace human beings in high-precision and tedious industrial operations. Bionic robots easily produce control errors due to the influence of spatial disturbance factors in the process of pose determination. It is necessary to calibrate robots accurately to improve the positioning control accuracy of bionic robots. Therefore, a robust control algorithm for bionic robots based on binocular vision navigation is proposed. An optical CCD binocular vision dynamic tracking system is used to measure the end position and pose parameters of a bionic robot, and the kinematics model of the controlled object is established. Taking the degree of freedom parameter of the robot’s rotating joint as the control constraint parameter, a hierarchical subdimensional space motion planning model of the robot is established. The binocular vision tracking method is used to realize the adaptive correction of the position and posture of the bionic robot and achieve robust control. The simulation results show that the fitting error of the robot’s end position and pose parameters is low, and the dynamic tracking performance is good when the method is used for the position positioning of control of the bionic robot.

Development of Fingerprint Identification Based on Device Flow in Industrial Control System

With the rapid development of industrial automation technology, a large number of industrial control devices have emerged in cyberspace, but the security of open cyberspace is difficult to guarantee. Attacks on industrial control devices can directly endanger the environment and even life safety. Therefore, how to monitor the industrial control system in real time has become the primary problem, and device identification is the basic guarantee of safety monitoring. There are limitations in building device identification model based on IP address or machine learning. The paper aim at the development of a device traffic fingerprint model and identify the device based on the periodicity of device traffic. The model generates device fingerprints based on pattern sequences abstracted from the traffic and suffix array algorithm. In the process of recognition, the exact pattern matching algorithm is used for preliminary judgment. If the exact pattern matching fails to hit, the final judgment is made by combination fuzzy pattern matching. This paper also proposes a diagonal jump algorithm to optimize the updating of the distance matrix, which saves on the computational cost of fuzzy pattern matching. Simulation results show that compared with SVM, random forest, and LSTM model, the device traffic fingerprint model has good performance advantages in accuracy, recall and precision.

Application of Intelligent Digitization in the Design and Production Process of Injection Molding Machine

The combination of industrial information technology, automation technology and design and manufacturing technology can not only give full play to the advantages of existing technology, but also optimize the process technology and practical operation skills of injection molding machines, ensure the accuracy and simplicity of precision processing processes, and improve the quality and efficiency of products. On the other hand, it can also effectively guide the urgently needed intelligent manufacturing talents in China, promote the career transformation of universities, ensure the full use of talents, and promote the technological development of high-tech industries. In this paper, the author makes a detailed analysis and research on the application of intelligent digitization in the production and assembly of injection molding machines, hoping to provide conditions for the current production and assembly of injection molding machines.

Digital twins creation based on discrete modelling of non-destructive evaluation objects

Nowadays, the implementation of digital industrial technologies for maximal industrial processes automation and unmanned technologies creation is the main direction of technological development in the key of Industry 4.0 and NDE 4.0 paradigms. Service-oriented approach is proposed for the implementation of digital non-destructive evaluation as an effective solution for obtaining, processing and storing data and testing results. The model of industrial radiography laboratory simulator is considered, discrete models of digital twin objects are built when obtaining an image, for which the basic statements and tools of set theory are used. Examples illustrating the correspondence of digital twins’ mathematical models to their graphical analogues as well as the image generated on the detector are presented. The subject of extending CAD systems tools in the context of practical design of digital twins geometric image is considered. It is noted that virtualization of such a complex technical object as industrial radiography laboratory allows to check feasibility of technical solutions and engineering hypotheses with regard to real industrial conditions and needs in a short period of time and with minimum costs, thus providing, variability in implementation of process steps, and increasing the number of trained qualified specialists in radiation kind of non-destructive evaluation.

Research on Industrial Machinery Design and Automation Technology under the Vision of Intelligent Manufacturing

Research on Industrial Machinery Design and Automation Technology under the Vision of Intelligent Manufacturing

Penerapan Model Pembelajaran Group Investigation untuk Meningkatkan Keaktifan dan Hasil Belajar Siswa di SMK YPWKS Cilegon


 This research is a type of Classroom Action Research (CAR) which aims to improve the quality of the teaching and learning process which leads to an increase in student activity and student result in Basic Electrical and Electronics subjects. The design of classroom action in this study refers to the design of Kemmis & Taggart model which consist of several cycles and each cycle there are four stages, namely (1) planning stage, (2) action stage, (3) observation stage, and (4) reflection stage. The population of this study were students of class X TOI in the Industrial Automation Engineering Expertise Program at SMK YPWKS Cilegon, the samples in this study were students of class X TOI A as many as 22 people. Data were collected through tests and observations. Technical analysis of data using descriptive quantitative. The results showed that the application of Group Investigation learning model in Basic Electrical and Electronics subjects could increase the student activity and student result of class X TOI A students. This increase in activity can be seen from student contribution during the learning process in the form of student enthusiasm in paying attention to teacher explanations, paying attention to friends' explanations while presentations, asking question, expressing opinions, taking notes on material, writing assignments given by the teacher, and working together with groups. The highest achievement in the indicator is paying attention to the teacher’s explanation, taking notes and listening to the teacher’s explanation, each indicator has a percentage of 95,45%. Meanwhile, the lowest achievement is the indicator of doing group work and listening to questions and answers with a percentage of 72.73%. The increase in student learning activity has an effect on increasing students' psychomotor and cognitive learning outcomes. The average value of psychomotor learning outcomes in the first cycle was 67.73%, increasing to 81.82% in the second cycle. The students' cognitive aspect obtained the percentage of the first cycle of 74.06% and the second cycle increased to 69.32%. And increased in the second cycle that is equal to 82.27
 Keywords: Group Investigation;Learning Activity; learning Outcomes;Basic Electrical and Electronics subjects

Discussion on Application of Industrial Electrical Automation Technology in Energy-saving Project of Coking Plant

Discussion on Application of Industrial Electrical Automation Technology in Energy-saving Project of Coking Plant

Applications of Flexible Manufacturing Systems in Industry: A Review

Abstract: FMS and its adoption is a nuanced cradle technology in India and sees a limited implementation of its innovative technology and concepts. Our world today is a technologically sophisticated world, only characterized as fictional a few years ago. A lot today has transformed smart and technologists are striving to bring home new and innovative devices, homes, factories, and even cities. Scientifically proven is that these advanced technologies and concepts can boost an organization’s overall performance. Many corporations, researchers, and institutions agree that automation and robotic systems are the foundation of industrial production and a key factor of the Industrial 4.0 revolution. There is an ongoing demand for increased output and manufacturing quality, as well as an urgent requirement to improve overall efficiency in manufacturing systems to withstand competitive pressure. Flexible Manufacturing Systems has added these advantages to organizations, due to its capabilities, capacity and advantages. The work of extensive research presented highlights the importance of FMS adoption and acceptance of various industrial automation technologies and techniques, as well as FMS potential. Keywords: Flexible Manufacturing Systems, Manufacturing, Industry 4.0, Industrial Production, Automation

PROSIM in the Cloud: Remote Automation Training Platform with Virtualized Infrastructure

The need for high-availability, efficient, training solutions for industrial automation technology is driving the adoption of new tools, software and services supporting the Industry 4.0 paradigm. Specific requirements include vendor agnostic and standards-based programming languages and availability of both physical input–output and industrial communication, while providing a rich and immersive experience through suitable human–machine interface components. The PROSIM platform represents an integrated embedded hardware-software system for programmable logic controller automation training which provides examples in increasing order of complexity, ranging from basic digital logic up to complex manufacturing simulations and process control. A fully software-based solution has been developed in order to complement the laboratory-based system as a training digital twin. We describe the design, implementation and evaluation of the platform as ported onto the HUBCAP collaborative sandboxing environment for virtualized operation. Integration of dedicated communication libraries for Open Platform Communications, Unified Architecture, with simulated programmable logic controllers such as PLCSIM and ARSim is also discussed for fully leveraging new features emerging from increased information and operational technology convergence witnessed in realistic, plant-level, environments. Qualitative and quantitative evaluation in comparison to the physical system while leveraging the resources provided by the cloud environment is carried out.

Penerapan Media Pembelajaran Pada Mata Pelajaran Sistem Elektropnuematik Berbasis website

This study aims to determine the level of feasibility of the website of the subject electropneumatic controlsystem. Based on early observation data, the Department of Industrial Automation Engineering does not yethave a learning website to support the electropneumatic system learning process. Thus, this study used a research and development (R and D) model with the Waterfall method. However, the subjects involved inthis study were class XI Industrial Automation Engineering Department. The data collection techniqueschosen were interviews and questionnaires with the media evaluation sheet instrument, material evaluationsheet, and user assessment sheet which presented on a Likert scale. The results showed that the websitesupported the learning of electropneumatic system subject had been tested in 8 meetings in the class XI ofIndustrial Automation Engineering. The results of this development research showed that the mediafeasibility test got an average of 75% in the "appropriate" category, the results of the material evaluation testreached an average of 49.7% in the "adequate" category and the assessments from teacher and studenttook an average of 55% with "decent" category. It can be concluded that the website developed iscategorized as "appropriate" to be applied as a learning support in the Department of Industrial AutomationEngineering, especially in the subject of electropneumatic systems. Based on the results, it can besuggested website development on other subject matter to support the success of the learning process.

Flowrox rebrands as Roxia

Filtration Industry AnalystVol. 2021, No. 11 NewsFlowrox rebrands as RoxiaPublished Online:11 Dec 2021AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View articleAbstractWith the sale of Flowrox's valve and pump businesses to Neles Corp, the Finnish company's dewatering, industrial automation and environmental technologies, including filtration, will continue under the Roxia name and brand (see Filtration Industry Analyst, July 2021). FiguresReferencesRelatedDetails 1 January 2021Volume 2021Issue 11ISSN (print): 1365-6937ISSN (online): 1873-6440 Metrics History Published online 11 December 2021 Published in print 1 January 2021 Information© MA Business Ltd.PDF download

2nd International Conference on Computational Intelligence and Energy Advancements (ICCIEA-2021), September 11-12, 2021 Virtual Format

International Conference on Computational Intelligence and Energy Advancements (ICCIEA-2021) is a prestigious event organized with a motivation to provide an excellent platform for the academicians, researchers, engineers, industrial participants and budding students around the world to share their research findings with the global experts. The conference aimed to provide researchers and attendees with prospects for global collaboration and networking among universities and corporate organizations for promoting research. ICCIEA-2021 was conducted in virtual format owing to the pandemic situation during 11th to 12th September 2021. It involved all classes of academia having research orientation.The conference was organized jointly by departments of ECE and ME, Vaagdevi College of Engineering and King Abdulaziz University, Rabigh, Saudi Arabia. As it was an event conducted in cyber form, the presentations were delivered using an online platform. Five different and parallel technical sessions were scheduled based upon the tracks of the articles and a Best Paper was awarded in each session. A total of 38 articles were selected for presentation after double blind peer review process. This proceeding issue compiles oral presentations that were presented by the authors and rigorously reviewed by a special committee designated by the editor team of the conference.The organizing and editorial committee of the ICCIEA-2021 hopes you enjoy reading the articles published in this volume of the JPCS. Besides, we wish to thank all the authors and participants for providing their valuable contributions for this proceeding as well as the reviewers for their constructive recommendations and criticism aiding to improve the presented articles. We are confident that this issue will serve as a reference for computational intelligence, industrial automation, energy advancements and sustainable technologies in the world of scientific research, engineering and technology.List of Organizing Committee, Steering Committee, Technical Program Committee, Industry Experts Committee, Editorial Committee, Invited Speakers, Organizing Institutions, Academic Partner are available in this pdf.

Towards Multi-Finger Dexterous Hand Mechanics Control and Tactile Feedback

Abstract：The ever-changing demands of industrial automation and space technology have promoted the rapid development of robotics. Traditional robotic end effectors are difficult to perform smart operations, so there is an urgent need for a robotic hand to perform complex operations instead of humans. In this article, we will focus our attention on mechanical control and haptic feedback. Mechanical control and haptic feedback are necessary conditions for the stable and accurate grasping of multi-finger dexterous hands. Tactile perception can provide stiffness and temperature to multi-finger dexterous hands. Important information makes the function of the dexterous hand more perfect. This article introduces the kinematics and dynamics of dexterous hand fingers, as well as the kinematics and dynamics solving equations, then reviews the current sensors and various control driving methods used in dexterous hands, discusses drive control, and compares each method Pros and cons. Finally, the future development of dexterous hands is predicted.

Application of Computer Vision Technology in Industrial Automation

With the continuous development of artificial intelligence and Internet of Things technology, the manufacturing industry is transforming and upgrading from automation to intelligence. Countries continue to increase support and investment in intelligent manufacturing from the policy and financial aspects. This paper mainly studies the application of computer vision technology in the field of industrial automation. In this paper, the sparse representation algorithm is applied to the category detection of artifacts with arbitrary orientation. By establishing the Gabor feature dictionary of the workpiece and solving the sparse reconstruction solution according to the feature vector of the detected workpiece image, the category label of the workpiece is obtained. Aiming at the reconstruction of 3D point cloud of workpiece, this paper uses the method of feature point matching to realize the reconstruction of 3D point cloud. Finally, the workpiece detection method studied in this paper is implemented on the system and the performance of the algorithm is verified.