Industrial Automation Systems Research Articles

Securing the Industrial Backbone: Cybersecurity Threats, Vulnerabilities, and Mitigation Strategies in Control and Automation Systems

Industrial Control and Automation Systems are the invisible workhorses of modern industry, managing critical infrastructure and manufacturing processes. The increasing convert of IT and OT networks has exposed ICAS to a growing range of cybersecurity threats. This paper explores the current landscape of cybersecurity threats and vulnerabilities plaguing ICAS. It delves into the technical aspects of these vulnerabilities, ranging from network weaknesses to human error. Additionally, the paper proposes a comprehensive set of mitigation strategies that can be implemented to bolster the security posture of ICAS. These strategies encompass network segmentation, access control, vulnerability management, incident response planning, and ongoing security awareness training. Finally, the paper highlights the importance of adhering to industry security standards and fostering a culture of cybersecurity within organizations.

AI/ML-aided capacity maximization strategies for URLLC in 5G/6G wireless systems: A survey

Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands. Most of the reported 5G/6G applications are focused on URLLC, which necessitates a latency of milliseconds and very high dependability for transmitted data. These systems encounter several obstacles since conventional networks cannot fulfill such demands. According to the standards of the 3rd generation partnership project URLLC, it is predicted that the dependability of a single transmission of a 32-byte packet would be no less than 99.999%, and the latency will not exceed 1 ms. The exceptional degree of dependability and minimal delay will result in the emergence of many novel applications, including smart grids, industrial automation, and intelligent transport systems. This review discusses several methods for maximizing capacity in URLLC, focusing on resource allocation strategies, multi-access approaches, and beamforming with massive MIMO. Furthermore, it explores the requirements and constraints of URLLC and the role of AI/ML in URLLC. Finally, this study examines possible future research areas and obstacles to achieving the URLLC standards.

The adoption of ICT and robotic automation systems in the pharmaceutical industry

Background: South African pharmacies face challenges like cost reduction, inventory management, and employee efficiency. Balancing dispensing error rates and customer satisfaction, along with ensuring accurate medication dispensing, is also crucial. An effective solution to these challenges is adopting automatic robotic dispensing systems, which enhance stock inventory management, integrated systems, and accurate dispensing capabilities.Objectives: The study delved into IT and robotic automation in South African pharmaceutical dispensing, assessing current methods and advocating for integrated IT and Automated Storage and Retrieval Systems in retail and hospital pharmacies.Method: The research used a quantitative approach to gather data from public and private hospital pharmacy employees, aiming to understand their requirements and expectations. It assessed the potential improvements that could result from adopting a new system.Results: The study found that most government and private pharmacies in South Africa prefer automated dispensing systems to reduce errors, lower costs, improve customer service, and enhance inventory management. Benefits also included minimising medication errors, improving operational efficiency, and ensuring patient safety.Conclusion: The study holds importance as it underscores the necessity of integrating Information Technology (IT) and Robotic Automation in the pharmaceutical sector to address prevailing issues. It identifies factors contributing to medication dispensing errors and demonstrates the potential of automated robotic systems in mitigating these errors.Contribution: South Africa’s pharmaceutical sector must enhance efficiency and competitiveness by adopting integrated IT and Robotic Automation Systems. The study identified key factors for future implementation and emphasized the need for clear pre-implementation policies outlining functions and benefits.

Domain‐specific translation tool from structured text to C source code with code readability enhancement in programmable logic controllers

AbstractThe Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings.

Pendampingan Program Technocreator dan Perancangan Kurikulum Ekstrakurikuler Robotik untuk SMAK 1 BPK Penabur Bandung

This community service establishes a collaborative effort between Parahyangan Catholic University and SMAK 1 BPK PENABUR Bandung to enhance the TechnoCreator program through community involvement. The program aims to enhance students' understanding of electronics by incorporating advanced technologies like the Internet of Things (IoT) and machine learning. It also introduces the concepts of Industry 4.0 and technopreneurship. The research entailed conducting a series of focused group talks, often known as focus groups, to ascertain the material and learning requirements. Additionally, an extracurricular curriculum for robotics was developed. The outcomes are the creation of novel educational programs, maintenance of guest lectures, strategic planning for the construction of an ESP32-based Internet of Things kit, the design of a prototype industrial automation system, and the implementation of a supplementary robotics program aimed at enhancing students' preparedness for advanced studies in electrical engineering. The activity also seeks to facilitate the transfer of students from secondary education to college. The result implies that this project effectively enhances student learning experiences and can be used as a blueprint for comparable technology education programs in the future.

The Development of Manufacturing Industry Revolutions from 1.0 to 5.0

Industry 5.0 is a term used to describe how the manufacturing industry is being modernized and how human and machine collaboration is being fostered to increase efficiency and speed up results. By integrating digital technologies, this revolutionary transformation not only creates tailored goods that increase customer happiness but also establishes a new paradigm in supply chain management and manufacturing. Complex problems are addressed by using artificial intelligence and machine learning to the analysis of large amounts of manufacturing data. Gaining a competitive edge and promoting economic growth requires embracing IoT and the Industry 5.0 phenomenon in today's fast-paced corporate environment, which is defined by technical breakthroughs and globalization. Because they work in unison with human intelligence, robotics' contribution to this revolution enhances the accuracy of computation and prediction in industrial automation systems. In the age of technology, rapid changes are driving the development of mass customization and smart manufacturing, with robots playing a crucial role in enhancing human talents. This essay examines the historical background of industrial revolutions and the effects of Industry 4.0 and Industry 5.0 on society and manufacturing. It also highlights the salient characteristics and potential applications of these next industrial revolutions, culminating in a summary of the differences between Industry 4.0 and Industry 5.0.

Cybersecurity of Industrial Systems—A 2023 Report

The article explores the importance of cybersecurity in Industry 4.0, specifically focusing on safeguarding industrial automation systems from cyberattacks. It discusses essential security measures, network monitoring, and employee training, emphasizing the significance of risk management for ensuring industry stability. Adherence to industrial security standards, such as ISA/IEC 62443, is crucial (ISA—International Society of Automation, IEC—International Electrotechnical Commission). The article outlines cyberthreat challenges and their impact on various sectors, including healthcare and finance, as well as the risks faced by large industrial enterprises. Additionally, it elucidates evolving cybersecurity strategies and principles, underscoring the necessity for continuous, multi-layered protection. Collaboration with operators, strict information security policies, and robust incident response plans are emphasized. The importance of risk monitoring and adaptability to ever-changing threat landscapes is highlighted, emphasizing the collaborative and flexible nature of cybersecurity in the face of escalating digital threats.

Mechatronic automatic control system of electropneumatic manipulator

Mechatronic systems of electropneumatic automation are one of the main classes of industrial automation systems. A laboratory stand for the study of the mechatronic system of automatic control of the pneumatic manipulator and a computer model for preliminary experiments on the adjustment of the automatic control system were developed. Manual and software control modes are provided for research of indicators of safety and quality of management in both modes. To implement the software control mode, a microcontroller part of the laboratory stand based on ADuC841 was developed, with the help of which it is possible to simulate a part of a certain technological process, to detect and eliminate faults in the automatic control system. A study of automatic control systems using a traditional relay-contactor control system, based on GrafCet technology and using a virtual controller. The combination of computer modeling of technological processes and physical modeling of executive mechanisms is a kind of digital double that displays its state, parameters and behavior in real time. The use of a laboratory stand in combination with an adequate simulation model reduces the complexity of developing control systems for practical applications, and also contributes to the formation of students' creative component, ability to analyze the results, and make decisions in unusual situations, which will increase their theoretical and practical training. The study of mechatronic systems of pneumatic manipulators will allow to increase their efficiency and productivity, to optimize their speed and accuracy for various applications in production. The interaction of mechatronic systems of pneumatic manipulators with other technologies, such as machine learning, artificial intelligence, IoT is the basis for creating more integrated and intelligent systems.

ADAPTIVE PROCESS MANAGEMENT USING DEEP LEARNING ON A PROGRAMMABLE LOGIC CONTROLLER (PLC)

In the era of Industry 4.0, the integration of advanced digital technologies into manufacturing processes has become paramount for enhancing operational efficiency and adaptability. This study introduces a groundbreaking approach to adaptive process management through the integration of deep learning algorithms within Programmable Logic Controllers (PLCs), thus addressing the limitations of traditional PLCs in dynamically adjusting to new operational conditions without manual intervention. By leveraging the inherent capabilities of deep learning for realtime data analysis and decision-making, this research develops a novel framework that enables PLCs to autonomously learn from process data, adapt control strategies in real-time, and optimize manufacturing operations. The methodology encompasses the design and implementation of deep learning models tailored for PLC environments, the development of a data-driven learning mechanism directly on the PLC, and a comprehensive evaluation of the system’s adaptability, efficiency, and performance in real-world industrial settings. The findings reveal significant improvements in process efficiency, reduction in downtime, and enhanced adaptability to changing operational conditions, demonstrating the potential of combining deep learning with PLC-based systems for fostering intelligent and flexible manufacturing processes. This study not only provides a viable solution to the challenges of static PLC programming but also opens new ways for research and development in smart manufacturing technologies, offering insights into the practical implications of deploying intelligent automation systems in Industry 4.0.

Industrial Internet of Things: A Review of Improvements Over Traditional SCADA Systems for Industrial Automation

This review article provides an overview of the potential of the Industrial Internet of Things (IIoT) to revolutionize industrial automation. The IIoT is the Internet of Things (IoT) but in an industrial context, i.e., IIoT is used more to connect machines and devices in industrial environments. The IIoT has the potential to benefit from advances in artificial intelligence, particularly machine learning and deep learning, to increase efficiency and productivity and reduce overhead costs. We provide an overview of the supervisory control and data acquisition system, a definition of IIoT, and how IIoT can offer industry greater potential for system integration to improve automation and optimization. In addition, five of the major IIoT protocols are discussed, namely, message queue telemetry transport, advanced messaging queuing protocol, constrained application protocol, data distribution service, and open platform communication unified architecture. We then identified key IIoT improvements for industrial automation. These are; efficient and low-cost systems, digital twin, machine failure prediction, real-time remote monitoring, and security. We then discussed the key research in the literature for each category. We presented some public IIoT datasets so that researchers can use them to develop new learning models to improve the security of IIoT systems. Finally, we discussed some of the limitations, recommendations, and future perspectives for developing IIoT-enabled systems.

An Environmentally Adaptive and Contrastive Representation Learning Method for Condition Monitoring of Industrial Assets.

Condition monitoring of assets is significant to the efficiency and reliability of industrial automation systems. However, the accuracy of condition monitoring results is easily impaired by variational environments and volatile operations, especially for complex automation systems. In this article, an environmentally adaptive and contrastive representation learning method is proposed to address the problem. To suppress the unexpected effects of environmental variations on operating data, a regression model between the operational and environmental variables is developed. The variable regression adjustment is achieved by solving a penalized optimization problem based on spline functions, and the solution is explicitly derived. Then, negative samples and pseudo labels are generated based on the designed pattern of data augmentation, and valid data representations for asset condition monitoring can be obtained by contrastive learning. Moreover, the reference statue of healthy assets is established by kernel density estimation, and control charts are employed for online monitoring with alarm thresholds. Taking wind turbine blades as examples, the remarkable performance of the developed method is demonstrated with real-world measurements from wind farms. Furthermore, comparative analysis with benchmark approaches and ablation study are conducted to reveal the superiority and effectiveness of the proposed method.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0.

Design of a 16–bit 500 MS s–1 SAR-ADC at 45 nm for low power and high frequency applications

This study gives a thorough analysis of the performance of a 45 nm CMOS process-designed 16-bit 500 MS/s successive approximation register analog-to-digital converter (SAR-ADC). 16-bit R-2R DAC double-tail dynamic latch, Widlar current method, variable body-biasing technique, sample and hold block, 16-bit SAR, and 16-bit latch are used to design proposed SAR-ADC block. The SAR-ADC design and simulations were carried out using Cadence Virtuoso software. This powerful electronic design automation (EDA) tool facilitated the design, layout, and simulation of the ADC, ensuring a comprehensive analysis of its performance characteristics. MATLAB was used for post-simulation data analysis, processing, and visualization. The proposed SAR-ADC is compared with few existing examples listed as ADS8881, LTC2380–16, ADS8344, LTC2368, and MAX11156 on the performance metrics including signal-to-noise ratio (SNR), figure of merit (FOM), total harmonic distortion (THD), resolution, delay, power consumption and figure of Merit (FOM). This work is highlighting different aspects of the suggested architecture and demonstrates how it outperforms benchmark of ADCs in terms of power usage, SNR, THD, and FOM. A SAR-ADC attains a power consumption of 39.2 μW while operating at sampling frequency of 500 MS s–1 at supply voltage of 1 V. The results provide fresh perspectives for potential improvements in existing work in terms of reduction in power consumption and high-speed ADC at 16-bit resolution and also Jitter is scrutinized across various stages of the SAR-ADC. The proposed low power, high speed and high-resolution SAR ADC is targeted for high-quality analog-to-digital signal conversion useful in industrial automation systems, medical devices, IoT and audio processing modules.

An Approach to Develop Digital Twins in Industry

The industry is currently undergoing a digital revolution driven by the integration of several enabling technologies. These include automation, robotics, cloud computing, industrial cybersecurity, systems integration, digital twins, etc. Of particular note is the increasing use of digital twins, which offer significant added value by providing realistic and fully functional process simulations. This paper proposes an approach for developing digital twins in industrial environments. The novelty lies in not only focusing on obtaining the model of the industrial system and integrating virtual reality and/or augmented reality but also in emphasizing the importance of incorporating other enabled technologies of Industry 4.0, such as system integration, connectivity with standard and specific industrial protocols, cloud services, or new industrial automation systems, to enhance the capabilities of the digital twin. Furthermore, a proposal of the software tools that can be used to achieve this incorporation is made. Unity is chosen as the real-time 3D development tool for its cross-platform capability and streamlined industrial system modeling. The integration of augmented reality is facilitated by the Vuforia SDK. Node-RED is selected as the system integration option, and communications are carried out with MQTT protocol. Finally, cloud-based services are recommended for effective data storage and processing. Furthermore, this approach has been used to develop a digital twin of a robotic electro-pneumatic cell.

Evaluating a self-manageable architecture for industrial automation systems

Evaluating a self-manageable architecture for industrial automation systems

Cloud-Fog Automation: Vision, Enabling Technologies, and Future Research Directions

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things (IIoT) environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, Artificial Intelligence (AI), and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this paper, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic Connectivity, Deterministic Connected Intelligence, and Deterministic Networked Computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

Smart Actuation for End-Edge Industrial Control Systems

Along with the fourth industrial revolution, industrial automation systems are evolving into a multi-tier end-edge computing architecture. Edge controllers, which are equipped with a larger computing capacity compared to local controllers, can communicate with local plants over mainstream wireless networks such as WirelessHART, Wi-Fi, and cellular networks. Well-known challenges induced by networks, such as uncertain time delays and packet drops, have been intensively investigated from various perspectives: control synthesis, network design, or control and network co-design. The status quo is that the industry remains hesitant to close the loop between the edge controller and the actuation side due to safety concerns. This work offers an alternative perspective to address the safety concern, by exploiting the design freedom of an end-edge computing architecture. Specifically, we present a smart actuation framework, which deploys (1) an edge controller, which communicates with physical plant via wireless network, accounting for optimality, adaptation, and constraints by conducting computationally expensive operations; (2) a smart actuator, which is co-located with the physical plant on the end tier and executes a local control policy, accounting for system safety in the view of network imperfections, (3) the end-edge control co-design strategies and cooperation logic for both performance and stability. For certain classes of plants, semi-globally asymptotic stability of the resulting end-edge control systems is established when the edge controller is the model predictive control (MPC), or policy iteration-based learning control. We also provide an adaptation strategy for the end-edge control systems facing model parameter mismatches when the edge controller employs reinforcement learning. Extensive simulations demonstrate the advantages of the proposed end-edge co-design and cooperation procedures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Edge computing is gaining momentum in areas that require low latency and high efficiency, i.e., mobile computing, video analytics, and autonomous driving. Industrial automation systems are also evolving into a multi-tier end-edge computing architecture. It pays obvious dividends to leverage the cooperation between end and edge, benefiting from fast and reliable communication on the end side, and powerful computation capacity on the edge side. The current end-edge cooperation focuses on how to partition tasks and offload computation resources in order to minimize delay and energy consumption, as well as how to balance the tradeoff between them. However, the impacts of end-edge cooperation on the safety, optimality, and cost of industrial automation have not been systematically studied. This paper aims to tailor end-edge cooperation in a smart actuation framework, for industrial automation to reconcile the above aspects by leveraging co-design of end and edge controllers and their switching logic. Extensive pure and semi-physical simulations demonstrate the advantages in performance and system stability of the proposed end-edge co-design and cooperation procedures.

Alternating current servo motor and programmable logic controller coupled with a pipe cutting machine based on human-machine interface using dandelion optimizer algorithm - attention pyramid convolution neural network

&lt;abstract&gt; &lt;p&gt;The proposed research addresses the optimization challenges in servo motor control for pipe-cutting machines, aiming to enhance performance and efficiency. Recognizing the existing limitations in parameter optimization and system behavior prediction, a novel hybrid approach is introduced. The methodology combines a Dandelion optimizer algorithm (DOA) for servo motor parameter optimization and an Attention pyramid convolution neural network (APCNN) (APCNN) for system behavior prediction. Integrated with a Programmable Logic Controller (PLC) and human-machine interface (HMI), this approach offers a comprehensive solution. Our research identifies a significant research gap in the efficiency of existing methods, emphasizing the need for improved control parameter optimization and system behavior prediction for cost reduction and enhanced efficiency. Through implementation on the MATLAB platform, the proposed DOA-APCNN approach demonstrates a noteworthy 30% reduction in computation time compared to existing methods such as Heap-based optimizer (HBO), Cuckoo Search Algorithm (CSA), and Salp Swarm Algorithm (SSA). These findings pave the way for faster and more efficient pipe-cutting operations, contributing to advancements in industrial automation and control systems.&lt;/p&gt; &lt;/abstract&gt;

Performance Analysis of Object Placement Systems Based on Siemens S7-1200 its’ Evaluation of Effectiveness and Accuracy

The evolution of industrial automation systems has led to innovative solutions for efficient and precise object manipulation. This study introduces a groundbreaking "Object Placement System Based on Siemens S7-1200," designed to enhance object positioning tasks in manufacturing environments. The system's core control unit is the Siemens S7-1200 Programmable Logic Controller (PLC), seamlessly integrated with a network of sensors and actuators for coordinated operations. Operators can easily define target positions and parameters for object placement through a user-friendly human-machine interface. The PLC facilitates real-time data exchange with sensors, enabling accurate object detection and adjustments during placement. The system's implementation involves meticulous PLC programming, sensor integration, and interface design. Experimental results validate its capability to achieve precise and repeatable object placements. The Siemens S7-1200 is a versatile PLC supporting controlled positioning of electrically driven axes and technology objects, tailored for small automation systems requiring simple or advanced Human-Machine Interface (HMI and communication capabilities. The object placement system leverages the capabilities of the S7-1200 PLC for integrated sensor and actuator coordination, ensuring accuracy and repeatability. The study concludes that the innovative system utilizing the Siemens S7-1200 PLC underscores its potential for optimizing production processes and enhancing operational efficiency in industrial automation systems. The demonstrated ability to achieve precise and repeatable object placements highlights the viability of the Siemens S7-1200 platform for advancing object manipulation in industrial settings. The S7-1200 system manual provides specific information about the operation and programming of the PLC. Additionally, the system is supported by the TIA Portal programming software, which offers flexibility and ease of use.