Industrial Automation Applications Research Articles

Development of a DC motor control system by Xilinx system generator

This study introduces a numerical approach utilizing a Xilinx System Generator to develop a DC motor control system, showcasing a significant ability to regulate and enhance motor speed. We developed the proposed system in the MATLAB/SIMULINK environment, utilizing a collection of blocks generated by the Xilinx System Generator. We developed and simulated a proportional-integral (PI) controller and pulse width modulation (PWM). Subsequent to simulation, we can transfer the HDL code from the Xilinx System Generator block to a Field Programmable Gate Array (FPGA) board using Vivado. The system can produce accelerated PWM signals, thereby enhancing the control of the DC motor. Because of the FPGA board's superior processing capabilities, it was crucial to design and simulate the control system using Sysgen. We proposed the FPGA board to address this issue, facilitating the efficient and rapid execution of the control signals. This work represents a significant advancement in the enhancement of DC motor control systems. It shows how well Matlab/SIMULINK and Xilinx System Generator can work together to make PI and PWM controllers. These findings present compelling possibilities for future applications in power electronics and industrial automation.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

The role of telecommunications in enabling Internet of Things (IoT) connectivity and applications

This review paper explores the pivotal role of telecommunications in enabling Internet of Things (IoT) connectivity and applications, providing a comprehensive overview of the current landscape and future prospects. The primary objective is to synthesize existing research and industry insights to highlight how advancements in telecommunications infrastructure and technology have catalyzed the adoption and expansion of IoT. The review systematically examines the evolution of telecommunications technologies, including 4G, 5G, and emerging network protocols such as Narrowband IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). It assesses how these technologies enhance IoT connectivity by providing the necessary bandwidth, low latency, and reliability for real-time data exchange and device interoperability. Key findings from the literature reveal that the deployment of 5G networks has been a game-changer for IoT, offering unprecedented speeds and connectivity capabilities that support a wide range of applications in smart cities, healthcare, agriculture, and industrial automation. Additionally, the paper discusses the role of telecommunications in addressing challenges related to security, scalability, and energy efficiency in IoT deployments. The review concludes that telecommunications are integral to the growth and sustainability of IoT ecosystems. As telecommunications technology continues to advance, its ability to support increasingly complex and ubiquitous IoT applications will drive innovation and enhance efficiency across various sectors. The paper emphasizes the necessity for ongoing investment in telecommunications infrastructure and research to fully exploit the transformative potential of IoT connectivity and applications.

Virtual numerical control: an approach towards autonomous manufacturing with a case study in welding

Today’s customer lifestyles have reshaped their expectations and preferences, driving a growing demand for tailor-made products. While current conventional manufacturing (MNF) systems are robust, they often lack the flexibility needed to accommodate customization. Most MNF systems, despite advances in technology and machinery, still rely on executing predefined instructions, limiting their flexibility. In contrast, human workers excel at handling product variations due to their cognitive abilities, which allow them to perceive, analyze, and make appropriate decisions to adapt to changing conditions. This study introduces virtual numerical control (VNC) as a solution to upgrade MNF systems and overcome these limitations. VNC aims to transform MNF systems into cognitive entities capable of autonomous decision-making, enabling greater flexibility to meet customization demands. To demonstrate the potential of VNC, we implemented it in a welding system as a practical case study. The results showed that VNC enabled the system to operate autonomously. It accurately identified the shape of the objects to be welded, determined the appropriate welding paths, and executed them with high precision, all without human intervention. This highlights the significant potential of VNC technology for broader applications in industrial automation in welding and beyond.

Research on the application and prospects of variable reluctance inductive sensors

Variable reluctance inductive sensors are extensively utilized in the industrial field due to its unique working mechanism and good performance characteristics. Based on the working principle of Variable Reluctance Inductive Sensors, this paper reviews its development history, application in industrial automation and medical industry. Through the analysis of variable reluctance inductive sensors, variable reluctance inductive sensors have the advantages of high sensitivity, low cost and reliability which can support its wide application in precision measurement, position detection and motion control, but it is susceptible to the interference of the external magnetic field and the influence of the temperature which limits its development space and scope. However, the advancement of new materials, intelligent technologies and multi-mode integration has broadened the potential of future development of sensors and created more challenges for the development of variable reluctance inductive sensors. The update of new research conditions also provides a valuable reference of research and application in related fields

Time-Sensitive Networking (TSN) for Industrial Automation: Current Advances and Future Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

Towards an ultrafast 3D imaging scanning LiDAR system: a review

Light detection and ranging (LiDAR), as a hot imaging technology in both industry and academia, has undergone rapid innovation and evolution. The current mainstream direction is towards system miniaturization and integration. There are many metrics that can be used to evaluate the performance of a LiDAR system, such as lateral resolution, ranging accuracy, stability, size, and price. Until recently, with the continuous enrichment of LiDAR application scenarios, the pursuit of imaging speed has attracted tremendous research interest. Particularly, for autonomous vehicles running on motorways or industrial automation applications, the imaging speed of LiDAR systems is a critical bottleneck. In this review, we will focus on discussing the upper speed limit of the LiDAR system. Based on the working mechanism, the limitation of optical parts on the maximum imaging speed is analyzed. The beam scanner has the greatest impact on imaging speed. We provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning speed. Especially, we highlight the spectral scanning LiDAR as a new paradigm of ultrafast LiDAR. Additionally, to further improve the imaging speed, we then review the parallel detection methods, which include multiple-detector schemes and multiplexing technologies. Furthermore, we summarize the LiDAR systems with the fastest point acquisition rate reported nowadays. In the outlook, we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

FABRICATION AND PERFORMANCE OF AUTONOMOUS VEHICLE

Line following and obstacle avoidance are essential functionalities in robotics. A Line Following Robot utilizes arrays of optical sensors to track and follow lines autonomously. Equipped with four sensors, it ensures precise movement along the line. DC gear motors drive its wheels, controlled by an Arduino Uno interfacing algorithms for speed regulation and steering. Additionally, an LCD interface displays the traveled distance. This versatile robot finds applications in industrial automation, household tasks, and guiding systems in museums. Conversely, an obstacle avoidance robotic vehicle, built with an ATmega328 microcontroller and ultrasonic sensors, demonstrates intelligent navigation. When obstacles are detected, the microcontroller redirects the vehicle by actuating motors through a motor driver. Unlike infrared sensors, the ultrasonic sensors offer compatibility and reliability. This project amalgamates both functionalities to create a comprehensive robotic system capable of navigating along lines while autonomously avoiding obstacles. Key components include the Arduino UNO, motor shield L293d, ultrasonic sensor HC-SR04, DC motors, and servo motor.represents a significant leap towards a future where vehicles navigate with unprecedented efficiency and safety, heralding a new era of transportation. Keywords: Line Following Robot, Optical Sensors, DC Gear Motors, Arduino Uno.

Image Segmentation Technology Based on Ant Colony Algorithm

Image segmentation is a key task in computer vision, with applications ranging from medical diagnosis to autonomous driving. The Ant Colony Algorithm (ACO), modeled after ant foraging behavior, has emerged as a viable segmentation methodology. However, ACO-based segmentation algorithms frequently generate segmented outputs with jagged or uneven boundaries, which reduces their interpretability and usability. To alleviate this problem, they study the use of boundary-smoothing approaches in ACO-based segmentation. In this paper, they investigate image segmentation technology based on the Ant Colony Algorithm, with a focus on border smoothing. They examine the fundamentals of ACO and its application to image segmentation, emphasizing its strengths and limits. They also look at several boundary smoothing strategies, such as morphological operations, edge-preserving filters, and active contours (snakes), and how they affect segmentation performance. Through experimental validation and comparative analysis, they show that boundary smoothing improves the accuracy and visual quality of segmented images produced by ACO-based segmentation algorithms. These results help to design more robust and visually appealing segmentation algorithms, which have potential applications in medical imaging, remote sensing, and industrial automation.

High precision detection of artificial defects in additively manufactured Ti6Al4V alloy via laser ultrasonic testing

Laser powder bed fusion (LPBF) is a cutting-edge metal additive manufacturing technology with promising applications in aerospace, biomedicine, and industrial automation. Its ability to fabricate intricate geometric shapes, achieve high surface precision, and deliver comprehensive results renders it highly advantageous. In LPBF process, several factors such as the interference of laser processing fluctuations, rapid cooling rate, and environmental gas, contribute to the initiation and expansion of defects including pores and cracks, limiting the application of LPBF components. Laser ultrasonic testing (LUT) is a promising non-destructive evaluation method that can accurately detected defects in LPBF manufactured components by non-contact generation and detection. In this paper, a multi-physical field LUT model for the pores and cracks in LPBF additively manufactured Ti6Al4V alloy is proposed. The interaction of ultrasound with defects produces defect reflection echo wave, diffracted wave, and transmission wave. The transmission and diffraction phenomena occur in the longitudinal sound waves at defects. Furthermore, the echo wave reflected at crack exhibited enhanced prominence and an irregular shape. Clear differences were observed in the effects of laser ultrasonic detection on different defect types, pore depths and sizes. Finally, the LUT technology achieved the detection of 90 μm pore defects in LPBF manufactured Ti6Al4V alloy.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

FEATURES OF LINEAR MOTORS APPLICATION IN INDUSTRIAL AUTOMATION

Linear motors provide high precision, fast speeds, and a high force-to-weight ratio, making them ideal for applications where fast and accurate linear motion is required. Additionally, their direct-drive design eliminates the need for gears or mechanical linkages, which reduces friction and increases system efficiency. The article also mentions that linear motors can be controlled to produce different speeds and accelerations by varying the sequence and intensity of the current flowing through the coils. However, linear motors also have some disadvantages that must be considered, such as their cost, complexity, and sensitivity to environmental factors. Despite these drawbacks, the benefits of using linear motors make them a popular choice in many industrial automation applications.

Embedded Systems Basics

Embedded systems play a pivotal role in modern technological advancements, integrating computing capabilities into various devices and systems. This paper presents an abstract of an embedded system designed for real-time monitoring and control applications in industrial automation. The system utilizes a combination of microcontroller-based hardware and software components to achieve reliable and efficient operation.

A recommender system for human operators in industrial automation

This paper presents a recommender system to assist human operators in industrial automation applications, which suggests proper actions to take during process operation and those to avoid. The proposed recommender system is built based on a three-stage computational procedure. In the first stage, historical process data segments containing timestamped events are clustered using a k-medoids-based algorithm, where instead of distance metrics, structural similarity of the segments is used. This is achieved by using a Smith–Waterman-based algorithm to consider all events and their timestamps in the segments. In the second stage, a modified collaborative filtering technique applicable to time sequences is proposed, where not only operator ratings, but also the structural similarity of the segments are taken into account. In the third stage, possible actions are analyzed and accordingly, proper and improper actions are determined and presented to the human operators. The effectiveness of the proposed recommender system is examined through a power system operation example.

Deep learning anomaly detection in AI-powered intelligent power distribution systems

Introduction: Intelligent power distribution systems are vital in the modern power industry, tasked with managing power distribution efficiently. These systems, however, encounter challenges in anomaly detection, hampered by the complexity of data and limitations in model generalization.Methods: This study developed a Transformer-GAN model that combines Transformer architectures with GAN technology, efficiently processing complex data and enhancing anomaly detection. This model’s self-attention and generative capabilities allow for superior adaptability and robustness against dynamic data patterns and unknown anomalies.Results: The Transformer-GAN model demonstrated remarkable efficacy across multiple datasets, significantly outperforming traditional anomaly detection methods. Key highlights include achieving up to 95.18% accuracy and notably high recall and F1 scores across diverse power distribution scenarios. Its exceptional performance is further underscored by achieving the highest AUC of 96.64%, evidencing its superior ability to discern between normal and anomalous patterns, thereby reinforcing the model’s advantage in enhancing the security and stability of smart power systems.Discussion: The success of the Transformer-GAN model not only boosts the stability and security of smart power distribution systems but also finds potential applications in industrial automation and the Internet of Things. This research signifies a pivotal step in integrating artificial intelligence into the power sector, promising to advance the reliability and intelligent evolution of future power systems.

An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors’ lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network’s lifetime: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> ) how to balance energy consumption between mobile sensors using their mobility capabilities; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> ) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper provides pseudo code, mathematical expressions, performance evaluation, and results, which can serve as a practical implementation guide for practitioners.

Call for Papers IEEE Transactions on Automation Science and Engineering Special Issue on Beyond Classic Deep Learning: Algorithms for Dealing with Real-World Applications in Industrial Automation

Call for Papers IEEE Transactions on Automation Science and Engineering Special Issue on Beyond Classic Deep Learning: Algorithms for Dealing with Real-World Applications in Industrial Automation

Virtual Commissioning with TIA Step7 and Simulink without S‐Functions

This paper presents the development and validation of a custom MATLAB library, designed to facilitate seamless and efficient real‐time communication between the Siemens S7‐PLCSIM Advanced simulator and MATLAB/Simulink. The library uses Siemens.Simulation.Runtime.API to enable this integration and its architecture is structured into three classes—PLCSimAdv, PLCSR, and PLCSW. To demonstrate the library’s capabilities, we have chosen the validation parameters and conducted functional experiments including real‐time communication at the millisecond level, integration and control of a simulated industrial process, and simultaneous operation with multiple virtual controllers. One of the library’s preliminary conditions was to avoid the requests for regenerating of any version of control program or Simulink model as it is presently by S‐functions in TIA portal. The results suggest that the proposed library provides a robust tool for various applications in industrial automation, from the digital twin modelling or simulation of advanced technologies to the operation of traditional control systems. This work has the potential to significantly streamline the simulation, control, and validation of automated processes and opens new ways for research and development in automation and control systems.

EAKRR : An energy-efficient key management in WSN with ECC-based cryptography

Environmental monitoring, industrial automation, and other applications require Wireless Sensor Networks (WSN). WSN must efficiently manage cryptographic keys for safe communication and energy efficiency. This study introduces “Energy-Aware Key Revocation and Rekeying” (EAKRR) to address energy efficiency and security in WSN using Elliptic Curve Cryptography (ECC). Energy-efficient key management is needed in WSN which have sensor nodes with limited resources and energy. EAKRR is designed to adapt to sensor node energy levels to make crucial key revocation and rekeying decisions while maintaining strong and resilient security. Energy profiling is used by EAKRR to assess sensor node energy consumption during cryptographic and operational operations. Energy conservation is achieved by localized key revocation when a node’s energy drops below a threshold. EAKRR also optimizes security and energy efficiency with ECC-based adaptive key length modifications. EAKRR is compared to LEACH and AES to determine its efficacy. The results show that EAKRR is highly energy efficient, extending the network’s lifespan and maintaining security. Cryptographic operations are careful and wise because they can adjust to energy levels in real time. The balance between security and energy usage shows that ECC-based encryption works. Energy-efficient key management in WSN is crucial. EAKRR solves WSN problems by saving energy, extending network life, and securing the network.

Deep learning-based position detection for hydraulic cylinders using scattering parameters

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that outlines the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To address these limitations, we propose machine learning and deep learning-based methods to learn the relationship directly in a data-driven manner. As a result, all deep learning models in this paper consistently outperform the physical one by a large margin. We further deliberate on the choice of models based on domain knowledge and provide in-depth analyses combining model performance with real-world physical characteristics. Specifically, we use Convolutional Neural Network (CNN) to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. The combination of these techniques results in our best-performing model, a complex-valued CNN with Frequency Encoding, which exhibits substantial improvement in accuracy with an error reduction of 1/12 compared to the traditional physical model.