Industrial Automation Applications Research Articles

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.

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

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.

Parameters Identification of a Permanent Magnet DC Motor: A Review

Since permanent magnet direct current (DC) motors are mainly used in various industrial automation applications, the demand for electric motors is increasing rapidly. However, in the mass production of electric motors, often, only random inspections are used to check the specifications and performance of electric motors. For manufacturing or engineering application staff to have a more thorough understanding of the characteristics of the motor, it is necessary to conduct a full or quick inspection during the production process to ensure the quality of the electric motor. Based on this, this literature review reveals several methods and algorithms often used to estimate DC motor parameters, given the importance of knowing the parameters of the DC motor and the lack of research on estimating the parameters of the DC motor.

A BCI framework for smart home automation using EEG signal

Since the first recording in 1924, modern developments in technology have enabled human electroencephalogram (EEG) acquisition as a non-invasive process, enabling a multitude of opportunities to learn about human brain dynamics. With the capability to tap into localized brain dynamics, it is possible to correlate event-related potentials such as meditation, concentration, and motor control with localized brain activities, opening up a broad spectrum for exploration and implementation in prosthetic, control, and brain computer interfaces. In this work, an attempt has been made to explore human emotions and other intelligent states that can be interpreted to automate and control electrical appliances for differently abled people. A smart home automation model has been designed and implemented using a Think Gear Application-specific integrated circuit (ASIC) Module (TGAM) EEG sensor module integrated with a Bluetooth module, which serves as the core for control applications. A combination of external triggers and brain states are interpreted and forwarded to gain control of the connected appliances via a local server over the internet. Equipped with internet connectivity and Internet of Things (IoT), the system also facilitates long-distance communication and control, which can be easily translated to industrial control and automation applications. Based on a single-channel analog EEG signal acquisition module, this study details the development of a Brain Computer Interface (BCI) control and monitoring system for smart home automation with blink and attention features. The designed BCI system has a large bandwidth of 400 Hz, an easy setup, Morphological Component Analysis (MCA) based blink detection, monitoring, control of devices, and high accuracy at a low cost. Each subject completed three trials separated by one minute. The smart devices underwent testing in two states, namely on and off. The response time and accuracy of the system were recorded for each trial. The average system response time for the devices was determined to be 17.13 sec for switching ON and 20.66 sec for switching OFF, with an accuracy of 98.73% and 95.75% for ON and OFF states respectively.

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.

A simple signal extraction‐based online real‐time diagnosis approach for interturn short‐circuit fault of permanent magnet motor

SummaryA fast and reliable interturn short circuit (ITSC) diagnosis system is necessary to secure safe operations of permanent magnet synchronous motor (PMSM) for its widespread applications in industrial automation and electric vehicles. This paper develops a simple online real‐time technique based on signal extraction to detect the ITSC fault in the PMSM. Some popular diagnosis methods involving machine learning or instantaneous power information may require large computing resources, data storage, or extra sensing devices for data collection. This makes it difficult to fulfill online and self‐detection of ITSC faults in motor drive systems. This paper makes use of the second‐order harmonic components in dq‐axis current for diagnosis when they occur due to ITSC faults. Through the developed amplitude‐tracking algorithm, the diagnosis can be implemented by using a common digital signal processor without complex algorithms and additional hardware or sensors. Therefore, it is suitable for low‐cost and remote applications where self‐detection is needed. The proposed method is validated by finite element analysis and measurements with a hardware‐in‐the‐loop toolkit. It is found that the method successfully detects a short circuit fault with only one out of 54 turns in one phase of the motor (1.9%).

Medium-Voltage Seven-Level Multiplexed Converter for AC Applications

After several decades of technological evolution, power electronics applications have become extremely important for electric/hybrid propulsion systems, smart grids, renewable energy systems, energy saving, and bulk energy storage, besides the typical applications in industrial automation and high-efficiency energy systems. In all these applications, a power conversion system with high efficiency and power density is required without sacrificing the system's power quality. The paper describes the specific characteristics and the scalar modulation strategy of a new multi-level multiplexed inverter topology for medium-voltage AC applications. The proposed power converter configuration is intended for high-power applications aiming to reduce the active device's voltage rating and losses exhibiting a resulting low switching frequency of the power switches. The proposed modulation strategy aims to reduce the computational burden and simplify the implementation process, which are at the basis of any possible industrial adoption. Simulation results and full Hardware-In-the-Loop verification support the analysis. The Hardware-In-the-Loop assessment has enabled the development of a suitable modulation strategy, and the testing of the power converter is a safe environment.

PLC-BASED MODERN PROTECTION SYSTEM OF ELECTRIC MOTORS

In the past, simple protection systems of electric motors based on components such as timers, contactors, electromagnetic switches, and voltage and current transformers were slow and inaccurate, as well as low sensitivity. However, the production of PLCs and their application in this field eliminated the mentioned problems. Extensive operation of motors, being the main executive equipment in the field of application, requires the creation of modern automated protection systems to ensure their reliable and stable operation. Today, PLCs are continuously evolving and striving to be the best choice for various industrial automation applications. Since the PLC-based protection system is a reliable, fast, high-precision system, modern protection systems based on PLC of electric motors are researched in the article.

Indexing in WoT to Locate Indoor Things

The Web of Things (WoT) is an enhanced form of the Internet of Things (IoT) that has changed the trend of life nowadays. Due to IoT, life is transformed into smart life, such as smart buildings, smart vehicles, smart agriculture, smart businesses, etc., by connecting a certain number of things to the internet. Many people are now working on ways to locate indoor things to interact and exchange data between smart things and web services and apps, which is called “WoT,” or “Web of Things.” To interact and exchange the data, researchers need a search engine on WoT. However, locating indoor things in the Web of Things (WoT) remains a challenge due to the lack of a unified indexing system. In this research, we propose a novel approach to index indoor things in the WoT by leveraging machine learning and web technologies. Our approach includes a data preprocessing step, where we extract relevant features from the sensor data, followed by a clustering algorithm to group similar devices. We then use a semantic model to assign meaning to the clusters and develop a search engine to enable efficient searching of indoor things. Our proposed approach improves the accuracy and efficiency of locating indoor things in the WoT, paving the way for new applications in smart homes, healthcare, and industrial automation.

Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology.

Research on Equipment Management Decision Model based on Semantic Analysis

In recent years, the industrial automation of cigarette enterprises has made great progress, and the underlying data acquisition, centralized control and condition monitoring systems have been established. The management has also established a large number of application systems. There are a large number of equipment data resources in these industrial automation systems and application systems, but incremental data value mining is difficult to really solve the problem. The exploration of big data and machine learning has gradually encountered bottlenecks and ceilings, Lack of integration of domain knowledge, it is difficult to mine high-value application scenarios. From the point of view of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application, which is the basis of effective data collection and management. From the perspective of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application.