Real-time Equipment Research Articles

Internet of Things Implementation Project in Industry 4.0: Opportunities and Challenges

Objective: In Industry 4.0, Internet of Things (IoT) is considered a basic technology that can be used in several areas, from to real-time equipment monitoring and control to other services, but its implementation is surrounded by challenges. The objective of this study is to analyze the most relevant opportunities and challenges in the IoT implementation projects. Method: The methodology used was literature review and field research developed in two manufacturing plants. To classify the opportunities and challenges under study, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multicriteria decision-making method (MCDM), was used. Results and Discussion: The main opportunities identified were: IoT helps reduce production costs, and IoT technology reduces production waste, and the main challenges were: there is uncertainty about the return on investment in IoT, and the lack of professional qualifications hinders/prevents the implementation of IoT. Research Implications: A practical and theoretical implication of this research is to present concerns about the possibility of having cleaner production practices in parallel with cost reduction in IoT implementation projects. Originality/Value: The theoretical relevance of this study is to classify opportunities and challenges according to their degree of importance. The practical contribution is to help those responsible for IoT implementation projects, so that they can try to maximize opportunities and minimize challenges.

ANALYTICAL REVIEW OF THE METHODS AND MEANS OF CONTROLLING ELECTRIC DRIVES OF ELECTROMECHANICAL SYSTEMS OF APC

Control of electric drives in electromechanical systems of the agro-industrial complex (APC) is a critical factor in increasing the efficiency and reliability of technological processes that directly affect the productivity of agricultural production. In connection with the introduction of the latest technologies, the growth of product quality requirements and the need to automate processes, modern agricultural technologies require the improvement of electric drive control methods. The relevance of this topic is due to the need to optimize management processes in the agricultural sector to increase productivity, reduce energy consumption, and increase system reliability. The review examines classical and modern approaches to the control of electric drives. Traditional proportional, integral and differential methods ensure the stability of systems and minimize errors, adapting to the conditions of agricultural production. An important role is played by feedback systems that automatically adjust the operating parameters of electric drives in response to changes in external factors. Fuzzy management systems are also an important part, allowing decisions to be made based on expert judgments in cases where accurate data is difficult to obtain. Adaptive management systems capable of changing their parameters in conditions of variability are considered separately, which is especially important in the agricultural sector with its changing conditions. The article also highlights modern intelligent control systems using artificial intelligence, machine learning and remote monitoring, which allow analyzing large volumes of data, predicting resource needs and increasing energy efficiency. Internet-based systems such as SCADA provide operators with the ability to remotely monitor and control equipment in real time, an essential element of modern agricultural production. In conclusion, the control of electric drives in agriculture is aimed at automating and increasing the efficiency of production processes, optimizing the use of energy resources and ensuring the safety of the systems, which makes them an integral part of modern agricultural technologies.

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL- a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Electromagnetic intensity investigation of emitted non-ionizing radiation from base transcriptive stations in the urban region of southern Iran

ABSTRACT Their monitoring in urban regions is essential for policymakers and the population. In this study, the electromagnetic intensity (EMI) was measured around 30 stations of base transcriptive stations (BTS) at both distances lower than 20m and higher than 200m using real-time equipment SMP2-dual in summer and winter. Results have shown that EMI in summer (range: 2-6500 mW/m2) was more than in winter (1.5-5000), and the intensity of about 93% of samples exceeded WHO standards. EMI has consistently decreased with the increasing distance from BTS. There was a negative correlation between the temperature and EMI in summer and humidity and EMI for both distances. The mapping of EMI depicted the highest value of EMI across the central region from south to eastern north at a distance lower than 20 m in winter. The clustering of the EMI in this region was influenced by the geographical location of BTS.

Research on the Application of Intelligent Technologies in Equipment Management of Vocational Logistics Training Rooms

As the importance of vocational logistics training rooms in education continues to grow, issues such as low equipment management efficiency, untimely maintenance, and underutilization of resources have become increasingly apparent, severely impacting teaching effectiveness. The rapid development of intelligent technologies offers new solutions in this field. Technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are widely applied in the logistics industry and gradually provide efficient and refined management methods for vocational logistics training room equipment management. This study analyzes the shortcomings of existing management models, explores key applications of intelligent technologies in real-time equipment monitoring, maintenance optimization, and resource scheduling, and constructs an intelligent equipment management system aimed at improving management efficiency, reducing maintenance costs, and enhancing teaching effectiveness. The research provides theoretical support and practical references for the intelligent transformation of equipment management in vocational logistics training rooms.

Predicting Factory Equipment Lifespan Through Manufacturing Data Analysis using AI

Recently, research on applying artificial intelligence (AI) to various industries, especially manufacturing, is being actively conducted. In the field of smart factory, the purpose is to improve productivity based on data generated in the process of producing or processing products. The tool breakage during metal product processing causes fatal difficulties of predicting tool life. Moreover, if tool life is not predicted, defects may occur product reliability deteriorate, which may adversely affect product performance or economic aspects. In this paper, data related to machining is collected from CNC equipment in real time, and through machine learning and deep learning, which factors affect the wear of cutting tools are identified and the lifespan of cutting tools is predicted. An AI-based solution was applied to the system, productivity improved due to an increase in tool life.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Curriculum teaching of mechatronics in the information age

Abstract: In the wave of digital education, the teaching design and practice of mechatronics integration courses in the information age become the key to innovative education. The course combines online teaching, virtual experiments and remote control techniques to break the limitations of a traditional classroom. Through multimedia courseware, real-time interactive platform and virtual simulation laboratory, students can not only master theoretical knowledge flexibly, but also conduct practical training online. Remote control technology enables students to operate practical equipment in real time and enhance their practical skills. The preliminary practice shows that this mixed teaching mode effectively improves students' interest in learning and practical ability, and provides a new direction and opportunity for mechatronics education.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Optimization of electronic sensors based on the connection structure of double rotors with circular arc end teeth

To analyze the mechanical performance of the arc end tooth connection structure, the optimization of electronic sensors based on the connection structure of double rotors with circular arc end teeth was studied. By designing and optimizing the structural dimensions of the arc end tooth, the relative position of the grinding wheel and the workpiece was determined, and the optimal electromagnetic actuator size, as well as the number and installation position of the optimized sensors, were obtained. The experimental results showed that the relative error of the rigid vibration mode of the arc end tooth double rotor connection structure was controlled within 10 %. At high speeds, the electromagnetic actuator had a significant control effect on the vibration of the centrifugal machine shaft. It diminished the vibration amplitudes in the X and Y directions by 37.6 % and 30.2 %, respectively. By optimizing the double rotor connection structure with circular end teeth and electronic sensors, effective control of the relative error of rigid vibration modes has been achieved, leading to significant reduction of the centrifugal shaft's vibration at high speeds. In addition, the sensor layout was optimized to enable real-time equipment vibration monitoring, ensuring equipment safety and stability. These achievements not only enhance the mechanical performance of the rotor system, but also provide important guarantees for the stability of the mechanical system.

Vibration prediction and analysis of the main beam of the TBM based on a multiple linear regression model

The vibration of tunnel boring machine (TBM) is very difficult to monitor on sites, and related research on prediction methods is rare. Based on the field tunnelling test of a TBM in the Xinjiang Ehe project, the vibration information of the main beam of the TBM under different surrounding rock conditions is collected. The relationships among the tunnelling parameters, surrounding rock parameters and vibration parameters were studied. The results show that the penetration, cutter head speed, torque and thrust are important parameters affecting TBM vibration. In addition, the field penetration index and cutter head driving power index are significantly related to the root mean square of acceleration. Based on this, a multiple regression prediction model of TBM vibration is established. The model was verified and analysed via field projects, and the relative prediction error was less than 12%. This method can be used to predict the vibration of a TBM in real time through characteristic parameters without the use of a traditional monitoring system. This approach is highly important for determining the status of TBM equipment in real time.

GRU 기반의 농장 내 전력량 관리 및 이상탐지 자동화 시스템 설계

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces

Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep learning and computer vision techniques (referred to as DeepVision), this method aids the operation of demand-driven ventilation systems in highly polluted spaces. A Faster RCNN model was employed and trained to detect kitchen equipment usage in real-time. The ventilation rate for the kitchen space was adjusted based on this real-time detection. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, accurately identifying appliances in use, such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while also achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates in response to real-time equipment usage. When the fan speed was varied according to the real-time detection method, the PM2.5 concentration in the cooking period was 16.5 % lower, with only a 3.7 % rise in fan power and a 0.8 % rise in daily heating demand, compared to using a constant fan speed.

Insulation aging and fault diagnosis of high-voltage equipment based on deep Q-networks

In response to the escalating demand for electric energy in the world, the maintenance technology for electric power equipment has evolved significantly. This study addresses the critical issue of insulation aging in high voltage equipment through an innovative approach. By integrating advanced Deep Q-networks (DQN) algorithms and intelligent data mining techniques, our proposed diagnostic scheme enables accurate and timely fault detection. The intelligent DQN algorithms process real-time equipment data, providing essential insights into operational status and faults. Implementing this research offers a strategic framework for enhancing the reliability of high-voltage equipment, contributing to stable system operation, and laying the groundwork for future advancements in equipment condition maintenance.

Application of ship’s information system to control and monitor the technical condition of ship’s equipment

Failures and breakdowns of marine equipment that occur during the daily operation of ships can lead to major accidents, endangering the lives of the crew and passengers of ships, and also pose a threat to the environment. A project for using a ship information system to monitor the technical condition of ship power equipment in real time through integration with ship automated systems is presented in the paper. The implementation of the information system was carried out on the Handy Size dry-cargo vessel with a deadweight of 36,987 tons, during which an analysis of ship systems was carried out and sources for obtaining initial information were identified. These were AMS system (alarm monitoring system), technical control system of MAN 5S50ME-B “CoCoS” main engine condition, equipment on the navigation bridge (AIS, GPS, ECDIS, VDR). During the implementation of the project, a secure connection to the above-mentioned ship systems for collecting and processing information using the installed Dataloger computer with software was made. Channels for transmitting information to the shore through the satellite Internet and mobile communication systems using data coding and compression to reduce traffic volume were determined. The use of the implemented information system makes it possible to remove some of the routine tasks for the crew and the onshore technical service, to obtain more accurate information on the formation and control of maintenance tasks in real time.

육계농장의 ICT장비가 생산한 빅데이터 분석을 활용한 질병발생예측모델(DOPM) 개발연구

Since 2014, the government has promoted the ICT equipment support project for livestock farmers as an alternative system to address the rapid aging of livestock farmers and sharp decline in labor force due to population decline. This project aims to realize more precise and scientific smart livestock farming through the analysis of big data produced by ICT equipment in real time. Prior to the introduction of ICT equipment, farmers relied on their long-term experience in farm operations to implement emergency measures by using a sudden increase in the number of dead birds or sudden change in the amount of water consumption data as an indicator of the occurrence of disease. However, damage and loss to farmers have not been reduced with these measures. Therefore, this study aims to address the farmers' consistent desire to develop a disease outbreak prediction model (DOPM) that can be easily utilized in the field and has high precision. The current study developed a disease outbreak prediction model (DOPM) that can find a singularity (golden time) in the data even if only one type of ICT data (Water & Feed) is properly monitored and analyzed. We found that if the time when a sharp decrease (more than 100% decrease from the previous day), confirmed through the daily feed intake data for two consecutive days (golden time = DOPM), is set as the start of the disease outbreak and preventive measures are taken quickly, this model can be used to minimize farm losses and damages better than the existing method that relies on analog data and experience. Additionally, the DOPM was confirmed to be the fastest, most sensitive, and most accurate model in the field poultry farms. As a result, we have found a method to interpret non-verbal signals of the chickens with the transformed ICT-Data in the AI-age.

Risk Assessment for Energy Stations Based on Real-Time Equipment Failure Rates and Security Boundaries

In the context of China’s 2020 dual carbon goals of peak CO2 emissions by 2030 and carbon neutrality by 2060, the security of multi-energy systems is increasingly challenged as clean energy continues to be supplied to the system. This paper proposes a risk assessment and enhancement strategy for distributed energy stations (DESs) based on a security boundary. First, based on the coupling relationship between different energy sources and combining the mutual support relationships between different pieces of equipment, a security boundary for DESs was constructed. Second, based on the characteristics of different sources of equipment failure, the real-time failure probabilities of equipment and pipelines were calculated in order to obtain the security risks of DES operation states based on the security boundary. Finally, for equipment and pipelines at high risk, an economic security enhancement strategy is proposed, and the Pareto solution set is solved using a multi-objective algorithm. The analysis shows that the proposed method can effectively quantify the security risks of energy systems in real time, and the proposed enhancement strategy takes into account both economics and system security.

Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software

Predictive maintenance is one of the most important topics within the Industry 4.0 paradigm. We present a prototype decision support system (DSS) that collects and processes data from many sensors and uses machine learning and artificial intelligence algorithms to report deviations from the optimal process in a timely manner and correct them to the correct parameters directly or indirectly through operator intervention or self-correction. We propose to develop the DSS using open-source R packages because using open-source software such as R for predictive maintenance is beneficial for small and medium enterprises (SMEs) as it provides an affordable, adaptable, flexible, and tunable solution. We validate the DSS through a case study to show its application to SMEs that need to maintain industrial equipment in real time by leveraging IoT technologies and predictive maintenance of industrial cooling systems. The dataset used was simulated based on the information on the indicators measured as well as their ranges collected by in-depth interviews. The results show that the software provides predictions and actionable insights using collaborative filtering. Feedback is collected from SMEs in the manufacturing sector as potential system users. Positive feedback emphasized the advantages of employing open-source predictive maintenance tools, such as R, for SMEs, including cost savings, increased accuracy, community assistance, and program customization. However, SMEs have overwhelmingly voiced comments and concerns regarding the use of open-source R in their infrastructure development and daily operations.

Optimizing generating unit maintenance with the league championship method: A reliability-based approach

The electrical power industry has experienced an unprecedented pace of digital transformation as a prevailing economic trend in recent years. This shift towards digitalization has resulted in an increasing interest in the collection of real-time equipment condition data, which provides opportunities for implementing sensor-driven condition-based repair. As a result, there is a growing need for the development of generator maintenance scheduling to consider probabilistic equipment behavior, which requires significant computational efforts. To address this issue, the research proposes the use of a meta-heuristic league championship method (LCM) for generator maintenance scheduling, considering random generation profiles based on generation adequacy criteria. The experimental part of the study compares this approach and its modifications to widely used meta-heuristics, such as differential evolution and particle swarm methods. The identification and demonstration of optimal method settings for the generation maintenance scheduling problem are presented. Subsequently, it is illustrated that employing random league scheduling expedience can reduce the variance of objective function values in resulting plans by over three times, with values of 0.632 MWh and 0.205 MWh for conventional and proposed techniques respectively. In addition, three approaches are compared to assess generation adequacy corresponding to different schedules. The study emphasizes the efficacy of employing the LCM approach in scheduling generator maintenance. Specifically, it showcases that among all the methods examined, the LCM approach exhibits the lowest variance in objective function values, with values of 38.81 and 39.90 MWh for LCM and its closest rival, the modified particle swarm method (MPSM), respectively.