System Equipment Research Articles

A Vibration Signal-Based Active Noise Control Method for Liquid-Filled Pipelines

Pulsation noise in the piping system generated by the excitation of the pump source seriously affects the reliability of the pipeline system and mechanical equipment. The active noise control can effectively suppress the low-frequency noise in the liquid-filled pipeline. Active control methods with intrusive secondary sources generally use dynamic pressure sensors or hydrophones to collect signals, which destroy the structure of the pipe. In this paper, we propose an active noise control method utilizing signals acquired by accelerometers, which adopts offline modeling of the secondary path and the notch narrowband FxLMS algorithm for controlling the secondary source actuation. The feasibility of this method is verified by LabVIEW simulation and active noise control test of the liquid-filled pipeline. The test results show that this method can achieve more than 4 dB reduction for low-frequency (10~200 Hz) line spectrum noise under most operating conditions.

Analisa Pengaruh Main Pressure Tank Cellar dalam Proses Discharge Semen di MV. Cepat

Discharge activities on a cement vessel can’t be separated from the discharge pneumatic system, because the bulk cement contained in the cellar tank must be pressurized so that the cement turns into dust and can be unloaded into the land sillo. Failure of the pneumatic system will pose a risk of time delays and losses for the company because it can’t carry out load discharge activities. When the cement discharge activity takes place, there are many problems with the pneumatic system due to the decrease in wind pressure. The purpose of this study was to determine the factors, impacts and efforts made to analyze the effect of main pressure on the cellar tank in the cement discharge process to reduce the risk of delays in cement discharge activities. This study used descriptive qualitative method. Where is described and explained each problem with a detailed explanation. Data collection techniques with the method of observation, interviews with the chief officer and supported by the method of documentation. Damage to the pneumatic valve system causes reduced pressure in the cellar tank to be a factor in the decrease in main pressure. The impact of decreasing main pressure causes cement discharge activities to not be carried out optimally and can even hinder cement discharge activities. The conclusion obtained is that efforts need to be made to improve the skills of the crew on the operation of the pneumatic system and control, routines for the maintenance of the pneumatic system equipment according to the system maintenance plan.

Investigation of the Ocular Response and Corneal Damage Threshold of Exposure to 28 GHz Quasi-millimeter Wave Exposure.

Electromagnetic radiation energy at millimeter wave frequencies, typically 30 GHz to 300 GHz, is ubiquitously used in society in devices for telecommunications; radar and imaging systems for vehicle collision avoidance, security screening, and medical equipment; scientific research tools for spectroscopy; industrial applications for non-destructive testing and precise measurement; and military and defense applications. Understanding the biological effects of this technology is essential. We have been investigating ocular responses and damage thresholds comparing various frequencies using rabbit eyes and dedicated experimental apparatus. In this study we investigated the 28 GHz quasi-millimeter wave band (wavelength: 10.7 mm), a candidate for 5G communication. Similar to millimeter wave frequencies, ocular damage from exposure to 28 GHz for 6 min (400 mW cm-2) included corneal epithelial damage, corneal edema, and opacity. The incident power density threshold, indicating a 50% probability of ocular damage from exposure for 6 min, was found to be 359 mW cm-2 for 28 GHz. Comparing the ocular exposure area for various millimeter wave frequencies (40, 75, 95 GHz) and 28 GHz quasi-millimeter waves using a thermosensitive liquid crystal capsule, we found that for millimeter waves, even at identical incident power densities, the ocular exposure area decreases as the frequency increases (lens effect). However, this lens effect was not observed at 28 GHz, where the entire anterior segment area was exposed to radio waves.

Research on adaptive control law of heat storage and heat exchange system for airborne electronic equipment

Research on adaptive control law of heat storage and heat exchange system for airborne electronic equipment

Study on energy-saving design of renewable energy applied to high-rise residential buildings

ABSTRACT Improving building energy systems is a major research hotspot due to the rising demand for indoor comfort and buildings’ increasing energy consumption. The research object in this work is a high-rise residential building in Nanjing. The photovoltaic system and ground source heat pump system are introduced into the traditional cooling and heating source system for energy-saving design of the building. Based on OpenStudio software, two photovoltaic systems, household photovoltaic panels and centralized rooftop photovoltaic panels, are analyzed in terms of dynamic energy efficiency on a time-by-time and day-by-day basis. In addition, the ground source heat pump system is studied in comparison with conventional split air conditioning system. Meanwhile, the energy-saving performance of systems is analyzed during the cooling, heating and transition seasons. The results showed that both PV systems can afford a total of 39.5% of the electricity demand for lighting and equipment systems throughout the year. The total primary energy consumption per unit area of the optimized building model is 51.17 kWh/(m2·a), which is 35.2% lower than that of the original building model. In addition, the energy replacement rate of the photovoltaic system is 23.7% and the contribution of renewable energy from ground source heat pump is 38.76%, which meets the national standard for near-zero energy buildings.

A Study on Detection System for Personal Protective Equipment of Entrants

A Study on Detection System for Personal Protective Equipment of Entrants

Influence of temperature on energy production performance at the Azito thermal power plant (southern part of Côte d'Ivoire)

Technological advances in plant design, cooling and materials are essential to optimise performance under variable conditions. These factors are even more critical under climate change, which may exacerbate these thermal challenges. In this study, the impact of temperature variations on the energy production efficiency of the Azito thermal power plant is investigated using the THERMOFLOW model. The simulation results show that the variation of the atmospheric temperature is an important factor that affects the operation of the basic equipment and the auxiliary equipment of the power generation units. The characteristics of the gas turbine, an essential piece of equipment in the combined cycle system, fall short of the manufacturer's specifications. This will reduce the efficiency of the unit.

A Review of Multi-Objective Optimization Methods of Grid-Connected Hybrid Renewable Energy Systems Combined with Electrical Vehicle Technique

Renewable Energy Sources (RESs) are regarded as highly promising and rapidly advancing forms of renewable energy. The commonly used RESs are solar energy and wind energy. However, obstacles are found in designing RESs. Using vehicle-to-grid (V2G) technology in combination with electric vehicles (EVs) and RESs in smart grid are of great significance for ensuring energy security, preventing air pollution, and promoting energy saving and emission reduction. Over the past decade, V2G technology has enabled EVs to become a potential energy storage capacity for alleviating the random fluctuation in renewable energy generation. Nevertheless, the primary drawbacks of these systems are inefficient energy conversion and substantial initial investment. The sizing of each piece of equipment in the hybrid renewable energy system (HRES) is challenging. Hence, it is imperative to employ a precise sizing technique to determine an ideal arrangement and meet the requisite load requirements. Hence, before the installation of RESs, it is crucial to consider the types and arrangements of photovoltaic (PV) panels and wind turbines (WTs), mathematical models of PV modules and WTs, storage battery options, environmental-economic-technical considerations, sizing methods based on techno-economic objectives, and the ultimate selection of the most optimal configuration. This work lists the general classification of optimization formulation framework, and multi-objective optimization methods of HRESs integrated with electrical vehicle technique are reviewed. This workprovided a thorough examination of the current advancements in the design optimization of HRESs using multi-objectiveoptimization (MOO). This study aims to select the most appropriate design before installing HRESs and provides afoundational platform for scholars interested in exploring the integration of RESs with EVs for further advancement.

IMAGE RECOGNITION FOR BEARING FAULT DETECTION BASED ON CONVOLUTIONAL NEURAL NETWORK

Bearing is an essential component for rotary machinery. The bearing serves as a fixture for position and provides stability for rotation. Bearing failure has detrimental effects on production schedules and operations. Consequently, detecting and diagnosing bearing issues in advance ensures the safety and reliability of rotating equipment systems, which will definitely save production costs and time. Therefore, this paper proposes the use of image recognition based on a convolutional neural network (CNN) for machine fault detection. Recent years have seen the development of deep learning-trained artificial intelligence, which aims to reduce human-induced errors and expenses. Initially, we acquire the vibration signals of the bearing from a test rig under four different conditions. We consider four bearing conditions: healthy bearings, inner race defects, outer race defects, and ball bearing defects. Each of the four conditions is recorded in the vibration time-series data, then converted into spectrogram images before feeding it to the CNN model for training. The performance of the CNN model is based on the comparison of two different models, which are Model A and Model B. Model B is developed based on the performance of Model A, where hyperparameter tuning is implemented to improve the performance. The result shows that the proposed model is capable of detecting and classifying the bearing faults up to 99.9% accuracy.

METHODOLOGY FOR OPTIMIZING THE FUNCTIONING OF THE OPTOELECTRONIC SURVEILLANCE SYSTEM

Context. Radar, thermal imaging, and video surveillance means are actively used in the protection of the state border. Together with the appropriate communication equipment, they allow to create optoelectronic surveillance systems, which are the basis for the intellectualization of border protection. The effectiveness of such systems is significantly affected by the peculiarities of their functional and structural design. A rational structural design, even in difficult physical and geographical conditions, allows for a high level of surveillance efficiency. However, the functional component also has a significant impact on improving the system efficiency. In many cases, the functioning of the elements of the optoelectronic surveillance system occurs under conditions of power supply restrictions. Such limitations determine the need for a rational choice of modes of use of certain types of surveillance equipment at certain time intervals in order to ensure effective surveillance, taking into account the time of day and weather conditions. The imperfection of the scientific and methodological apparatus for optimizing the functioning of optoelectronic surveillance systems determines the relevance of this study. Objective. The aim of the work is to develop a methodology for optimizing the optoelectronic surveillance system functioning by rationally selecting the modes of operation of different types of surveillance equipment in certain time intervals, taking into account the time of day and weather conditions in which they are used. Methods. The paper sets and investigates the two-criteria problem of choosing the modes of operation of different types of observation equipment of an optoelectronic surveillance system at separate time intervals, taking into account the time of day and weather conditions in which they are used, which ensures maximizing the efficiency of the optoelectronic surveillance system while minimizing the power consumed by active types of surveillance equipment in the presence of boundary restrictions on the efficiency and power consumed by the system. The proposed indicator for assessing the effectiveness of the system allows us to assess the level of impossibility of uncontrolled crossing of the perimeter of the protected area by an intruder. The peculiarity of this methodology is the possibility of ensuring a significant reduction in the level of energy consumption by the system components due to a slight decrease in the efficiency of monitoring. Results. The paper proposes an alternative approach to assessing the effectiveness of the optoelectronic surveillance system, the idea of which is that instead of assessing the effectiveness of surveillance over the entire sector of the controlled area of the border, the effectiveness of control is assessed only along the perimeter of this area. This approach significantly reduces the computational complexity of the problem of finding the value of efficiency which further simplifies the solution of problems of structural optimization of surveillance systems. A software and algorithmic implementation of the methodology for optimizing the functioning of an optoelectronic surveillance system is proposed. Using the developed software, a rational choice of modes of operation of certain types of surveillance equipment at certain time intervals was carried out taking into account the time of day and weather conditions. Conclusions. The use of the proposed methodology makes it possible to optimize the modes of operation of the optoelectronic surveillance system, taking into account the limiting factors in terms of efficiency and power consumption when using the same types of surveillance equipment on all towers of the system. A possible direction for improving the methodology is its adaptation to cases where different types of surveillance equipment are used on different towers of the system.

Algorithms of training programs for simulator training of ship's engine room personnel

The ship's engine room personnel (mechanics, electromechanics) ensure the technical operation and safe functioning of the ship's power systems. Professional training and retraining of marine specialists of ship's engine rooms is necessary due to the constant improvement of equipment and automation of electric power systems of modern ships. To ensure high-quality training of the ship's engine room team, simulator training is used with a set of modern simulators of ship power plants and electric power systems that meet the standards of the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers. In the course of the study, block diagrams of training algorithms have been developed; program features and examples of their operation have been shown. The obtained algorithms for training on simulators make it possible to implement both the initial training of cadets and the retraining of specialists when improving their qualifications. In the course of simulator practice, the opportunity to develop competencies for working with high-voltage equipment (3–10 kV) of modern ships in accordance with the requirements of the STCW Convention and the STCW Code is of particular importance. Algorithms for training in high-voltage equipment maintenance should be introduced into the educational process to improve the level of qualification of specialists.

MATHEMATICAL MODELING OF OPERATIONAL RELIABILITY OF GROUND-BASED NAVIGATION SYSTEMS

The theoretical justification for improving the effectiveness of vibration isolation for navigation equipment by reducing the natural frequency of the elastic suspension through the use of an additional electromagnetic loading system on the moving part is presented. An overview of the main elements of primary sensors and vibration protection systems for reproducing vibrations is provided. It is emphasized that vibration isolation of navigation equipment in the low-frequency disturbance range, typical for terrestrial carriers, is a crucial component in preventing emergency situations on airborne and marine vessels. A detailed calculation scheme for a vibration test stand using vertically installed flat springs is presented. Relationships between the natural frequency of the suspension and the system parameters are determined. One method for reducing the natural frequency of an elastic suspension on flat springs is analyzed. The calculation scheme for the suspension with vertically installed elastic springs is outlined. The stability conditions of the linear system, which depend on the amplitude of oscillations, are analytically investigated. It is emphasized that there is no translational movement of the suspension other than the chosen one. It is proposed to consider the calculation scheme of the suspension in the absence of dry friction. The structural component of the magnetic system is analyzed. The system of dispersive fluxes and their concentration in the air gap is outlined. An analysis of the compensatory nonlinearity of the stiffness characteristic of elastic elements is carried out. The reduction of distortions in the shape of the moving oscillations of the moving platform in the horizontal plane is analyzed. Stiffness characteristics of the suspension with a magnet for several actual values of kkk and δ\deltaδ are provided. Experimental calculations demonstrate that this vibration isolation system for navigation equipment leads to a significant reduction in the natural frequency of the suspension, ensuring its operational reliability and maximum prevention of low-frequency horizontal vibrations.

Bit efficiency of distributed- and collocated-massive MIMO base station systems in OTA measurement and simulation

Abstract This paper presents the bit efficiency of 28 GHz digital beamforming in over-the-air (OTA) measurements and simulations for distributed massive multiple-input–multiple-output (D-MIMO) and collocated massive multiple-input–multiple-output (C-MIMO) systems, as well as simulations for a 3.75 GHz small-cell scenario. Under the condition that users are randomly located in the line of sight coverage indoor area and spatially selected from each other by the normalized zero-forcing method, the OTA measured D-MIMO system exhibits an average of 4–7 dB better signal-to-noise ratio compared to C-MIMO when the number of simultaneously connected users “K” approaches the number of transceivers “M.” This means that the D-MIMO system provides higher bit efficiency than the C-MIMO system when K/M is large. Furthermore, the D-MIMO 3.75 GHz simulation predicts a relatively approximate 30% higher maximum efficiency than C-MIMO due to the shorter average distances between user equipment and access points in the D-MIMO system. To the best of the author’s knowledge, an earlier version of this paper has been presented at the 53rd European Microwave Conference as a first report on the 28 GHz OTA measured bit efficiency between C-MIMO and D-MIMO, highlighting D-MIMO’s advantage.

Research on the abnormal identification method of remote real-time monitoring of power system equipment based on artificial intelligence

ABSTRACT This study addresses the critical challenge of anomaly detection in modern power systems, proposing a novel hybrid approach that combines supervised and unsupervised machine learning techniques. The increasing complexity of power grids and integrating renewable energy sources and smart technologies necessitate more sophisticated methods for identifying and mitigating potential threats to system stability and security. Our model leverages deep learning architectures, including Long Short-Term Memory networks and Convolutional Neural Networks, to process high-dimensional time-series data from various grid sensors. The system demonstrates exceptional performance in detecting diverse anomalies, including cyber-attacks, equipment failures, and environmental disturbances, with an overall accuracy of 97.8% and an F1-score of 0.956. Real-time processing capabilities enable anomaly detection within 1.2 seconds, crucial for preventing cascading failures. A novel interpretability framework provides insights into the model’s decision-making process, enhancing trust and actionable intelligence for operators. Extensive case studies and robustness analyses validate the system’s effectiveness across various operational scenarios. This research significantly enhances power grid resilience and reliability, offering a robust foundation for future advancements in smart grid technologies and adaptive anomaly detection systems.

Digital Twin - An Innovative Strategy in Healthcare Transformation: An Extensive Review

In an age where the physical and digital worlds progressively intersect, the concept of the digital twin has aroused as a transformative force across various industries. Digital twins are dynamic digital imitations of physical objects; systems are procedures that can be used to simulate, analyse, and optimize their real-world analogue. In the health care field, a lot of work has gone into establishing digital twin of patient and medical devices. The digital twin of the patient is created by digitising the patient’s physical traits and bodily alterations. Real-world utilization of this technology includes accurate maintenance, advanced operational efficiency, and support for well-informed decision-making, all of which are trans-formative. The digital twin revolution is changing how healthcare professionals approach patient care, treatment planning, and facility administration. Digital twins provide instantaneous monitoring, personalized therapy, and predictive analytics by generating dynamic virtual replicas of patients, medical equipment, and healthcare systems. By offering insights on energy use, material consumption, and other vital variables, digital twin facilitates improved resource management and boosts businesses by cutting costs and waste. Digital twins are positioned to play a vital role in modern healthcare, inciting innovation and efficiency throughout the sector as technology advances. We focused on applications and development of digital twin in healthcare sector by analyzing a large number of studies from distinct medical sector, the effectiveness of digital twin in imaging studies and diagnosis, cancer, cardiology, neurology has been discussed in this review.

Exploratory Pilot Study Engages Community Health Workers to Test Drone-Based Package Delivery System for Personal Protective Equipment in High-Risk Appalachia Population.

During the COVID-19 pandemic, high-risk clients' and caregivers' access to essential personal protective equipment (PPE) was limited especially in many remote areas of Appalachia. A multidisciplinary team of community and university partners explored how to coordinate the use of community health workers (CHWs) and drone technologies to increase access to PPE in rural and remote Appalachian regions. CHWs recruited 10 Homeplace clients in an exploratory study of drone-based package delivery of PPE to assess importance and effectiveness of PPE self-efficacy related to PPE use, use of PPE, and ease and acceptability of drone delivery (following delivery only). CHWs educated each participant via in-person and Zoom meetings on the reasons for using PPE and proper use of PPE using Centers for Disease Control and Prevention guidelines. Most participants found the drone delivery of PPE easy and 80% were extremely satisfied with the drone delivery process. The frequency of mask-wearing increased from 60% at baseline to 90% at follow-up. On average, participants rated all types of PPE as effective in preventing the spread of disease. Drone officials used the findings of this pilot study to develop a waiver application to the U.S. Federal Aviation Administration to request permission to fly beyond visual line of sight in remote areas. Aerial drone technology could be a cutting-edge approach to health promotion in remote areas. The study results provide the proof of concept to assist investigators in designing future projects to promote healthy homes by collecting air and water samples and testing novel interventions deploying drone technology in remote Appalachian regions.

An Inherent Decoupled Triple-Active Bridge Converter for All-Electric Aircraft DC Power Systems

This paper focuses on a power conditioning system for an all-electric aircraft (AEA) powered by a single battery pack. The research project aims to identify a multi-port DC/DC converter topology that adequately supplies the two DC buses connected to the propulsion system and auxiliary equipment, respectively. To achieve this, a triple-active bridge (TAB) in its inherently decoupled configuration has been investigated, prototyped, and experimentally verified. The TAB voltage control system was designed, simulated, and experimentally validated. Specifically, start-up, steady-state and step-load performances were evaluated by the simulation study and then experimentally validated on a scaled prototype. The results assess the feasibility of using an inherently decoupled TAB as a power conditioning system for interconnecting the AEA battery pack with the electric propulsion and auxiliary systems. In particular, the developed TAB configuration secures the decoupled power transfer between the two output ports providing at the same time good dynamic performance in terms of voltage control during step-load variation.

Measurement of Atmospheric Coherence Length from a Shack–Hartmann Wavefront Sensor with Extended Sources

Free Space Optical Communication (FSOC) is a wireless communication method that utilizes laser beams for high speed and secure data transmission. Its performance is affected by various factors, among which atmospheric turbulence causes random fluctuations in the atmospheric refractive index, significantly impacting the reliability of communication links. The atmospheric coherence length is a key parameter describing the coherence properties of a laser signal as it propagates through the atmosphere, and accurately measuring it is crucial for assessing the quality of FSOC links. This paper proposes a novel strategy that utilizes extended sources directly as the information sources, combining the wavefront phase variance method with the extended source offset algorithm based on Shack–Hartmann wavefront sensors to directly measure atmospheric coherence length. Existing methods in extended scenarios typically rely on deploying laser beacons to aid in the calibration of atmospheric coherence length but setting up suitable beacons on horizontal communication links is challenging. Additionally, these approaches can be costly in terms of equipment and measurement expenses. Compared to traditional measurement methods, the algorithm proposed in this paper can measure directly based on extended scenarios in horizontal links, thereby effectively reducing system complexity and equipment costs. To verify the feasibility and effectiveness of this method, targeted simulations and experiments were conducted, and the results show that the coherence length measured by the algorithm is highly consistent with that measured by the Differential Image Motion Monitor (DIMM), with a deviation of less than 2% from actual values, effectively demonstrating the algorithm’s feasibility in coherence length assessment.

A System Dynamics Stability Model for Discrete Production Ramp-Up

Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research.

Knowledge Graph-Based In-Context Learning for Advanced Fault Diagnosis in Sensor Networks.

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors.