Search

The current distribution network is developing toward the direction of information and intelligent distribution Internet of Things (IoT). In order to explore the online monitoring system for transient electromechanical simulation of distribution networks, this paper is based on the equivalent model of generator sets. Firstly, it describes the relevant theoretical knowledge of edge computing, designs the distribution IoT based on edge computing, and briefly introduces its network architecture. Secondly, based on the discrete-time domain equivalent model of generators, the electromechanical transient simulation distribution network is constructed by introducing the machine network division of the power network. Finally, the Western System Coordinating Council (WSCC)-3 units and 9 nodes are taken as an example, and simulation experiments are conducted under two fault simulation conditions to verify the effectiveness of the simulation model. The results show that under the two fault simulation conditions, the results of equivalent model simulation of generator terminal voltage and relative power angle change are like those of the Power System Analysis Software Package (PSASP). The changing trend of the two is similar and stable. After the PSASP results are stabilized at a value, the simulation results fluctuate extremely up and down the value. For example, under fault condition 1, the changing trend of the relative power angle of the No. 2 generator is first fluctuating and then becomes stable. After violent fluctuation, the relative power angle tends to be stable from about 20s to -12.35°. The result of PSASP software is stable at -12.35°. The transient electromechanical simulation of the generator equivalent model can provide some ideas for the online monitoring system of the distribution network. [ABSTRACT FROM AUTHOR]

Pasta enriched with soy flour can be considered as a functional food, due to its content in nutraceutical compounds such as isoflavones, carotenoids, and other antioxidants. The quantification of the amount of a functional ingredient is an important step in food authenticity. The availability of non-destructive techniques for quantitative and qualitative analyses of food is therefore desirable. This research aimed to investigate the feasibility of hyperspectral imaging in reflectance mode for the evaluation of the soy flour content, also to investigate the possibility of implementing a feed-back control system to precisely dose the soy flour during the industrial production of pasta. Samples of pasta in shape of spaghetti were produced with durum wheat semolina and soy flour at increasing percentages (0, to 50%, steps of 5%). A feature selection algorithm was used to predict the amount of soy flour. The most influent wavelengths were selected, and a six-term Gauss function was trained, validated, and tested. The identified transfer function was able to predict the percentage of soy flour with high accuracy, with an R2adj value of 0.98 and a Root Mean Square Error of 1.31. The developed system could represent a feasible tool to control the process in a continuous mode. [ABSTRACT FROM AUTHOR]

Water is an important resource for performing ablution before prayer. However, lack of communication and information between prayer congregations regarding the amount of water used during ablution, the absence of environmental protection consciousness, and the impact of cultural behavior can be the factors causing wasteful water use during ablution. This study aims to design and implement an internet-based monitoring system that integrates cultural values in evaluating ablution water consumption. The correlation between cultural dimensions and preferences for an online monitoring system of ablution practices was examined using confirmatory factor analysis and structural equation modeling. The results showed that uncertainty avoidance, power distance, and long-term orientation were identified to significantly influence online monitoring preferences for ablution activities. The preferences of sensor accuracy, safety case to cover from water, and durable material were determined as the most important design specifications for online monitoring systems. The real-time evaluation of online monitoring was performed through smartphone applications and web-based interfaces. Considering cultural preferences when developing a water monitoring system strategy can increase awareness regarding environmental sustainability, advocate for conscientious water management, and control water usage. The inclusion of cultural value in the design process is expected to provide valuable insights for product designers when establishing design specifications. Additionally, it expands the usefulness of Hofstede's cultural theory in the context of the creation and development of products. [ABSTRACT FROM AUTHOR]

Water is an important resource for performing ablution before prayer. However, lack of communication and information between prayer congregations regarding the amount of water used during ablution, the absence of environmental protection consciousness, and the impact of cultural behavior can be the factors causing wasteful water use during ablution. This study aims to design and implement an internet-based monitoring system that integrates cultural values in evaluating ablution water consumption. The correlation between cultural dimensions and preferences for an online monitoring system of ablution practices was examined using confirmatory factor analysis and structural equation modeling. The results showed that uncertainty avoidance, power distance, and long-term orientation were identified to significantly influence online monitoring preferences for ablution activities. The preferences of sensor accuracy, safety case to cover from water, and durable material were determined as the most important design specifications for online monitoring systems. The real-time evaluation of online monitoring was performed through smartphone applications and web-based interfaces. Considering cultural preferences when developing a water monitoring system strategy can increase awareness regarding environmental sustainability, advocate for conscientious water management, and control water usage. The inclusion of cultural value in the design process is expected to provide valuable insights for product designers when establishing design specifications. Additionally, it expands the usefulness of Hofstede's cultural theory in the context of the creation and development of products. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a portable internet of things (IoT)-based system for online monitoring of soil nutrients, specifically nitrogen (N), phosphorus (P), and potassium (K), to improve precision and efficiency in smart farming. The main goal is to use IoT technology to analyze soil conditions on-site and provide advice about fertilization and soil management. The system measures soil nutrient levels using field-based sensors, such as an NPK probe, and transmits data over a wireless sensor network. The research comprises a quantitative evaluation of the performance of the IoT system using various sensors. An analysis of variance (ANOVA) was used to compare the accuracy of the IoT device with industrial soil nutrient measurement equipment, demonstrating differences in P and K values but not in N values. This disparity points to certain areas where the accuracy of the P and K measurements in the IoT system should be improved. This IoT-based soil nutrient monitoring system highlights the potential of smart farming technology to boost agricultural output, optimize resource consumption, and support sustainable farming practices. The system's portability and online data availability provide farmers with exact soil condition information, allowing them to make more efficient and intelligent farming decisions. [ABSTRACT FROM AUTHOR]

The medium and low voltage distribution network monitoring technology cannot effectively achieve the sharing and analysis of power grid operation information at all levels, and the monitoring visualization effect is poor. To solve this problem, this study uses the VISIO (Flowchart and vector graphics software) secondary development technology to design a C# based distribution network operation online monitoring system. The system has system basic configuration management, real-time data monitoring, data query, data analysis, early warning reminder management and other functions. While running, by establishing a connection, the distribution network operation data collected by the lower computer terminal can be obtained in real time and displayed on the monitoring main page. Users only need to control the relatively simple form controls to realize the basic information configuration and online monitoring of the system. The commissioning operation results show that the system can realize remote monitoring and timely warning reminders, so that the distribution network operation data can be used in real time, and the work efficiency can be effectively improved. [ABSTRACT FROM AUTHOR]

Tritium (3H) is one of the most critical nuclides for environmental monitoring, yet it is challenging to measure. Its high natural mobility and its potential to enter the human body through the food chain underscore the importance of not overlooking the radiation safety risks associated with tritium. The need for the online measurement of tritium at low concentrations is becoming increasingly apparent. This review examines the two principal stages of current measurement methodologies: sample preparation and radiation signal detection. It provides a summary of the tritium sample preparation and detection techniques, highlighting advances in the research with potential applications in online monitoring. The review concludes with an analysis of the issues inherent in the current techniques and offers perspectives on possible technological enhancements and future trajectories for the development of online monitoring systems for trace tritium levels. [ABSTRACT FROM AUTHOR]

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling. [ABSTRACT FROM AUTHOR]

Under the trend of carbon neutrality, the adoption of electric mineral transportation equipment is steadily increasing. Accurate monitoring of the operational status of electric check valves in diaphragm pumps is crucial for ensuring transportation safety. However, accurately identifying the operational characteristics of electric check valves under complex excitation and noisy environments remains challenging. This paper proposes a monitoring method for the status of electric check valves based on the integration of Adaptive Focal Loss (AFL) with residual networks and Extreme Learning Machines (AFL-ResNet-ELMs). Firstly, to address the issue of unclear feature representation in one-dimensional vibration signals, grayscale operations are employed to transform the one-dimensional data into grayscale images with more distinct features. Residual networks are then utilized to extract the state features of the check valve, with Extreme Learning Machines serving as the feature classifier. Secondly, to overcome the issue of imbalanced industrial data distribution, a new Adaptive Focal Loss function is designed. This function focuses the training process on difficult-to-classify data samples, balancing the recognition difficulty across different samples. Finally, experimental studies are conducted using industrially measured vibration data of the electric check valve. The results indicate that the proposed method achieves an average accuracy of 99.60% in identifying four health states of the check valve. This method provides a novel approach for the safety monitoring of slurry pipeline transportation processes. [ABSTRACT FROM AUTHOR]

The rise of wire arc additive manufacturing (WAAM) is attributed to the various field applications of additive manufacturing (AM), especially in producing large metal structures. The wire arc additive manufacturing is a process that can produce near-net-shape structures with high deposition rates and relatively lower costs. Due to the nature of the WAAM process, which is based on layer-by-layer fabrication and repeated heating and cooling cycles, surface defects can occur on the deposited layer, such as pores, cracks, oxidation, and geometric deviation. In understanding the factors that can affect the stability and mechanism of the WAAM process, online monitoring systems has been studied by researchers. This paper aims to critically review the different monitoring techniques as well as distinct sensing methods that can be applied in the WAAM process to enhance the quality of the 3D object. In conducting this review paper, the systematic literature review (SLR) method is chosen to enhance a better understanding of the current research works of monitoring systems in the WAAM process. The high heat accumulation and cooling rate process are found to be the most crucial factors that could influence the geometrical accuracy, microstructure, and mechanical properties of the deposited layer. [ABSTRACT FROM AUTHOR]

• Introduced an online system for monitoring ash deposition and corrosion rates. • Demonstrated rapid adaptability to condition changes in solid waste combustion. • Indicated enrichment of NaCl/KCl in submicron particles and inner deposit layer. • Useful for real-time operational adjustments and guiding maintenance decisions. Solid waste incineration is a clean and sustainable approach for solid waste management. However, ash deposition and corrosion remain a critical issue due to fuel's inherent enrichment of alkali chlorine. This study develops an integrated online deposition and corrosion monitoring system to enhance the operational safety and efficiency of solid waste incineration boilers. This system combines linear polarization resistance (LPR) for corrosion rate estimation with heat flux measurements for ash deposition analysis. It can offer a novel approach for real-time monitoring of heat exchangers' safety during solid waste combustion. It was deployed in a full-scale circulating fluidized bed (CFB) boiler that purely combust solid wastes. Key findings demonstrate the system's capability to deliver continuous, real-time data, crucial for the dynamic control of combustion processes and the maintenance of heat transfer surfaces. Its robust diagnostic capabilities were evident across various scenarios. Specially, initial corrosion rates sharply increase with deposition rates due to the enrichment of alkali chlorine on inner deposit layer, in which chlorine serves as a catalyst, facilitating the rapid penetration and aggravation of corrosion by other agents. As deposit further buildup, the corrosion rate steadily decreases along with surface temperature, highlighting a dynamic interaction. Moreover, measured corrosion rates can quickly response to temperature variations. Such multi-process online monitoring system provide more possibilities to investigate the inherent interaction between deposition and corrosion. Therefore, this work offers insights that could significantly influence operational strategies, maintenance protocols, and the overall reliability of waste-to-energy technologies. [ABSTRACT FROM AUTHOR]

The study of the formation of CO and CO2 is an important means of investigating the mechanism of low-temperature coal oxidation. However, the small changes in the gas produced by oxidation during the spontaneous combustion latency cannot be accurately measured by traditional flow reactors. A set of coal low-temperature closed oxidation multi-component gas online monitoring system was designed. Small changes in the volume fractions of O2, CO and CO2 can be automatically monitored in real time. Experiments on the isothermal oxidation of coal were conducted during the spontaneous combustion latency under different conditions (different temperatures, different initial oxygen volume fractions and different oxidation times). The oxidation experiment results were corrected by Henry model. According to the retardation time of gas production and the changes in Graham's ratio, CO and CO2 are produced from different reaction sequences during the spontaneous combustion latency. The active substances in coal react with O2 to produce unstable carbon-oxygen complexes, and CO2 is mainly derived from the decomposition of the intermediates. CO is derived from the H-capture reaction of stable solid oxygenated complexes. The research results will help to further understand the mechanism of spontaneous combustion of coal. [ABSTRACT FROM AUTHOR]

Automated fiber placement (AFP) in situ consolidation (ISC) of thermoplastic composite possess the potential to reduce manufacturing costs and improve manufacturing efficiency. The properties of composite manufactured by the ISC are affected by several mechanisms including polymer degradation, crystallization, intimate contact, polymer healing and void dynamics. All these mechanisms are directly affected by the temperature history. Consequently, the control and accurate measurement of temperature history during ISC are particularly important for improving the properties of composite. In this study, a simplified three-dimensional transient heat transfer model was established. The effect of tool temperature and placement speed on the temperature history and peak temperature were predicted. Simultaneously, an online temperature monitoring system was built and the optical Fiber Bragg Grating sensors (FBGS) was used to measure the temperature history. The results indicated that the predicted results of the model were consistent with the measured results, the error was below 8%. In addition, the temperature history of layers was significantly affected by the tool temperature and placement speed. The temperature of the layers decreased to near the tool temperature after cooling, and a higher tool temperature increasing its peak temperature because of the reduce of the cooling rate. On the contrary, an increase in placement speed will reduce the peak temperature of the layers. [ABSTRACT FROM AUTHOR]

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults' occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability. [ABSTRACT FROM AUTHOR]

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper's contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Clinical & Cardiovascular Perfusion / Türk Klinik ve Kardiyovasküler Perfüzyon Dergisi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Anaerobic digestion (AD) relies on the cooperation of specific microbial communities, making it susceptible to process disruptions that could impact biogas production. In this regard, this study presents a technological solution based on the Arduino platform, in the form of a simple online monitoring system that can track the produced biogas profile, named as biogas analyzer module (BAM). The applicability of the BAM focused on monitoring the biogas produced from sugarcane vinasse inoculated with sewage sludge biodigestion processed in mesophilic conditions (38 oC), in a pH range of 6.5–7.5, and following a three-stage operational model: (i) an adaptation (168 h), (ii) complete mixing (168 h), and (iii) bio-stimulation with glycerol (192 h). Then, the lab-made BAM was used to trace the produced biogas profile, which registered a total biogas volume of 8,719.86 cm3 and biomethane concentration of 95.79% (vol.), removing 90.8% (vol) of carbon dioxide (CO2) and 65.2% (vol) of hydrogen sulfide (H2S). In conclusion, the results ensured good accuracy and efficiency to the device created by comparisons with established standards (chromatographic and colorimetric methods), as well as the cost reduction. The developed device would likely be six times cheaper than what is available in the market. [ABSTRACT FROM AUTHOR]

Global interest in groundwater resource management is significant, with petrochemical industrialization identified as a potential source of groundwater pollution. Petrochemical industrial processes cause significant impacts on groundwater quality through the discharging of highly contaminated waste with oil and chemicals. This study aims to determine the impact of the heavy petrochemical industrial city of Jubail Saudi Arabia on groundwater quality and identify special sources of pollution using a statistical approach. Samples from 47 groundwater wells were collected and analyzed for 23 indicators, mainly chlorinated hydrocarbons, 1,1-olefin hydrocarbons, vinyl chlorides, 1,1,2-trichloroethylene and chloroform, 1,2-dichloroethylene, chlorobenzene, and benzene. The results were compared with permissible standard limits to identify the level of exceedance. The results showed an exceedance in the concentrations of chlorinated hydrocarbons, total petroleum hydrocarbons, 1,1-dichloroethane, 1,2-dichloroethane, 1,1-trichloroethane, and vinyl chloride more than ten times above the permissible limits of these concentrations. The study revealed the high impact of the petrochemical industries on groundwater quality. Since the current monitoring program depends only on annual sampling of groundwater, there has been a recommendation to install an online groundwater monitoring system to conserve water quality and achieve sustainable management. [ABSTRACT FROM AUTHOR]

Continuous hemodynamic monitoring in a wearable means can play a crucial role in managing hypertension and preventing catastrophic cardiovascular events. In this study, we have described the fully wearable tonometric device, referred to as flexible adaptive sensing tonometry (FAST), which is capable of continuous and accurate monitoring of hemodynamic parameters within the medical-grade precision. In particular, the FAST system integrates a 1 × 8 unit array of highly sensitive and highly flexible iontronic sensing (FITS) with 1 mm spatial resolution and a closed-loop motion system. The flexible tonometric architecture has been used to determine the radial arterial position with high sensitivity and high conformability, which simplifies the biaxial searching process of the traditional applanation tonometry into a highly efficient uniaxial applanation while keeping the medical-precision assessments. Importantly, a self-calibration algorithm can be automatically implemented during the applanation process, from which the intra-arterial blood pressure wave can be continuously predicted within the medical-grade precision, and subsequently, multi-parametric hemodynamic analysis can be performed in real-time. Experimental validations on health volunteers have demonstrated that the FAST measurements are all within the required accuracy of the clinical standards for continuous pulse wave assessments, blood pressure monitoring as well as other key hemodynamic parameter evaluations. Therefore, the FAST system, by integrating the flexible iontronic sensing array, provides a real-time, medical-grade hemodynamic monitoring solution in a continuously wearable manner, from which remote patient-centered monitoring can be delivered with both medical precision and convenience. [ABSTRACT FROM AUTHOR]

In order to better detect and identify the running state of the wind turbine equipment and its generator in the power system wind farm, and then improve the operation reliability of the wind turbine in the power system wind farm, the monitoring method of power system state operation based on Supervisory Control and Data Acquisition (SCADA) model was studied. The SCADA model pre installs different types of sensors on the units of the power system to collect, record and store the power data of the power system. The empirical wavelet transform method is selected to extract the signal characteristics from the state operation signals collected by the SCADA model. ReliefF algorithm is used to select features related to equipment status monitoring from the extracted status operation signal features. Fuzzy C-means clustering algorithm is used to analyze the correlation of feature selection results and identify power system operating conditions. According to the condition identification results of power system, support vector regression prediction algorithm is selected to output the monitoring results of power system state operation. The experimental results show that the monitoring time of the generator and other equipment is less than 100 ms, which provides a strong guarantee for the reliable operation of the power system. This result not only validates the validity of the monitoring and data acquisition model proposed in this paper, but also provides a new idea and method for the intelligent operation and safety management of power system. [ABSTRACT FROM AUTHOR]

To address the issues of low visibility, poor real-time performance, and weak interaction capabilities in the operation of magnetic levitation circular knitting needles, an online monitoring method based on digital twin technology for magnetic levitation knitting needles is proposed. This method first conducts an in-depth analysis of the digital twin five-dimensional model of the magnetic levitation knitting needle to monitor the operational status of the equipment in real time. Additionally, an online monitoring system based on the Unity3D engine and a digital twin-based architecture for magnetic levitation knitting needles are designed. On this basis, the comprehensive monitoring, operation, and maintenance of the magnetic levitation knitting needles are achieved through the design of virtual models, visual interfaces, and real-time data acquisition and driving technology. Finally, this method takes a 5-inch 32-needle magnetic levitation circular knitting machine as the application research object. By analyzing the movement trajectory and real-time current feedback data of the three working positions of the magnetic levitation knitting needles, the rationality, effectiveness, and accuracy of the proposed method and model are demonstrated, achieving online monitoring of the magnetic levitation knitting needles and reflecting the full lifecycle process of the magnetic levitation knitting needles. [ABSTRACT FROM AUTHOR]

Monitoring of operating parameters of machines and devices is recently used more often in hard coal mining. This solution is one of the elements of the intelligent mine concept. This concept means that the enterprise uses innovative technologies while considering economic and social factors. The aim of applying practical solutions under the guiding idea is to increase the efficiency of the production line and the safety of the crew's work. The authors propose introducing a system monitoring the geometrical parameters of the powered roof support. The proposed system fits the requirements of creating an intelligent mine, a plant that meets the global safety and efficiency requirements. The article depicts studies of the underground working conditions of the section geometry monitoring system. The tests contributed to determining sensor installation locations and clarifying the geometric parameters of the measurement and recording system. The system is a solution so far not used in Polish hard coal mining at such a highly advanced level. Based on the measurements, the tested system determined the transverse and longitudinal inclinations of the essential elements of the section. In addition, through the designated angles, the working height of the section in the wall excavation was determined. [ABSTRACT FROM AUTHOR]

Plasma fluctuations, the uncertainty of laser ablation, and bremsstrahlung limit the identification of online element analysis during laser processing and cause difficulty in achieving concentration results with sufficient accuracy and repeatability. A laser‐induced breakdown spectroscopy (LIBS) online monitoring system with plasma spatial filtering and spectral screening is proposed to solve this problem. In this system, the high‐frequency ablation noise component of the plasma is eliminated using a specially designed optical Fourier filtering structure, and a spectral screening system based on plasma time waveform monitoring is used to suppress the influence of plasma fluctuations. Without noise filters or algorithm optimizations and based only on the basic internal standard method, the calibration curves of all nine elements in the alloy sample exhibits a Pearson's R2 value ranged from 0.91 to 0.99, with a mean of 0.94. The relative standard deviations are all in the range of 3.5%–8.4% with a mean of 5.4%. The accuracy and repeatability are comparable to those of typical LIBS systems. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Oil spills are a serious pollution problem in water ecosystems, causing significant damage to living things and the environment. The demand for devices to handle oil remediation is increasing due to the frequent occurrence of oil spills. To date, numerous works have been reported to address this pollution problem, but most of them do not utilize Internet of Things (IoT) technology. Having an IoT system of this kind would improve operational efficiency by providing real-time data, alerts, and notifications of oil pollution through monitoring systems. This study presents the development of an IoT-based automatic oil-water separator device to support an oil pollution monitoring system in water ecosystems. The built device consists of sensors, a microcontroller, and an actuator, with the capability to automatically detect oil pollution and perform water-oil separation. The device incorporates a monitoring system. The water-oil separation was carried out by applying semi-permeable membrane nanotechnology. Moreover, the device has undergone a series of oily-water separation experiments to simulate oil pollution, achieving an oil separation efficiency of 62.8% (on average) with good repeatability. Additionally, the automatic device is developed with IoT pairing with a website application for an online monitoring system. Users can monitor the process using a personal computer or smartphone via the web/mobile application at any time and from any place. It is of interest to explore this further since it offers an effective and implementable method to solve oil pollution problems in real-world applications. [ABSTRACT FROM AUTHOR]

In large-scale dairy farming, the use of high-temperature-fermented dairy manure bedding instead of rice husk-based bedding and other commercial types of bedding is widely favored. Strip-stacking aerobic fermentation is the main production method of dairy manure bedding, but it has problems including unstable fermentation and the secondary breeding of pathogens. In this work, a multi-probe, integrated, online monitoring system for temperature and relative humidity was used for fermentation process optimization. The effects of the temporal and spatial distribution of fermentation temperature and relative humidity on the nutrient content curve and the moisture and ash content of manure bedding materials were systematically studied. The effect of the fermentation process on the retention rate of effective bedding materials (cellulose, hemicellulose, and lignin) was analyzed. The experiments proved that high-quality bedding material can be obtained through reasonable stacking fermentation. The fabricated bedding material has a total dry base content consisting of cellulose, hemicellulose, and lignin of 78%, an ash content of 6%, and a nutrient content of 17%. The obtained bedding material was produced to increase the bed rest rate and continuously inhibit the bedding bacteria content, keeping it at a low level for 5 days. This study proves that temperature and humidity monitoring can guide the optimization of the strip-stacking fermentation process of dairy manure and that it can be applied to large-scale farms to improve fermentation parameters. [ABSTRACT FROM AUTHOR]

The electrification of society, increasing renewable energy sources and mobility charging lead to new loading patterns for power transformers. Dynamic load conditions induce enhanced mechanical stress on the transformers' windings, potentially causing degradation of the solid insulation over time and compromising the transformer's short-circuit withstand capability. Thermal expansion of the windings, caused by losses in the copper conductors, occurring as the transformer is loaded, increases the stress. Conversely, magnetic losses in the core and tie plate expansion contribute to a reduction in stress. This paper presents the effect of step changes in core losses and copper losses by on-line measurements of the clamping pressure, to better understand the mechanical stresses acting upon the solid insulation cellulose materials. Energisation is found to decrease the clamping pressure following warming up of the transformer, and loading the transformer increased the pressure as the windings increased in temperature. The converse effect was found when unloading and de-energising. The on-line monitoring system provides a new and important step towards ensuring the short-circuit performance of power transformers. [ABSTRACT FROM AUTHOR]

The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges. [ABSTRACT FROM AUTHOR]

The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R2) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears (~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring. [ABSTRACT FROM AUTHOR]

This study introduces an evaluation methodology tailored for bioreactors, with the aim of assessing the stress experienced by algae due to harmful contaminants released from antifouling (AF) paints. We present an online monitoring system equipped with an ultra-sensitive sensor that conducts non-invasive measurements of algal culture's optical density and physiological stage through chlorophyll fluorescence signals. By coupling the ultra-sensitive sensor with flash-induced chlorophyll fluorescence, we examined the dynamic fluorescence changes in the green microalga Chlamydomonas reinhardtii when exposed to biocides. Over a 24-h observation period, increasing concentrations of biocides led to a decrease in photosynthetic activity. Notably, a substantial reduction in the maximum quantum yield of primary photochemistry (FV/FM) was observed within the first hour of exposure. Subsequently, we detected a partial recovery in FV/FM; however, this recovery remained 50% lower than that of the controls. Integrating the advanced submersible sensor with fluorescence decay kinetics offered a comprehensive perspective on the dynamic alterations in algal cells under the exposure to biocides released from antifouling coatings. The analysis of fluorescence relaxation kinetics revealed a significant shortening of the fast and middle phases, along with an increase in the duration of the slow phase, for the coating with the highest levels of biocides. Combining automated culturing and measuring methods, this approach has demonstrated its effectiveness as an ultrasensitive and non-invasive tool for monitoring the physiology of photosynthetic cultures. This is particularly valuable in the context of studying microalgae and their early responses to various environmental conditions, as well as the potential to develop an AF system with minimal harm to the environment. [ABSTRACT FROM AUTHOR]

C-plane sapphire (α-Al 2 O 3) is a commonly used substrate material for integrated circuits and Light-emitting diodes (LEDs). Efficient, stable, and low-damage lapping processes are used to ensure surface smoothness and meet application requirements. Fixed abrasive pads (FAPs) are typically used for the lapping of sapphire substrates, but the pads are prone to be glazed or even failed. In this study, loose alumina grains were used for in-situ reconditioning of fixed diamond abrasive pads. The material removal rate and surface roughness of the lapped sapphire substrate were calculated, the types and size distributions of the wear debris were analyzed, the surface morphology of the FAP after lapping was examined, and an online monitoring system was used to track the friction coefficient between the FAP and the sapphire substrate. In order to determine the ideal size and concentration of loose grains and to reveal the in-situ reconditioning mechanisms for the fixed diamond abrasive pads, the effects of the size and concentration of loose alumina grains in the lapping slurry on the in-situ reconditioning of the FAP were investigated. This was done by examining the wear behavior of loose alumina grains during the lapping processes of sapphire substrates. • In-situ reconditioning mechanism for fixed abrasive pads based on the wear behavior of loose grains is discussed. • Loose alumina abrasives improve lapping efficiency and stability of fixed diamond abrasive pads. • Three-body wear of alumina grains on the pad enhances self-conditioning ability of fixed diamond abrasive pads. • Size of loose alumina grains should be close to the maximum protrusion height of the diamond abrasives. [ABSTRACT FROM AUTHOR]

The awareness of tailings dam safety monitoring has widened due to the recent disasters caused by failures of such structures. The failure rate of tailings dams worldwide (i.e., the percentage of failed dams out of total) is estimated at 1.2%, compared to the 0.01% rate for traditional water dams. Most of the tailings dam monitoring guidelines suggest that the owner develops a robust surveillance program to detect possible indicators of potential failures. This paper presents a thorough review of major guidelines on tailings storage facility (TSF) monitoring and surveillance, the visual parameters to be monitored, as well as good practice in the development of monitoring systems. This paper reviews the recent literature with an emphasis on the development of monitoring systems utilizing sensors, unmanned aerial vehicles (UAVs), and satellite images that may be considered as supplementary guarantees against failure events. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of technical functional analysis in the early development stages of an advanced Internet of Things (IoT) system designed for remote monitoring of patients' health parameters. The primary objective of this work is to enhance healthcare efficiency and accessibility, addressing the increasing demand for optimized medical services. Function identification is a pivotal aspect of this endeavor as it plays a crucial role in the successful development of the IoT system and often leads to the identification of competitive advantages. The methodology employed in this study involves identifying the basic need, together with the primary, secondary, and tertiary functions. These functions are then hierarchically organized, weighed, and assessed in both technical and economic dimensions. The cost–value relationship of the functions is analyzed using the least‐squares method and an optimal concept framework for an advanced IoT system tailored for remote health parameter monitoring of patients is proposed. [ABSTRACT FROM AUTHOR]

The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. [ABSTRACT FROM AUTHOR]

This article outlines the relevance of building online telemetry systems for online monitoring of the technical conditions of rolling mill equipment. Electromechanical systems of the horizontal stand of the plate Mill 5000 are described, when operating in harsh conditions caused by the shock loading when workpieces enter the stand. It is noted that dynamic torque overloads, exceeding the rated motor torque by many-fold, cause the fatigue failure of spindle joints and breakage of rolls. In this regard, the development and implementation of systems for monitoring the elastic torque on spindles are extremely urgent. This issue has long been studied, but the references provide no information on the building principles and hardware composition of such systems. The use of strain gauges connected according to a balanced bridge circuit to measure the elastic torque is justified. This paper's contribution is the proposed modular principle for building a telemetry monitoring system based on the analysis of known techniques for measuring and transmitting diagnostic data. The developed system structure is provided and the concept of data transfer and processing are explained. This article suggests the inductive power supply of a measuring unit mounted on a shaft without the use of batteries. A hardware structure was developed to be applied in a system for measuring, transmitting, and visualizing signals proportional to the elastic torque, manufactured on the basis of data measuring instruments by leading companies. The specifics of placement and connection of strain gauges are considered. The hardware providing a wireless power supply to the signal encoder and digital data transfer between the transmitter and receiver is described. The results of implementing the system on Mill 5000 are provided. The installation of a telemetry ring and a receiving head for the inductive power supply and data reception is shown. An experimental assessment of the elastic torques occurring when workpieces enter the cage was obtained by implementing a drive control algorithm which provided biting in the drive acceleration mode. The reliability of measuring the elastic torque with an error not exceeding ±5% and the reduction of dynamic loads on the spindle by 1.3–1.5 times due to the elimination of impacts from closing angular gaps in spindle joints was confirmed. This increases the service life of mechanical equipment and reduces the cost of eliminating the accident aftermath. The prospect of modifying the developed system into a cyber-physical system for monitoring the rolling mill's mechatronic equipment conditions is shown. [ABSTRACT FROM AUTHOR]

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments. [ABSTRACT FROM AUTHOR]

Water, energy, and food (WEF) security has become a key area of research in sustainable development. Prompted by the United Nations' Sustainable Development Goals, a large body of research highlights the inter-connectedness of WEF systems and the dangers of their siloed treatment. Simultaneously, more attention is being placed on the WEF security of remote populations in the Arctic, especially for areas like Alaska, where WEF insecurities are prevalent, yet obscured by national reporting. This research developed a WEF Nexus Security Index for the state of Alaska that can be used by policy-makers to consider plausible causes for regional inequalities and why some regions of Alaska, and not others are exposed to higher levels of WEF insecurity. Beyond the conceptual value of the index, an on-line Arctic WEF Nexus Security portal was developed that further enables policy-makers and community leaders to track changes in WEF security and monitor the effects stemming from future interventions, be they policy or technological in nature. By bridging nexus thinking and nexus doing, we hope to provide policy-makers with a novel and accessible tool for achieving WEF security in Alaska. [ABSTRACT FROM AUTHOR]

During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible–near–infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea. [ABSTRACT FROM AUTHOR]

With the increasing lifespan of bridges, various forms of deterioration will inevitably occur. The bridge health monitoring system enables direct monitoring of the loads (such as wind, temperature, and vehicles) experienced by the bridge during operation, as well as the corresponding vibration response of the bridge structure (including main beam deformation, tower top displacement, cable acceleration, etc.). These data provide valuable insights into the safety and durability of the bridge during its operational lifetime. However, the challenge lies in extracting meaningful information from the vast amount of collected data. This article focuses on the Fuyu Bridge as a research case and analyzes the variations in environmental factors, such as temperature, humidity, wind load, and structural vibration responses (main beam and cable), obtained from the Fuyu Bridge's structural health monitoring system. It also conducts a structural safety assessment during the operational period of the bridge. Furthermore, the research explores the relationship between environmental factors, load effects, and structural response to establish a foundation for future structural safety assessments of the Fuyu Bridge during its operational period. For instance, changes in environmental factors or load effects can be used to predict structural responses. Additionally, this study provides access to the structural health monitoring data of the Fuyu Bridge, facilitating data‐driven research for other scholars in the field. [ABSTRACT FROM AUTHOR]

The cumulative length of subways and tunnels in China has reached nearly 25 000 kilometers, bringing huge demand for underground construction. The monitoring for construction safety has become more significant, due to complicated environment and rapid-onset natural disasters, which raises the frequency of accidents in underground construction. The informatization engineering of monitoring and early-warning on natural disasters is built to achieve construction safety, through promoting integrated monitoring on multi-hazard and disaster chains, and practicing intelligent algorithms on comprehensive quick accurate perception and early-warning of construction hazards. The integrated network architecture for intelligent safety monitoring with edge-computing ability is established, realizing the visualization of monitoring data analysis through researches on AD hoc transmission based on directional antenna network bridge, development of IoT edge computing software and researches on accessing to IoT platform for multiple types of sensors, and development of the front-end and back-end of the monitoring system. The introduction of IoT and cloud computing sustains accessing to platform for huge amount of sensors and full exploiting of big-data mining, improving intelligent safety hazard identification, and reducing safety risks in construction. Based on the instant response of edge devices and interval data acquisition of sensors, and the capability for more than 100 000 sensors to access the system simultaneously, the complete monitoring system for underground constructions with second-level warning is established. [ABSTRACT FROM AUTHOR]

Over the past decade, significant research has focused on detecting abnormalities in metal laser powder bed fusion (L-PBF) additive manufacturing. Effective online monitoring systems are crucial for enhancing process stability, repeatability, and the quality of final components. Therefore, the development of in situ detection mechanisms has become essential for metal L-PBF systems, making efficient closed-loop control strategies to adjust process parameters in real time vital. This paper presents an overview of current in situ monitoring systems used in metal L-PBF, complemented by ex situ characterizations. It discusses in situ techniques employed in L-PBF and evaluates the applicability of commercial systems. The review covers optical, thermal, acoustic, and X-ray in situ methods, along with destructive and non-destructive ex situ methods like optical, Archimedes, and X-ray characterization techniques. Each technique is analyzed based on the sensor used for defect detection and the type or size of defects. Optical in situ monitoring primarily identifies large defects from powder bed abnormalities, while thermal methods detect defects as small as 100 µm and keyholes. Thermal in situ detection techniques are notable for their applicability to commercial devices and efficacy in detecting subsurface defects. Computed tomography scanning excels in locating porosity in 3D space with high accuracy. This study also explores the advantages of multi-sensor in situ techniques, such as combining optical and thermal sensors, and concludes by addressing current research needs and potential applications of multi-sensor systems. [ABSTRACT FROM AUTHOR]

With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being centralized and unified. In view of the problems related to large underground spaces in the process of constructing complex structures, with the introduction of Internet of Things technology and digital twins, we put forward an application of an intelligent safety-monitoring digital twin system in the construction of a large underground space structure, and at the same time, explore the Internet and digital integration mechanism of the twin system. The twin system uses BIM technology to establish the corresponding physical construction model, and collects multi-source heterogeneous monitoring data in real time through Internet of Things technology to achieve the exchange of information between the virtual construction model and the physical construction model. The twin system uses the multi-source heterogeneous data for real-time security analysis, and obtains the security status of the structure and feeds it back to the application service layer. The effectiveness and practicability of the twin system in large underground spaces are verified by an example project. Aiming at the safe performance of the orthogonal arch, the mapping relationship of various parameter indexes is obtained, and reasonable control measures are given. This study provides a new solution for improving the safety of construction projects and risk prevention and control, and has important theoretical and practical value for the safety management of underground space construction processes. [ABSTRACT FROM AUTHOR]

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction. [ABSTRACT FROM AUTHOR]

To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469. [ABSTRACT FROM AUTHOR]

Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system's ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry's demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 μm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process. [ABSTRACT FROM AUTHOR]

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality. [ABSTRACT FROM AUTHOR]

We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available. [ABSTRACT FROM AUTHOR]

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem. [ABSTRACT FROM AUTHOR]

As coalmines extend more deeply, strong mining-induced earthquakes occur more frequently; these seriously threaten the safety of underground miners and aboveground residents, as well as the productivity and effectiveness of mining activities. The distribution of mining-induced earthquake characteristics and hard roof breaking mechanisms are analyzed and revealed using the methods of field monitoring and theoretical analysis, and prevention and control technology were proposed and applied in a field application, for a typical dynamic disaster problem at Dongtan Coalmine. As indicated by the results, strong mining-induced earthquakes (E ≥ 105 J) were mostly related to the coordinated failure of several strata, and the migration and fracture of the overlying strata led to significant subsidence of the surface. As mining activities advance, the abutment stress within the coal seam in front of the coalface will dynamically change and be transferred more deeply into the coal seam under the influence of a mining-induced earthquake. Based on the mechanical behavior, the fracture structures of the overlying multiplicity of key strata are divided into main and subordinate "O-X" types. As the coal is exploited, the O-X scope gradually increases from the near field to the far field. As indicated by the field application, the implementation of deep-hole blasting could effectively weaken the integrity of the thick and hard rock strata and change the length of the overhang roof, so as to reduce the risk of strong mining-induced earthquakes, i.e., the incidence of strong mining-induced earthquakes above 5 × 105 J would be significantly reduced. Meanwhile, the frequency of rock strata collapse increases, and the average distance from the epicenter centrum to the coal seam is reduced to 47 m. The governing effect is good. We expect that the results of this study could provide references for dynamic disaster governance in similar mine areas. [ABSTRACT FROM AUTHOR]