Current Monitoring Systems Research Articles

High speed data transmission method of substation intelligent auxiliary monitoring system

In order to better ensure the effective operation of substation intelligent auxiliary monitoring system, aiming at the problems of slow data transmission speed and poor data processing security of current monitoring system, a high-speed data transmission method of the substation intelligent auxiliary monitoring system is proposed. By constructing the data processing platform of substation intelligent auxiliary monitoring system, the data transmission algorithm of auxiliary monitoring system is optimized, reasonably select the optimal path of data transmission and conduct security encryption processing, so as to improve the high-speed data transmission efficiency of substation intelligent auxiliary monitoring system. Finally, the experiment shows that the high-speed data transmission method of the substation intelligent auxiliary monitoring system has higher practicability in the process of practical application and fully meets the research requirements.

Monitoring Large-Scale Hydraulic Engineering Using Sentinel-1 InSAR: A Case Study of China's South-to-North Water Diversion Middle Route Project

The Middle Route of South-to-North Water Diversion (SNWD) Project,known as one of the greatest hydraulic projects of China, undertakes most of domestic and industrial water use in the Beijing&#x2013;Tianjin area, as well as a part of ecological water use. The whole canal should be monitored to ensure a continuous water supply for water-receiving areas. Compared with traditional ground-based monitoring tools limited by the low distribution density, intensive labor force, and huge cost, the satellite synthetic aperture radar interferometry (InSAR) with wide coverage, high-frequency revisiting, and low cost is more suitable for monitoring large-scale infrastructures. In this study, we employed the Sentinel-1 data to monitor the canal of the Middle Route of the SNWD Project within Henan Province. The deformation rates along the canal were obtained by using persistent scatterer InSAR, from which a total of 20 deformed canal sections and seven suspected deformed canal sections were identified. Among them, the Shahe aqueduct is overall stable except for the Shahe Beam Aqueduct and Lushanpo Landing Aqueduct. The Yuzhou-Changge canal passes through a subsidence funnel with a maximum deformation rate of about &#x2013;20 mm&#x002F;year. The InSAR-derived deformation coincides well with the <i>in-situ</i> leveling measurements over the Yuzhou-Changge canal. Their correlation is 0.95 and the root mean square error of their differences is 2.41 mm&#x002F;year. The results demonstrate the effectiveness of satellite InSAR for monitoring large-scale hydraulic engineering. It can be combined with ground leveling to achieve overall investigations and detailed monitoring, largely improving the efficiency and cost of the current ground monitoring system.

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system&#x2019;s reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

УДОСКОНАЛЕННЯ ДІЮЧОЇ СИСТЕМИ СПОСТЕРЕЖЕНЬ ЗА ЯКІСТЮ АТМОСФЕРНОГО ПОВІТРЯ В М. КИЄВІ У ВІДПОВІДНОСТІ ДО ВИМОГ ЄС

The paper is dedicated to the problems of the current system for atmospheric air quality monitoring which is operated by the national hydrometeorological service of Ukraine in Kyiv. The problems are discussed in the context of implementing the Directives of the European Parliament and the Council on ambient air quality. The improvement of the current monitoring system needs immediate solutions due to the nonconformity to modern requirements not only in Kyiv, but also on the entire Ukrainian territory. The study makes an attempt to define the main steps of network improvement and the optimization of air pollution measurement network using air pollution data from the archives of the Central Geophysical Observatory, pollutants’ emission, land-use/land cover and meteorological data for 2000-2019. We discuss main EU requirements for monitoring sites’ location, atmospheric air pollution and emission data. The analysis showed the necessity for preliminary urban field measurements before the establishment of monitoring sites. Depending on the wind regime, the new measurement network must take into account the prevailing synoptic processes for future understanding how air pollution is transported towards and outwards the urban area. Land-use/ land cover data derived from the Landsat allowed distinguishing residential areas, industrial zones, main roads, water bodies and green areas which is crucial for the selection of suitable places for monitoring sites. Using mentioned principles and requirements, we propose 15 monitoring sites for the improved air quality network in Kyiv. This number of sites are greater than EU Directives claimed because conclusions were made on theoretical estimations without field measurements. The presented list of the monitoring sites needs additional analysis of spatio-temporal pollutants’ distribution in combination with atmospheric modelling. However, the analysis considers actual residential areas and emission sources in the background of the modern wind regime in Kyiv.

Microcontroller-Based Lead-Acid Battery Balancing System for Electric Vehicle Applications

In application of lead-acid batteries for electrical vehicle applications, 48 V of four 12 V batteries in a series configuration are required. However, the battery stack is repeatedly charged and discharged during operation. Hence, differences in charging and discharging speeds may result in a different state-of-charge of battery cells. Without proper protection, it may cause an excessive discharge that leads to premature degradation of the battery. Therefore, a lead-acid battery requires a battery management system to extend the battery lifetime. Following the LTC3305 balancing scheme, the battery balancing circuit with auxiliary storage can employ an imbalance detection algorithm for sequential battery. It happens by comparing the voltage of a battery on the stack and the auxiliary storage. In this paper, we have replaced the function of LTC3305 by a NUCLEO F767ZI microcontroller, so that the balancing process, the battery voltage, the drawn current to or from the auxiliary battery, and the surrounding temperature can be fully monitored. The prototype of a microcontroller-based lead-acid battery balancing system for electrical vehicle application has been fabricated successfully in this work. The batteries voltage monitoring, the auxiliary battery drawn current monitoring, the overcurrent and overheat protection system of this device has also successfully built. Based on the experimental results, the largest voltage imbalance is between battery 1 and battery 2 with a voltage imbalance of 180 mV. This value is still higher than the target of voltage imbalance that must be lower than 12.5 mV. The balancing process for the timer mode operation is faster 1.5 times compared to the continuous mode operation. However, there were no overcurrent or overtemperature occurred during the balancing process for both timer mode and continuous mode operation. Furthermore, refinement of this device prototype is required in the future to improve the performance significantly.

Progress report on addressing meteotsunami risk in the eastern Yellow Sea

On 31 March 2007, strong, tsunami-like waves of 1.0–2.5 m were recorded at most tide gauges along the west coast of Korea. The following year, on May 4, unexpected, abnormal waves in the eastern Yellow Sea reached a maximum height of ∼1.3 m. Both events occurred without warning, resulting in severe loss of life and property. Subsequent analysis found that these tsunami-like waves were meteotsunamis generated by air pressure oscillations. Evidence of possible meteotsunamis has been recorded by existing observation systems. However, the lack of understanding of the phenomenon and meteotsunami-specific monitoring system has hindered community preparedness, resulting in severe damage. We utilized existing observation systems (meteorological stations, tide gauges, and radar) during 2018 to develop a real-time meteotsunami monitoring system in the eastern Yellow Sea. This system detects the intensity and propagation of air pressure oscillations to identify potential coastal hazards and prevent damage caused by meteotsunamis. Two air pressure disturbance methods for measuring intensity of air pressure oscillation (a range of pressure changes over a 60 min window vs the rate of pressure change over a 10 min window) were compared, and several test operations were performed during development of the proposed system. The progress and limitations of the current observation and monitoring system were confirmed based on recent monitoring reports of air pressure jumps during the meteotsunamis on 7 April 2019. To address the insufficient lead time of meteotsunami warnings, installation and testing of open-ocean buoys outfitted with pressure sensors commenced in 2019.

Global to local impacts on atmospheric CO2 from the COVID-19 lockdown, biosphere and weather variabilities

The worldwide lockdown in response to the COVID-19 pandemic in year 2020 led to an economic slowdown and a large reduction in fossil fuel CO2 emissions (Le Quéré 2020 Nat. Clim. Change 10 647–53, Liu 2020 Nat. Commun. 11); however, it is unclear how much it would slow the increasing trend of atmospheric CO2 concentration, the main driver of climate change, and whether this impact can be observed considering the large biosphere and weather variabilities. We used a state-of-the-art atmospheric transport model to simulate CO2, and the model was driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.21 ppm decrease in the atmospheric column CO2 anomaly in the Northern Hemisphere latitude band 0–45° N in March 2020, and an average of 0.14 ppm for the period of February–April 2020, which is the largest decrease in the last 10 years. A similar decrease was observed by the carbon observing satellite GOSAT (Yokota et al 2009 Sola 5 160–3). Using model sensitivity experiments, we further found that the COVID and weather variability are the major contributors to this CO2 drawdown, and the biosphere showed a small positive anomaly. Measurements at marine boundary layer stations, such as Hawaii, exhibit 1–2 ppm anomalies, mostly due to weather and the biosphere. At the city scale, the on-road CO2 enhancement measured in Beijing shows a reduction by 20–30 ppm, which is consistent with the drastically reduced traffic during the COVID lockdown. A stepwise drop of 20 ppm during the city-wide lockdown was observed in the city of Chengdu. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signals at different policy relevant scales (country and city) against the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment. Its impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy, as well as expanded spatiotemporal coverage of our monitoring systems.

A novel reconstruction method of temperature field for thermomechanical stress analysis of arch dams

Temperature distribution on the dam surface plays an important role in reliable thermomechanical stress analysis of arch dams. In this paper, a novel reconstruction method based on a convolutional neural network is proposed for the temperature field on the downstream surface of arch dams. The environmental variables, including solar radiation, mountain shadows, ambient temperature, and weather conditions, are considered in the reconstruction method. The temperature field of the Xiaowan dam is predicted using the proposed method and it agrees well with the monitoring data in the spatial and temporal scale. The results show that the environmental variables significantly affect the temperature on the downstream surface, and result in temporal-spatial variability of nonuniform temperature distribution. In addition, the proposed method solves the data sparseness and low-frequency acquisition problems that may miss the peak temperature values in the current monitoring system. The thermomechanical stress analysis based on the reconstructed method and finite element method is conducted, and its results illustrate that the proposed reconstructed method can give more realistic performance of the dam and predict possible thermal cracking on the dam surface.

Validity of a novel respiratory rate monitor comprising stretchable strain sensors during a 6-min walking test in patients with chronic pulmonary obstructive disease

BackgroundBreathing frequency is rarely measured during a field walking test since the current monitoring system using a face mask is cumbersome for older adults. For effective clinical application, we aimed to validate the new respiratory monitor using wearable strain sensors during a 6-min walk test (6MWT) in young adults and patients with chronic obstructive pulmonary disease (COPD). MethodsThe study included young adults and patients with stable COPD voluntarily recruited from three hospitals. Breathing frequency during 6MWT were measured by the strain sensor and a nasal capnometer. Total breathing frequencies were measured by the capnometer. The Bland–Altman method was used to estimate the mean limit of agreement for breathing frequency. ResultsA total of 23 young adults (age = 23.1 ± 3.7, mean ± SD) and 50 patients with COPD (age = 75.2 ± 7.2, %FEV1 = 59.1 ± 19.7) were analyzed. During the entire test period, the total breathing frequencies were measured based on an average of 252 ± 46 breaths, and the total breathing frequency was higher in patients with COPD than in young adults (mean difference = −3.349, p < 0.0013). The mean difference in breathing frequency between the strain sensors and capnometer was −0.28 (95%CI: 0.75 to 0.20), and the limit of agreement ranged from −4.1 to 3.6. The CI of the limit of agreement included the limit of equivalence (4 counts/min). ConclusionsThe novel respiratory monitor with wearable sensors achieved the target accuracy in both young adults and patients with COPD in the 6MWT.

Research on the Architecture of Integrated Platform of Intelligent Substation Auxiliary Monitoring System

For the problems existing in the current substation auxiliary monitoring system, such as various types of equipment, inconsistent standards and poor interaction, the structure design scheme of integrated platform of intelligent substation auxiliary monitoring system is proposed. The structure, function and algorithm of the system are introduced in detail. Firewall isolation technology and security partition technology are adopted to ensure the security and of the system, and give consideration to the real-time data transmission. Through the use of software bus technology, multi data fusion technology and interface standardization technology, the availability and reliability of data are effectively improved and the reliability of the system is improved. Combined with the integrated monitoring platform, the characteristics of intelligent linkage technology of main and auxiliary equipment are put forward. The intelligent linkage and unified management between auxiliary equipment is realized, which effectively improves the intelligent operation and maintenance level of substation auxiliary system.

Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China

BackgroundAedes albopictus is a vector of major arboviral diseases and a primary pest in tropical and temperate regions of China. In most cities of China, the current monitoring system for the spread of Ae. albopictus is based on the subdistrict scale and does not consider spatial distribution for analysis of species density. Thus, the system is not sufficiently accurate for epidemic investigations, especially in large cities.MethodsThis study used an improved surveillance program, with the mosquito oviposition trap (MOT) method, integrating the actual monitoring locations to investigate the temporal and spatial distribution of Ae. albopictus abundance in an urban area of Shanghai, China from 2018 to 2019. A total of 133 monitoring units were selected for surveillance of Ae. albopictus density in the study area, which was composed of 14 subdistricts. The vector abundance and spatial structure of Ae. albopictus were predicted using a binomial areal kriging model based on eight MOTs in each unit. Results were compared to the light trap (LT) method of the traditional monitoring scheme.ResultsA total of 8,192 MOTs were placed in the study area in 2018, and 7917 (96.6%) were retrieved, with a positive rate of 6.45%. In 2019, 22,715 (97.0%) of 23,408 MOTs were recovered, with a positive rate of 5.44%. Using the LT method, 273 (93.5%) and 312 (94.5%) adult female Ae. albopictus were gathered in 2018 and 2019, respectively. The Ae. albopictus populations increased slowly from May, reached a peak in July, and declined gradually from September. The MOT positivity index (MPI) showed significant positive spatial autocorrelation across the study area, whereas LT collections indicated a nonsignificant spatial autocorrelation. The MPI was suitable for spatial interpolation using the binomial areal kriging model and showed different hot spots in different years.ConclusionsThe improved surveillance system integrated with a geographical information system (GIS) can improve our understanding of the spatial and temporal distribution of Ae. albopictus in urban areas and provide a practical method for decision-makers to implement vector control and mosquito management.Graphical abstract

Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

This paper presents an advanced model for monitoring losses on a 400 kV over-head transmission line (OHL) that can be used for measured data verification and loss assessment. Technical losses are unavoidable physical effects of energy transmission and can be reduced to acceptable levels, with a major share of technical losses on transmission lines being Joule losses. However, at 400 kV voltage levels, the influence of the electrical corona discharge effect and current leakage can have significant impact on power loss. This is especially visible in poor weather conditions, such as the appearance of fog, rain and snow. Therefore, loss monitoring is incorporated into exiting business process to provide transmission system operators (TSO) with the measure of losses and the accurate characterization of measured data. This paper presents an advanced model for loss characterization and assessment that uses phasor measurement unit (PMU) measurements and combines them with end-customer measurements. PMU measurements from the algorithm of differential protection are used to detect differential currents and angles, and this paper proposes further usage of these data for determining the corona losses. The collected data are further processed and used to calculate the amount of corona losses and provide accurate loss assessment and estimation. In each step of the model, cross verification of the measured and calculated data is performed in order to finally provide more accurate loss assessment which is incorporated into the current data acquisition and monitoring systems.

Research on Remote Monitoring and Fault Diagnosis System for Coal Mine Electromechanical Equipment

This article proposes an architecture of equipment remote monitoring and fault diagnosis system in view of the increasingly complex business logic and functional requirements of the current coal mine electromechanical equipment remote monitoring and fault diagnosis system. We established a loosely coupled Web service system by abstracting and dividing the business logic and functional requirements of the remote monitoring and fault diagnosis system at different levels and different granularities. At the same time, we use the fault diagnosis model of multi-scale residual network and attention mechanism algorithm to verify the effectiveness of the fault diagnosis system.

Novelty Detection on Power Quality Disturbances Monitoring

Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.

Microbial source tracking of fecal contamination in Laguna Lake, Philippines using the library-dependent method, rep-PCR.

Laguna Lake is an economically important resource in the Philippines, with reports of declining water quality due to fecal pollution. Currently, monitoring methods rely on counting fecal indicator bacteria, which does not supply information on potential sources of contamination. In this study, we predicted sources of Escherichia coli in lake stations and tributaries by establishing a fecal source library composed of rep-PCR DNA fingerprints of human, cattle, swine, poultry, and sewage samples (n = 1,408). We also evaluated three statistical methods for predicting fecal contamination sources in surface waters. Random forest (RF) outperformed k-nearest neighbors and discriminant analysis of principal components in terms of average rates of correct classification in two- (84.85%), three- (82.45%), and five-way (74.77%) categorical splits. Overall, RF exhibited the most balanced prediction, which is crucial for disproportionate libraries. Source tracking of environmental isolates (n = 332) revealed the dominance of sewage (47.59%) followed by human sources (29.22%), poultry (12.65%), swine (7.23%), and cattle (3.31%) using RF. This study demonstrates the promising utility of a library-dependent method in augmenting current monitoring systems for source attribution of fecal contamination in Laguna Lake. This is also the first known report of microbial source tracking using rep-PCR conducted in surface waters of the Laguna Lake watershed.

A review of previous studies on dam leakage based on distributed optical fiber thermal monitoring technology

PurposeSeepage of the dam is an important safety problem, which may cause internal erosion of the structure. In the field of seepage monitoring in civil engineering, the distributed optical fiber sensing technology based on the temperature tracing method has been paid more attention due to its unique advantages of high sensitivity, good stability and high resolution. The purpose of this paper is to make a review of the existing related research, so as to facilitate the later scholars to understand and further study more systematically.Design/methodology/approachIn this paper, three kinds of commonly used distributed fiber temperature measurement technologies are introduced. Based on the working principle, monitoring system, theoretical analysis, experimental research and engineering application of the fiber seepage monitoring technology, the present situation of dam seepage monitoring based on distributed fiber is reviewed in detail and their advantages and disadvantages are compared.FindingsThe thermal monitoring technology of seepage measurement depends on the accuracy of optical fiber temperature measurement (including the accuracy of the system and the rationality of the discrimination method), the correct installation of optical fiber and the quantitative analysis of temperature data. The accuracy of the current monitoring system can basically meet the existing measurement requirements, but the correct installation of optical fiber and the calibration of temperature data need to be further studied for different discrimination methods, and this field has great research value.Originality/valueAt present, there are many applications and research studies of optical fiber sensing in the field of structural health monitoring, and there are also reviews of related aspects. However, there is little or no review only in the field of seepage monitoring. This paper summarizes the research and application of optical fiber sensing in the field of seepage monitoring. The possibility of the gradient method to find its new prospect with the development of monitoring systems and the improvement of temperature resolution is discussed. The idea of extending the seepage monitoring method based on distributed optical fiber thermal monitoring technology to other monitoring fields is also given in the paper.

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.

Research on Monitoring Method of Fuel Sulfur Content of Ships in Tianjin Port

Fuel sulfur content of marine sailing within the emission control area should not exceed 0.5%. Traditional boarding inspections are difficult, inefficient, and poorly targeted. Remote monitoring of air pollutants from ships is required to screen unqualified ships. The article investigates the topographical conditions and weather conditions of Tianjin Port, selects the technology of existing monitoring methods, elaborates and analyzes the composition and working principle of the current monitoring system, and finally forms a monitoring method of fuel sulfur content of ships in Tianjin Port.