Parameter Monitoring System Research Articles

Fatigue Analysis of Shovel Body Based on Tractor Subsoiling Operation Measured Data

This paper aims to investigate the effects of soil penetration resistance, tillage depth, and operating speeds on the deformation and fatigue of the subsoiling shovel based on the real-time measurement of tractor-operating conditions data. Various types of sensors, such as force, displacement, and angle, were integrated. The software and hardware architectures of the monitoring system were designed to develop a field operation condition parameter monitoring system, which can measure the tractor’s traction force of the lower tie-bar, the real-time speed, the latitude and longitude, tillage depth, and the strain of the subsoiling shovel and other condition parameters in real-time. The time domain extrapolation method was used to process the measured data to obtain the load spectrum. The linear damage accumulation theory was used to calculate the load damage of the subsoiling shovel. The magnitude of the damage value was used to characterize the severity of the operation. The signal acquisition test and typical parameter test were conducted for the monitoring system, and the test results showed that the reliability and accuracy of the monitoring system met the requirements. The subsoiling operation test of the system was carried out, which mainly included two kinds of soil penetration resistances (1750 kPa and 2750 kPa), three kinds of tillage depth (250 mm, 300 mm, and 350 mm), and three kinds of operation speed (4 km/h low speed, 6 km/h medium speed, and 8 km/h high speed), totaling 18 kinds of test conditions. Eventually, the effects of changes in working condition parameters of the subsoiling operation on the overall damage of subsoiling shovels and the differences in damage occurring between the front and rear rows of subsoiling shovels under the same test conditions were analyzed. The test results show that under the same soil penetration resistance, the overall damage sustained by the subsoiling shovels increases regardless of the increase in the tillage depth or operating speed. In particular, the increase in the tillage depth increased the severity of subsoiling shovel damage by 19.73%, which was higher than the 17.48% increase due to soil penetration resistance and the 13.07% increase due to the operating speed. It should be noted that the front subsoiling shovels consistently sustained more damage than the rear, and the difference was able to reach 16.86%. This paper may provide useful information for subsoiling operations, i.e., the operational efficiency and the damage level of subsoiling shovels should be considered.

Wireless sensor network-based machine learning framework for smart cities in intelligent waste management

Environmental safety is one of the key issues that are directly related to a country's prosperity. One of the most fundamental aspects of a sustainable economy is waste management and recycling. Better recycling safety and efficiency may be achieved via the use of intelligent devices rather than manual effort. In this research, we describe a machine learning-based architecture for smart trash collection and sorting using the Internet of Things and wireless sensor networks. The goal of this study was to develop an autonomous method for producing an efficient and intelligent waste parameter monitoring system for a novel waste management system, using the Internet of Things (IoT) and Long Range (LoRa) technologies. Several possibilities are explored, all of which may be applied to the development of the three nodes. The number of trash cans, garbage stench, air quality, weight, smoke levels, and waste categories are all tracked in real-time via the Internet of Things and the Thing Speak Cloud Platform, which can be set up in numerous places. In the end, a fog layer-deployed intelligent waste classification framework consists mostly of four layers: input, feature, classification, and output. Using the Thrash Box dataset, the proposed system develops a categorization method into trash classes such as household, medical, and electronic garbage, in addition to object identification. Traditional machine learning methods, such as the multi-kernel support vector machine (SVM) and the Adaboost ensemble classifier, are employed in the classification layer, while the Resnet-101 deep convolutional neural network model is used in the feature layer. Experiments were conducted to evaluate the suggested method's ability to classify garbage and provide accurate predictions about their respective categories. Compared to other state-of-the-art models, the suggested method's performance was shown to be superior in the presented trials.

Revitalizing IoT-based air quality monitoring system for major cities in Indonesia

An IoT-based air quality monitoring system is a technology that integrates with the internet to monitor and measure numerous air quality metrics in real-time. CO levels, dust particle levels, temperature, and humidity are the air quality characteristics that must be monitored. The air quality monitoring system in its current state requires further development, such as challenges to acquiring accurate and real-time data and difficulty in accessing reliable information. Poor air quality causes various health problems, respiratory, vision, heart disease, and even cancer. The development of air pollution producers continues to increase along with the number of oil-fueled vehicles, industries operated using petroleum-fueled engines, power plants that use energy from coal, gas and petroleum. This study presents an IoT-based air quality parameter monitoring system solution that is connected with the Blynk platform and can be accessible in real time, in an effort to assist the SDGs program, which is mandated by the global community through the UN. The research technique employed is Analysis, Design, Development, Implementation, and Evaluation. The study successfully presented an IoT-based air quality monitoring system connected with the Blynk platform, which showed great accuracy in measurement 94.34% (CO), 81.15% (dust), 99.14% (temperature), and 96.84% (humidity). These results advance urban air quality monitoring and inform sustainable technology development, contributing to environmental and health-related SDGs.

Progressive Monitoring of Micro-Dumps Using Remote Sensing: An Applicative Framework for Illegal Waste Management

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

Fault Diagnosis and Prediction System for Metal Wire Feeding Additive Manufacturing.

In the process of metal wire and additive manufacturing, due to changes in temperature, humidity, current, voltage, and other parameters, as well as the failure of machinery and equipment, a failure may occur in the manufacturing process that seriously affects the current situation of production efficiency and product quality. Based on the demand for monitoring of the key impact parameters of additive manufacturing, this paper develops a parameter monitoring and prediction system for the additive manufacturing feeding process to provide a basis for future fault diagnosis. The fault diagnosis and prediction system for metal wire supply and additive manufacturing utilizes STM 32 as its core, enabling the capture and transmission of temperature, humidity, current, and voltage data. The upper computer system, designed on the LabVIEW 2019 virtual instrument platform, incorporates an LSTM neural network model and facilitates a connection between LabVIEW and MATLAB 2019 to achieve the prediction function. The monitoring and prediction system established in this study is intended to provide basic research assistance in the field of fault diagnosis.

On the Applicability of Information Sources for the Monitoring System of Electrical Parameters of Electrical Installations of Buildings

On the Applicability of Information Sources for the Monitoring System of Electrical Parameters of Electrical Installations of Buildings

SMS based reservoir condition monitoring sensor system for pumped hydro storage in mini-grids

Solar mini-grids are a cost-effective and efficient method of improving energy access in sub-Saharan Africa, particularly in low-income communities. However, conventional battery storage systems have reduced the affordability of solar mini-grid-generated electricity, necessitating the development of alternative energy storage systems such as pumped hydro storage to replace lithium-ion batteries. This paper introduces a model for an SMS-based fluid parameter monitoring system to provide visibility of reservoir conditions during peak and off-peak periods. The model uses hardware such as voltage regulators, sensors, microcontrollers, and C- programming language software. Sensors monitor various parameters in the reservoir and penstock, transmitting signals to a microcontroller for analysis. The results are relayed to a display unit and personnel's mobile phones for alert notifications, ensuring precise and accurate reporting. Simulation results show the reliability of the design to monitor water levels, water temperature, flow rate, and transmission through SMS to mobile devices for timely reporting. The paper recommends the development and commercialization of this model for larger applications, such as monitoring dams for irrigation and power generation.

Early Dinner Improves the Glycemic Profile in Habitual Late Eaters With Uncontrolled Type 2 Diabetes Mellitus in the Short Term.

Background Late dinner (LD) can worsen the glucose profile in type 2 diabetes (T2D). We assessed the short-term effect of early dinner (ED) on glycemic control in habitual late eaters with uncontrolled T2D. Methodology This interventional, single-arm, within-group trial recruited 10 habitual late eaters with uncontrolled T2D (glycosylated hemoglobin: 7-9% and either fasting plasma glucose (FPG): ≥140 mg/dl or post-prandial plasma glucose: ≥200 mg/dl). They had their usual LD (beyond 22:00 hours) on Days 0-3 and ED (before 20:00 hours) on Days 4-10. Continuous glucose monitoring system (CGMS) parameters, two-hour post-dinner, and fasting (10-hour post-dinner) investigations were analyzed. Bedtime hunger was assessed using a Labeled Magnitude Satiety Scale. Results The mean dinner time was reduced from 22:28 hours to 19:29 hours. CGMS revealed that ED lowered the 10-hour post-dinner incremental area under the curve (22,587.9 ± 5,168.3 mg/dl×mins vs. 15,886.3 ± 4,288.7 mg/dl×mins, P < 0.002), 10-hour post-dinner average blood glucose (ABG) (137.5 ± 9.3 mg/dl vs. 125 ± 7.9 mg/dl, P < 0.002), 24-hour ABG (132.2 ± 7.5 mg/dl vs. 124.8 ± 5.4 mg/dl, P = 0.037), and night mean amplitude of glucose excursion (83.7 ± 5.8 mg/dl vs. 69.3 ± 7.5 mg/dl, P = 0.027). ED also reduced FPG (119.8 ± 7.3 mg/dl vs. 105.2 ± 5.7 mg/dl, P = 0.015), fasting insulin (15.0 ± 4.3 µIU/ml vs. 9.7 ± 2.7 µIU/ml, P < 0.002), and HOMA-IR (4.36 ± 1.2 vs. 2.56 ± 0.79, P < 0.002). Post-dinner glucose, insulin, and inflammatory markers were unchanged. Bedtime hunger increased significantly on Days 4 and 5 but returned to baseline by Day 10. Conclusions A simple modification of dinner time in habitual late eaters with uncontrolled T2D improves FPG, glycemic control, and insulin resistance in the short term.

Surrogate numerical prediction method of TBM position via FEM simulation and machine learning

The position of the tunnel boring machine (TBM) during tunnel construction is critical and must be precisely controlled. Owing to the geological uncertainty and complexity of the interaction between the ground and the TBM, controlling the position of the TBM is challenging. Hence, a surrogate numerical method is proposed to predict the position of the TBM using the finite element method (FEM) and machine learning method. First, a refined three-dimensional FEM model was established. Different values of the property parameters of the ground and the thrust force of the TBM were input into the FEM model, generating a database that includes 1000 cases. Subsequently, the database is used to train a gradient boosting regression (GBR) model. The GBR model learns from the database and establishes the relationship between the construction parameters, ground parameters, and TBM position as a surrogate model. The surrogate model exhibited high accuracy on the test set. With a geological survey and a construction parameter monitoring system, the TBM position could be predicted quickly and precisely using a surrogate model. The construction parameters were adjusted if the TBM position did not satisfy the requirements.

Optimizing Greenhouse Design with Miniature Models and IoT (Internet of Things) Technology-A Real-Time Monitoring Approach.

The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper "mini-greenhouses" were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.

Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in Garang Watershed

Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in Garang Watershed

First results of the anomalies identification method for PV systems

A simple quality check procedure for early detection of photovoltaic (PV) isolated system anomalies had been previously presented [1]. The proposed method was developed over a short set of monitored system parameters from the Pierre Auger Project data base. In this paper we summarize the main results we obtained by using that protocol as well as the matching between them and the actual failures recorded within the Pierre Auger's data log.

T-Fire System: A Novel Integrated Fire Monitoring and Extinguishing System for Trucks

Nowadays, fires on heavy vehicles and trucks still represent a severe threat to environment and people safety, with serious consequences and injuries. In this context, fire losses can be minimized by preventing the rapid onset of fire and allowing drivers and occupants to escape safely by implementing an effective fire monitoring and extinguishing system. Commercially available fire extinguishing systems have shown multiple lacks concerning the structural implementation of the system only on specific types of heavy vehicles, the monitoring of system parameters, and the environmental toxicity of the extinguishing agents employed. Therefore, the present article proposes the early design and development of a novel integrated fire monitoring and extinguishing system for trucks and heavy vehicles called ‘T-Fire System’. The proposed solution can operate in any environment (confined and not) through a close interconnection between the onboard diagnostic sensors and the plug and play fire extinguishing system which employs environmentally sustainable fluorine-free extinguishing agent. The development of the T-Fire System has started from a patented prototype, and preliminary evaluations of both the monitoring and the fire extinguishing system have shown promptness and reliability. Further research will be focused on improving and customising the solution for the pre-industrialization phase of the system.

Preliminary results of an IoT-based prototype monitoring system for physicochemical parameters and water level in an aquifer: Case of Santa Marta, Colombia

The city of Santa Marta faces a significant potable water shortage of around 1600 liters daily, worsened by insufficient water sources and outdated supply systems. Annual precipitation of 362 mm exacerbates the problem. Groundwater, comprising 40% of the current supply, aims to alleviate this, yet doubts linger about water source quality and sustainability in a city with over half a million residents. This study employed real-time IoT monitoring for pH, dissolved oxygen, conductivity, and temperature. Data was Python-analyzed and graphed. Results indicate aquifer characteristic fluctuations, maintaining temperature and conductivity within limits, but with low dissolved oxygen and alkaline pH.

Analysis of climate risks impact on railway infrastructure and adaptation measures system development

Analysis of the impact of climate risks on railway infrastructure and development of a system of measures to adapt transport infrastructure to climate change. The object of the study is Russian transport infrastructure, analyzed using the method of identifying the peculiarities of the functioning of the transport infrastructure, taking into account regional specifics. An analysis of climate risk factors, the effects of climate risk factors, as well as the impact on railway infrastructure was carried out with the aim of integrated management of natural hazards and the development of adaptation measures to climate change. It is shown that improving measures and strategies for adapting railway transport to a changing climate may include the following systems: a system for preventing accidents and incidents; monitoring system for environmental parameters and infrastructure facilities; measures to protect railway transport facilities; introducing changes to production standards; relocation of infrastructure facilities when it is impossible to operate them in a given territory.

Internet of Thing untuk Memantau Sistem Kelistrikkan Pembudidaya Ikan Hias

The Pondakan Alam Sari ornamental fish cultivator group has approximately 50 types of ornamental fish cultivated for domestic and foreign commodities. Market demand for ornamental fish tends to increase yearly, but the market wants stable prices. This price stability is a dilemma for ornamental fish cultivator groups. One of the determinants of the selling price of ornamental fish is operational costs, especially electricity costs. The increase in basic electricity tariffs causes operational costs to increase. Electricity is used to power the circulating water pump 24 hours non-stop daily. This circulating water pump is very important for producing oxygen in the water. If there is no oxygen in the water, ornamental fish will die. To reduce operational electricity costs, the community service team built a DC electricity system sourced from solar energy so that the circulating water pump source no longer uses PLN electricity. This community service program aims to reduce operational electricity costs by building a DC electricity system sourced from solar energy and an IoT-based monitoring process. The implementation method consists of two stages: (1) building physical capital through a solar energy electricity source and DC pump installation and (2) creating a DC electrical parameter monitoring system with the Blynk IoT platform. The evaluation technique was carried out by comparing planning with the progress of implementing activities. The results and implications of activities include the availability of DC electricity sources from renewable energy, namely solar cells with a power of 960 WP, installing 10 DC pumps, and an IoT-based DC electrical parameter monitoring system. Operational costs in managing an ornamental fish business can be reduced by 46% from before using renewable and IoT-based energy. This result will certainly increase the profit margin of ornamental fish farming.

Prototype Development of an SMS-Based Industrial Water Tank Level Monitoring and Control Parameters System

This study describes the creation of a prototype SMS-based fluid parameter monitoring system. Many water industry and oil servicing/plant maintenance companies use workers to measure and monitor fluid parameters such as flow rate, temperature, pressure, and level (taking periodic readings). The method is determined to be extremely difficult and severe, raising serious concerns. To address the aforementioned issue, it is proposed that a system be developed that would reduce the staff stress of manual monitoring of fluid parameters and notify the same to personnel for necessary action. The suggested system is made up of both hardware and software. The hardware section consists of four units: power supply, input, control, and output, all of which were created utilizing a top-down system design approach. The C programming language was used to write the software that controls the entire system. Following that, multiple units were constructed and tested. When the system is turned on, the sensors monitor various fluid (water) characteristics and provide signals to the microcontroller, which analyzes the parameters and sends them to a display unit and the employee's mobile phone for alert notification. The outcome is quite precise and accurate. A simulation of the system performance was also performed in PROTEUS. This proposed method is inexpensive and can be implemented in oil service organizations to provide faultless services in place of employees.

Choice of the Parameters of an EMI Monitoring System for an AC Traction Network

The problem of ensuring the electromagnetic compatibility of the traction network with railway signaling lines is of practical interest, since electromagnetic interference from the traction network can cause malfunctions in the signaling systems. In order to ensure the electromagnetic compatibility of traction current with track circuits, which are widely used as track vacancy sensors in front of a moving train, electromagnetic interference in rails is periodically monitored in accordance with their maintenance schedule, which makes it possible to detect excessive increases in the level of interference in the rails in a timely manner and prevent possible failures in signaling systems. The problem of developing embedded systems for continuous monitoring of harmonics in the traction network lies in the need to ensure the necessary measurement accuracy of the interference monitoring system for a traction network in accordance with the strict requirements of standards. The correct choice of control system parameters is difficult because the traction current is a non-stationary random process, and the alternating traction current, in addition, has a large dynamic range of harmonic values, their proximity to each other and to a powerful fundamental harmonic, as well as the limitations inherent in the discrete Fourier transform, associated with the impossibility of simultaneously improving the accuracy of measurements in the time and frequency domains, which leads to the need for compromises when choosing the parameters of the monitoring system. The work is devoted to the practice of applying the theory of analog-to-digital conversion and the windowed Fourier transform to develop a technique for correctly choosing the parameters of an electromagnetic interference monitoring system for traction lines.

A wireless physiological parameter monitoring system with a treatment feedback function during neonatal phototherapy

Objective. Neonatal jaundice is a common condition in the early stages of newborns, and phototherapy is a fast, safe and effective method that is used to treat it. However, recent studies have shown that phototherapy may elicit side effects in infants, such as hypothermia, hyperthermia and dehydration. To improve the quality of phototherapy and the prognosis of patients, the changes in neonatal physiological parameters during phototherapy should be monitored to give better feedback to pediatricians or the phototherapy system. However, the current standard of clinical care during neonatal phototherapy with hard-wired devices limits this realization. Approach. Here, we developed a prototype of a neonatal wearable device, which can wirelessly potentially monitor the jaundice value, transepidermal water loss, skin wettedness factor and body orientation during phototherapy, and conducted prototype validation experiments. We also set up user-friendly interfaces and an analysis system on custom software, all designed to make the future addition of data interfaces for treatment feedback functions easier. Main results. The preliminary in vitro experiment demonstrated the effectiveness of simultaneous monitoring of the required physiological parameters. And further suggestions and specific operations are discussed in terms of optimization of the treatment of neonatal jaundice. Significance. It is believed that the established system has the potential to provide a basis for future phototherapy nursing guidelines and physiological monitoring standards.

Relationships of Water Quality Parameters for Hydroponic Production of Kale (Brassica oleracea) with In-Ground Passive Cooling System

Greenhouses in Philippines are encountering high ambient air temperature problem. Along with this, the water quality directly affects the growth, development and yield of hydroponically grown plants. This study was conceptualized through the principle of cooling the nutrient solution that will effectively regulate plant’s growth and yield with relatively low investment and easier management. One of the essential elements of a functional hydroponics system is water quality monitoring, hence this study utilized a monitoring system for water quality parameters. Nutrient solution was closely monitored to maintain and ensure that nutrient levels are not too low to inhibit growth and not too high to be potentially toxic. The study was conducted to develop a monitoring system for environmental and water quality parameters of in-ground passive cooling system for hydroponic production of kale. Specifically, the study aimed to determine the relationships of water quality parameters such as pH, EC, TDS, and temperature for kale production, compare the performance of an in-ground passive cooling system (IGPCS) and without cooling system (Control) on hydroponically grown kale, and to evaluate the water productivity of the in-ground passive cooling system. This study utilized both in-ground passive cooling system (IGPCS) and control (without cooling system) to compare its performance based on the growth and yield of the kale, and to determine the water productivity of utilizing IGPCS. In addition, water quality parameter such as pH, EC, TDS, and temperature were closely monitored using the installed monitoring system, and relationships of these water quality parameters were established. The gathered data were analyzed using trend analysis in Microsoft Excel, Analysis of variance (ANOVA) was computed in Completely Randomized Design (CRD) and comparison among means were compared using Least Significant Difference (LSD) Test at 5% level. Results revealed that EC and TDS of nutrient solution are inversely proportional to pH and temperature of nutrient solution, moreover EC and TDS are directly proportional, also with pH and temperature of nutrient solution. The performance of the IGPCS and without cooling system is comparable with growth and yield of kale in terms of leaf length, leaf width, number of leaves, weight and yield at harvest, but are significantly affected by the location of growing pipes where crops received less and ample amount of sunlight. Since the temperature of IGPCS was reduced up to 3°C, kale plants with cooling system uptake less water compared to without cooling system and results to higher productivity. Therefore, regulating one of the water quality parameters directly affects other water quality parameters, so relationships of these water quality parameters were established through close monitoring of water quality parameters using the monitoring system. Utilization of IGPCS reduced the temperature of the nutrient solution up to 3°C which gave higher yield (44.01 g/plant) than without cooling system (41.18 g/plant). Kale production in tropical areas can have higher yield using the in-ground passive cooling system that reduced water consumption, resulting to higher productivity.