LabVIEW Software Research Articles

Experimental Study of Vibration Isolation Using Electromagnetic Damping

This study presents an experimental investigation focused on the reduction of vibrations using electromagnetic damping techniques. The objective is to explore the effectiveness of electromagnetic damping in mitigating undesirable vibrations and enhancing system stability. A single-degree vertically constrained spring-mass system, two pairs of electromagnets, Lab VIEW software, an accelerometer, DAQ card were used for the experimentation. SWG 17 and SWG19 coil electromagnets were used in different conditions of energization to evaluate the effectiveness of the electromagnetic damping system. The parameters such as excitation frequency, vibration amplitude, and electromagnetic damping force are systematically varied and their effects on vibration isolation are analysed. The results demonstrate that the electromagnetic damping system effectively isolates base induced vibrations across a range of frequencies and amplitudes. The experimental data reveal that at lower frequencies, upto 6Hz the amplitude of RMS acceleration was same as that of the system without energizing the electromagnet and at higher frequencies, above 10 Hz, all 2V,4V and 6V of energized electromagnets in the SDVC system showed negligible variation in the RMS amplitude of acceleration. A substantial isolation of top plate was observed at higher frequencies of base excitation.
 

ENERGY TECHNOLOGY OF DAIRY PRODUCTS QUALITY ASSESSMENT USING IMPEDANCEMETRY

The article examines the peculiarities of milk quality research using the impedance method. Impedance is a complex parameter that characterises the interaction of the electric current with milk. The authors have developed a methodology based on the specified method to assess the quality of dairy products and have created a hardware and software complex that makes it possible to analyse the quality of dairy products. The research, conducted using the developed hardware and software complex, revealed several regularities characterising different types of milk. These regularities demonstrate the relationship between the impedance signal and the milk fat index. Analysis of the obtained data allowed us to identify differences in impedance between samples with varying fat content and origin from different manufacturers. The developed experimental setup prototype enabled the following research stages: checking the change in voltage during the passage of an alternating electric current through milk. This work used the AFS Sensor DAQ data acquisition system, designed for sensor control projects and which runs with NI LabVIEW software, for data output. The authors tested the installation while investigating the impedance of dairy products with different fat content and from multiple manufacturers. During the experiment, we measured the impedance and capacitance of the samples, and the obtained data became available on a PC for further analysis. The aim was to study the electrical properties of dairy products and their dependence on the composition and manufacturer. The obtained results represent valuable information for dairy product production and quality control. It is possible to use them to optimise production processes, ensure stable product quality, and improve consumer characteristics of dairy products. The obtained data contribute to a deeper understanding of the relationship between the impedance signal and the composition of dairy products, which can lead to the development of new methods of analysis and evaluation of the quality of food products. In addition, these studies have implications for food science and nutrition. Keywords: dairy products, milk quality, impedancemetry, electrical measurements, hardware and software complex.

SIMULATION OF MULTIPOINT CONTACT UNDER THERMOELECTRIC TESTING

The article presents the results of modeling a multipoint contact in the thermoelectric method of testing. Sources of thermoelectromotive force (thermoEMF) have shown different influences, according to their type, on the load characteristics of the equivalent source obtained by parallel connection of several thermocouples as shown in the explanatory figures. The presented model is implemented in the NI LabVIEW package, which is freely available. The model was verified on three types of thermocouples (copper-constantan, copper-nichrome, and chromel-alumel), which have been connected in parallel in different quantities. The calculated load characteristics of the equivalent sources differ from the experimental ones by no more than 6%. The results of modeling the load characteristics of the equivalent thermoEMF sources obtained by parallel connection of different quantities of two types of thermocouples are presented. It is shown that in order to obtain reliable data, it is necessary to provide an equivalent source load of at least 1 kOhm.

Prediction and Therapeutic Design for Diabetic Retinopathy Using LabVIEW

Diabetic Retinal Disease (DR) is a gradual obstruction in the functioning of the blood vessels in the retina of the eye caused by chronic hyperglycemia, which may be due to diabetes type 1 or type 2. LabVIEW software play as major role in predicting the diabetic retinopathy at different stages. The real time image of the affected eye is acquired using Vision Actuation tool in LabVIEW and then it is processed using Vision assistant tool. Lot technology is I for sending the output result to the corresponding end client. Image processing and lot. Both are implemented using LabVIEW. Additionally, the system extends its functionality beyond diagnostics by incorporating an innovative approach to monitor the temperature of the eye. By integrating LabVIEW's capabilities with lot, the proposed solution checks the temperature the eye in real-time. If the detected temperature is elevated, a Peltier crystal is employed to cool the eye, demonstrating an adaptive and responsive mechanism to address abnormal temperature conditions. Aims to check the patients whether they are suffering from diabetic retinopathy or not and if they are having the diabetic retinopathy in which stage it is. Because most diabetic people are suffering from this and may also lead to loss of vision. Due to this reason, it is highly required to classify the persons in a short time with high accuracy. Automation reduces human error and subjectivity. Can detect early stages of disease. Ability to process and analyze digital fundus images efficiently.

Modeling and control of electrical discharge wire sawing of single-crystal silicon

Electrical discharge wire sawing (EDWS) is a new composite processing method that combines wire electrical discharge machining with fixed abrasive wire sawing. During processing, relying on the grinding effect of the abrasive grains, craters and recast layers generated by discharge are removed. The surface roughness of the workpiece is reduced while the cutting efficiency is improved. In this study, the relationship between discharge voltage and workpiece surface roughness in composite machining is investigated. A single-input single-output control system model with applied resistance as the input and discharge voltage as the output is established. Based on the theory of system identification and parameter estimation, the order of the model is identified by calculating the variance of the model residuals and using the F-test method. The model parameters of the control system are estimated online using a recursive least squares algorithm with a forgetting factor. A generalized predictive controller is designed on the basis of the predictive control theory and the identified discharge voltage control system model. Using LabVIEW software, a discharge voltage prediction control system is constructed for the processing of single-crystal silicon by EDWS, and its prediction control performance is experimentally verified. The results show that the discharge voltage control system designed in this study can successfully control the composite machining of single-crystal silicon in real time. Under the same machining conditions, the surface roughness of the workpiece machined with predictive control was 23.5 % lower than that without predictive control.

Analysis of the linear regime of a power amplifier for underwater acoustics experiments

When transmitting sound in a laboratory water tank, a power amplifier is needed to boost the generated signal. If the amplitude of the generated signal is too high, the power amplifier introduces harmonics into the transmitted signal. Measurements were taken to determine the maximum input amplitude for which the TEGAM 2350 power amplifier, which provides x50 amplification, has a linear response. To evaluate when harmonics are generated, acoustical measurements were taken in our acrylic 1.22 m by 3.66 m tank filled with 0.5 m of water. Custom LabView software generated a chirp with a specified amplitude. This signal was sent through the power amplifier and an impedance matching transformer to a B&K 8103 transmitter. The sound was received on the B&K 8103 hydrophones. Both were attached to two different UR10e robots that could be positioned throughout the tank. The receiver hydrophone was moved to three different locations in the tank for a variety of chirp amplitudes to find the maximum amplitude without harmonic generation. Results of these measurements are shown for three frequency bands: 10–50 kHz, 50–100 kHz, and 100–150 kHz . [Undergraduate research supported by the College of Physical and Mathematical Sciences, Brigham Young University]

Design and Implementation of Smart Petrol Station

The problem that still exists nowadays with the petrol station is the method of operation because the petrol station is currently operated manually. As it is a time-consuming process that increases manpower, other problems are related to accuracy, gasoline smuggling, fluctuations in global oil prices, sales, database management, environmental pollution and others. Traditional methods of monitoring fuel in petrol station by humans on site are unable to meet the expectations for efficiency, accuracy and cost. Therefore, this paper designs an intelligent system of three filling stations, where the three stations are simultaneously displayed on a single web application, and this IoT-based system is implemented to address all the problems. Therefore, this paper presents the design and implementation of three petrol stations in which we are going to measure the level of fuel and show it to central server. internet of things (IoT) based petrol station monitoring system is a good approach to improve monitoring efficiency and to improve management efficiency in stations remotely. simulation results presented in LabVIEW software showed the ability of the system to monitor levels of petrol, detect fire, evaporation and etc.

Eight-channel signal synchronous acquisition system based on PCI and LabVIEW

In response to the multi-channel acquisition of the power grid simulator data collection system, this article builds a 8-channel signal synchronization acquisition system based on PCI-1706U and LabView, which introduces the characteristics of PCI-1706U hardware and LabView software DAQ system. A LabView DAQ program that uses the PCI-1706U to collect 8-way signal from the signal generator, complete the experiments of multi-channel synchronous acquisition and delayed start to collection, verify the feasibility and stability of the system, and analyzed the collected signals. Finally, the main work and innovation points of the thesis are summarized, and suggestions and outlook for future improvement are given. This study provides an effective implementation method based on PCI-1706U and LabView multi-channel signal synchronous acquisition system and delay acquisition function, with certain application value and promotion space.

Experimental analysis of vapour compression refrigeration system using nano lubricant with refrigerant R-134a

It has been proved that due to addition of nanoparticals to the refrigerant results in improvement in thermophysical properties and heat transfer characteristics of the primary refrigerant, which improves the coefficient of performance of the refrigeration system. In this experimental analysis of vapour compression system, the performance study has been carried out by preparing the nanolubricant. In this experimental study, three cases are considered. The compressor of the system filled with pure polyolester oil, SUNISO-3GS oil and polyolester oil + titanium dioxide nanoparticles as lubricant. In the nanolubricant, the volume concentration of the nanoparticles is 0.2 %. The experimental study shows that vapour compression refrigeration system works smoothly with nanorefrigerant. In this experimental study, it has observed that when nanolubricant used in place of traditional Polyol ester oil, the refrigerating effect has been increased and the compressor power consumption has reduced by 27% and the refrigeration system coefficient of performance improves by 29%. The analysis shows that the enhancement factor in the evaporator is 1.2 when nanorefrigerants are used instead of pure refrigerant. The properties of refrigerant like thermal conductivity and density are also studied for the nanolubricant by using the Labview software, in which the thermal conductivity & the density of R134a and nanolubricant mixture are observed higher in comparison with the R134a and pure lubricant mixture.

Improvement of a mine’s ventilation test bench, using industrial communication and open-source microcontrollers.

This paper shows the results of optimizing a test bed used to simulate parameters related to Underground mines ventilation. The data acquisition card was changed for an Arduino microcontroller; likewise, some sensors were changed for more precise ones, and a sensor was adapted to measure relative humidity, dry temperature, and heat index. The measurements will continue to be observed using the LabVIEW software and complemented by an industrial communication system, the Modbus protocol. This protocol increases the data transmission speed of Ethernet. With the improvement of the system, the precision in the parameters (Pressure, temperatures, relative humidity, concentration of methane gas, Voltage, current) increases, and ultra-fast data transmission is guaranteed.

Machine Learning tool to prevent and control Bag Filter clogging

Filter clogging represents a crucial issue in industrial filtration systems, and it can lead to various problems, such as reduced filtration efficiency, increased maintenance costs, and potential production disruptions. This paper presents a Machine Learning (ML) integrated solution, developed using Python, within a digital environment system created using LabVIEW to prevent and control filter clogging. The primary objective is to optimize the monitoring, control, and maintenance of a Bag Filter pilot plant. The digital environment incorporates a data comparison tool that trigger a compressor for filter cleaning. A Machine Learning algorithm has been integrated into the digital environment, where a comparison within real data acquired and the predicted one were performed to validate the selected algorithms and evaluate the Data Comparison tool. This initial phase's outcomes were used to develop a control system using LabVIEW software. The software compares estimated pressure drop values, reflecting optimal working conditions, with directly recorded pressure drop values. If the difference exceeds 10% of the optimal pressure drop, the controller activates a compressed air cleaning system, removing excess dust from the filter sleeves using an air jet. Additionally, when the estimated and acquired values diverge consistently over a 60-minute period, a user warning is triggered for filter sleeve replacement. Several tests were conducted at different air inlet velocities to assess the error between acquired and predicted data. The results indicated that the ML algorithm's error distribution remained below 5% for approximately 80% of the cases.

AUTOMATIC DETECTION AND RECOGNITION OF DIGITAL MODULATED SIGNALS USING A NEURAL NETWORK

Issues on automatic detection and recognition of digital modulated signals are becoming increasingly relevant in modern communication systems. These processes play a key role in ensuring reliable and efficient communication in a wide range of areas, from mobile communication to broadcasting and satellite systems. The importance of these processes continues to grow with the increase in the amount of data transmitted through these systems and with the development of new communication technologies. Accurate detection and classification of signals allow communication systems to correctly interpret the transmitted information, which, in turn, ensures the reliability and efficiency of data transmission. This is especially important in conditions when signals are subject to various types of interference and distortions. Therefore, the development and improvement of the methods for automatic detection and recognition are becoming increasingly important for ensuring high-quality communication. The present article presents a neural network-based system capable of automatically detecting signals with a single carrier frequency in the range up to 6 GHz and classifying four types of digitally modulated signals: BPSK, QPSK, 16-QAM, and 64-QAM. The system is capable of operating in the presence of additive white Gaussian noise, phase and frequency offset in the received signal. The algorithms for detection and recognition of signals, developed in the LabVIEW software environment are presented. The architecture and hyperparameters of the neural network allowing quick and efficient training of the network are described. These algorithms and training methods can be applied and adapted for other systems and applications, making them very useful for a wide range of tasks. The system was tested both on simulated signals and in a real communication channel. The results obtained confirm the effectiveness of the proposed methods and emphasise their potential for further application and research.

小区联合收割机智能测产系统的设计与试验

Traditional yield monitoring in breeding plots is manually conducted, which is labour-intensive and inefficient. To address this issue, an airborne yield monitoring system that collects yield information during the harvesting in plots was designed. The system consists of a host computer and a slave computer. The slave computer used weight and moisture sensors to collect yield information of crops. The host computer used LabVIEW software to design a data management platform that displays and saves yield information and draws yield distribution maps for data visualization. The host and slave computers communicate data through 4G networks. By designing anti-interference measures, the dynamic experimental results showed that the average errors between the measured weight and moisture content by the system were 1.330% and 2.800%, which met the accuracy requirements of yield testing in plot breeding. The results indicated that the structure of the system is reasonable and can provide reliable yield monitoring tools for researchers in breeding plots.

Simulation of PMSM Vector Control Using Multisim and LabVIEW

This paper represents the simulation of field-oriented control of a permanent magnet synchronous motor. Using Clark-Park transform and three PI controllers for controlling speed, direct-axis current, and quadrant axis current. As maximum torque occurs when the rotor field and stator fields are 90 degrees from each other. The goal is to bring the stator field always orthogonal to the rotor field. The simulation is performed using Multisim and LabVIEW software

Sound-card-based Johnson noise thermometer

This article presents a Johnson noise thermometer with a signal acquisition system based on a sound card. The thermometer uses a simple analogue circuit to measure the Johnson noise on a resistor. No external hardware or power supply is necessary, except for a reference multimeter. Data processing is performed in real time on a personal computer using LabView software. The results show that the developed thermometer has an expanded uncertainty (k = 2) of 0.8 ℃ over a temperature range from − 196 ℃ to 300 ℃, with a measurement time of 5 min.

SYSTEM OF DETECTION OF VENTRICULAR ARRHYTHMIYS

Determining the boundaries of QRS complexes in ECG signals is of fundamental importance in the diagnosis of arrhythmias and in the recognition of signals in clinical manifestations in general. Existing systems of mobile diagnostics of arrhythmias do not meet modern requirements: some algorithms are too complicated for software implementation, others are not sufficiently resistant to interference. Ventricular extrasystoles, as one of the most important studies in arrhythmology, are currently considered one of the main causes of sudden cardiac death, and their timely detection and objective diagnosis, as well as proper prevention, remain one of the most pressing issues. One of the urgent issues is the development of simple (acceptable for mobile devices – which can be implemented online) algorithms for the recognition and classification of ventricular arrhythmias. The aim of the work is to develop a system for determining the informative parameters of the ECG signal in real time for the diagnosis of ventricular extrasystoles and parameters of heart rate variability. An algorithm for recognition of arrhythmias (ventricular extrasystoles) is proposed, which is characterized by ease of implementation and minimal requirements for computing resources, while maintaining high values of sensitivity and specificity for ECG signals with sinus rhythm and single ventricular extrasystoles. The algorithm is implemented in the Labview software environment. Using ECG files taken from international databases, the algorithm and system were tested to determine the parameters of heart rate variability. Keywords: electrocardiographic signal, arrhythmia, heart rate variability, ventricular extrasystole

Evaluation of a cocoa dryer prototype using LabVIEW software and Arduino hardware

In a country like Colombia, food drying plays an important role in the extension of technology so that farmers improve the quality of their productivity. The objective of this work is to develop a system for acquiring thermodynamic variables, to establish the energy efficiency of a product drying oven, specifically for cocoa beans using Arduino hardware and LabView software. This research begins by establishing the variables to be measured and recorded by the DHT11 sensors, such as temperature and humidity. These sensors are connected to an Arduino Uno board that has an Atmel microcontroller, which captures the information from each of these. An algorithm was also developed that captures the temperature and humidity data and sends it through the serial port to the LabView software, in which the visualization on the front panel and programming in the block diagram have been developed, being viewed from the HMI (Human Machine Interface). For the experimental tests, 3,309 grams of fermented cocoa beans were taken, and dried for 36 hours, removing a total mass of water of 1,650 grams. The results show an energy efficiency of 10.62%, concluding that the drying oven that takes advantage of the residual heat of an 18000 BTU condenser integrated with the proposed variable system is suitable for drying cocoa beans. Despite this efficiency being low, it meets the objective of removing moisture from them.

Self-tuning triboelectric nanogenerator for omnidirectional broadband (1.2–13.8 m/s) wind energy collection and wind vector detection with deep learning

Wind speed and wind direction are not only important meteorological factors in environmental dynamic monitoring but also crucial parameters for harvesting considerable renewable wind energy. Although the multi-uniform unit arrangement of triboelectric nanogenerator (TENG) is a strategy for extending the responsive wind speed and direction, interunit crosstalk, low wind direction resolution, narrow working wind speed range, and unclear complex interaction between wake and wind capture component hinder further applications of wind-driven TENG. Here, a self-tuning triboelectric nanogenerator (S-TENG) is developed to effectively harvest broadband omnidirectional wind energy and simultaneously meet the high-resolution detection of wind vector. The gas-solid interaction model of this device is proposed as a two-step process. Meanwhile, owning to the self-tuning ability, the device greatly expands its working range from 2.4 to 6.4 m/s to 1.2–13.8 m/s, giving an excellent output power of 47.43 W/m3. Additionally, a real-time wind vector sensing system based on LabVIEW software exhibits wind direction resolution of up to 5° with only three channels. The recognition accuracy of wind direction relying on deep learning model reaches 98.98%. This work provides a guidance for designing effective high entropy wind energy harvesters and a deep understanding of gas-solid interaction processes.

Fault detection and identification in three phase transformer using AI based FSA and PVR analysis

The objective of this paper is to identify the winding faults in three phase transformer. A prototype model of the transformer has been designed and simulated using finite element method (FEM) based CAD package called Infolitica MagNet 6.11.2 in transient 2D solver. From the simulated results the peak variation response in flux linkage signature, flux density magnitude has been captured to identify the transformer winding faults. Then the faulty transformer result has been compared with simulated healthy transformer result. The design incorporated with external taps to create winding short circuit faults. The flux linkage simulation results are verified and validated with an experimental setup by the help of LabVIEW software.

Machine learning-based system for vacuum leakage detection in EAST fusion device

In order to achieve real-time monitoring of partial pressures of residual gases and timely detection of vacuum leakage in fusion devices, a machine learning-based system was successfully developed on the LabVIEW software platform for its first-time application in the EAST fusion device. This system acquires partial pressure data from the quadrupole mass spectrometer (QMS) in both histogram and P&T scanning modes to identify the nature of vacuum leakage. The backpropagation (BP) neural network algorithm is employed for leakage detection. To train the BP neural network model, historical data from normal and leakage scenarios on the EAST device are utilized as inputs. Subsequently, the trained model is integrated into the system. The BP neural network model exhibited an identification accuracy of 95.27 % on the test set, effectively detecting water and gas leakage on EAST. Consequently, this system enables rapid detection of vacuum leakage in fusion devices, facilitating prompt mitigation of their impact on plasma experiments and ensuring the device's safety and stability.