Automated Data Acquisition System Research Articles

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

Exploring high-rise preventive ventilation: Experimental investigation of inter-zone air pressurization with tracer gas analysis

Over the past four years, five major variants of the SARS-CoV-2 virus have circulated globally, causing seven million deaths. Meanwhile, specific preventative ventilation measures were recommended to minimize exposure to the aerosol infection risk, such as opening doors, and disabling ventilation systems. Given more attention to high-rise buildings, where quite a few outbreaks have been reported, there is a notable lack of reported studies assessing preventive ventilation measures. Therefore, this study focused on practicing several ventilation measures in a 16-story high-rise building located in Montreal, Canada. A variety of inter-zonal tracer measurements, including room-to-floor and inter-floor tests, were carried out considering controlled airflow/pressure across zones. An automated data acquisition system was developed for real-time monitoring of tracer/CO2 spatial concentrations. Findings show that enabled ventilation with opened doors reduces CO2 concentrations by up to 82 % in nearby source locations and positively pressurized zones; however, negatively pressurized zones experience a 40 % increase in tracer exposure. With ventilation active, a corridor source leads to a higher risk of tracer exposure (up to 86 %) than the in-room source. Disabled ventilation with closed doors is a recommended ventilation measure, for corridors with infection sources, to minimize tracer transport across zones. Zones with infection sources follow the latter ventilation measure; however, ventilation with open doors offers a conditional ventilation measure if careful airflow is designed particularly at return and exhaust points within the ventilation system. This study has implications for practicing preventative ventilation measures for post-COVID-19 building practices and design considerations.

Develop an automated data acquisition system for TPS correction magnet power supply

Taiwan Photon Source is a third-generation 3 GeV synchrotron light source. Since the successful operation of the first beamline in 2013, comprehensive long-term testing and acceptance work has been carried out to ensure the stable operation of various types of correction magnet power supplies, totalling 1038 units. To standardise and save workforce and time for the measurement of correction magnet power supplies, an automated data acquisition system platform for correction magnet power supplies was developed using LabVIEW. This platform integrates control interface cards, high-resolution digital voltmeters, and analysis instruments. It also includes linearization and calibration compensation for the multi-channel direct constant current transformer to ensure simultaneous measurement of 24 magnet power supplies. Furthermore, it can automatically generate test reports in Word format, saving significant human resources and time. This platform has verified TPS correction magnet power supplies before installation, reducing the failure rate. Regarding measurement results, the output current ripple for the DCCT version is within five ppm, while the shunt resistor version is within ten ppm. The long-term current stability is consistently within ten ppm. The development of the fully automated data acquisition system platform and the measurement of 1038 power supplies were completed in four months.

Multi-Species Weed and Crop Classification Comparison Using Five Different Deep Learning Network Architectures

Highlights ConvNeXt model was applied for weed and crop species classification, a first in this field. Five CNN architectures were utilized to classify six weed species and eight crop species. CNN architectures with millions of parameters were trained for improved performance. All CNN models showcased impressive performance, except for Densenet. Abstract. The detection of individual weed and crop species from RGB images is a challenging task that becomes even more difficult as the number of species increases. This is because similarities in the phenotypic traits of weeds and crops make it difficult to accurately distinguish one species from another. In this study, five deep learning Convolutional Neural Networks (CNNs) were employed to classify six weed and eight crop species from North Dakota and assess the performance of each model for specific species from a single image. An automated data acquisition system was utilized to collect and process RGB images twice in a greenhouse setting. The first set of data was used to train the CNN models by updating all of its convolutional layers, while the second set was used to evaluate the performance of the models. The results showed that all CNN architectures, except Densenet, demonstrated strong performance, with macro average f1-scores (measurement of model accuracy) ranging from 0.85 to 0.87 and weighted average f1-scores ranging from 0.87 to 0.88. The presence of three weed classes—palmer amaranth, redroot pigweed, and waterhemp, all of which share similar phenotypic traits—negatively affected the model's performance. In conclusion, the results of this study indicate that CNN architectures hold great potential for classifying weed and crop species in North Dakota, with the exception of situations where plants have similar visible characteristics. Keywords: Deep learning, Precision agriculture, Weed and crop classification.

Point cloud enhancement optimization and high-fidelity texture reconstruction methods for air material via fusion of 3D scanning and neural rendering

In order to realize the digital management and manufacturing of air material, the methods of 3D point cloud enhancement optimization and high-fidelity texture reconstruction in entity digitization of air material based on the fusion of 3D scanning and neural rendering are proposed. In this paper, an automatic data acquisition system is designed and built, and the acquired training images are enhanced. Then the Yolov8 segmentation model and CascadePSP boundary refinement model are combined for training to realize foreground segmentation and background removal of air material images. In this paper, a 3D point cloud enhancement optimization method is proposed, which combines the point cloud obtained by binocular structured light scanner and the point cloud reconstructed by Colmap algorithm for registration and fusion. By combining the coarse registration of PointNetLK network with the fine registration of ICP algorithm, a more complete and high-quality dense point cloud is generated, and the effects of illumination conditions and image quantity on point cloud reconstruction is studied experimentally. Using the optimized point cloud model and random perspective enhancement (RPE) method, the neural rendering model NeuS is improved, which improves the 3D surface texture reconstruction quality and the sight extrapolation effect. In order to verify the effectiveness of the proposed method, comparison and ablation experiments are carried out in this paper. The experimental results show that point cloud enhancement optimization and RPE have a good effect on improving the model, and the model performance has been greatly improved.

Rethinking Reliability in Terms of Margins

Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance.
 This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources.
 This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

New concept of permafrost degradation monitoring based on photonics technologies: Case study from Calypsostranda (Bellsund, Svalbard)

AbstractGround temperature measurements are crucial for a better understanding of changes in the natural environment, especially in the Arctic. Previous measurement systems provided accurate measurements; however, their most significant disadvantage was the relatively low spatial resolution, including in the vertical profile. The aim of this work was to develop and initially validate a new, original temperature measurement system based on the photonic sensing technique of optical frequency‐domain reflectometry (OFDR). The system consists of a fibre‐optic sensor, an interrogator, and an automatic data acquisition system. Such fibre‐optic sensors allow a significant increase in spatial resolution. Data on precise temperature distribution in the ground profile will allow for a detailed determination of the changes in the thickness of the permafrost active layer (PAL) and, as a consequence, a better description of the current state of the permafrost and the layers above it in relation to their progressive degradation. In the longer term, it will make a better prediction of the pace of possible changes in the polar environment and will open up previously unavailable opportunities in the field of climate change monitoring and forecasting.

A tensile property measuring system for miniaturized samples from 300 K to 70 K based on pulse tube cryocooler

With the rapid development and wide application of cryogenic, superconducting and space technologies in engineering fields, measurements of the mechanical properties especially the tensile properties of small-sized samples at cryogenic temperatures are more and more important. In this study, a tensile property measuring system for miniaturized samples was designed and built, where the samples can be measured from 300 K to 70 K with a resolution of 0.1 K. The cryogenic environment was provided by a pulse tube cryocooler. The force load applied to the sample was up to 10 N with a resolution of 0.001 N. The structure of the cryogenic system and sample holders were specially designed for miniaturized samples. A control and automatic data acquisition system was used to control and acquire data from all devices of the instrument. The simulations of the stretching part under load at cryogenic temperature were performed to ensure the measurement accuracy and reliability. To verify the performance and accuracy of the system, the tensile properties of two kinds of fine high-purity copper wires were measured at both 77 K and 300 K. The tensile measurement results were discussed and the measurement uncertainty of the system was analyzed. With different clamps more kinds of mechanical measurements of miniaturized samples can be carried out on this system.

Development of an electronic profilometer to measure mobilization variables in soil harrowing

Aim of study: This experiment's objective is to develop an automatic data acquisition system for profilometry, evaluating four harrowing speeds. Area of study: Federal University of Parana, Curitiba, Brazil. Material and methods: We experimented at the laboratory using a completely randomized design, comparing the data of modified roughness, raised and mobilized area, blistering, and thickness. These were acquired with traditional and electronic profilometers in seven replications. We executed the field test in lines, using a completely randomized design. The profilometers were in the plots and the targeted speeds in the subplots. We submitted the data for analysis of variance and when significant, to Tukey's test and regression analysis. Main results: Laboratory testing showed no significant difference in the parameters of modified roughness, elevated and mobilized area, blistering, and thickness, denoting the phase validation that indicates applicability in the field. The field testing presented superior results for the electronic profilometer in elevated and mobilized areas and soil layer thickness. That is due to the absence of interference in the measurements that occur in the conventional profilometer caused by the insertion of the rods in the soil. Research highlights: The increase in the mechanized set speed provided the reduction of the elevated area and soil blistering caused by the rise in disc rotation and consequent deviation of the soil particles.

Explanation of an innovative programming approach to create a progressive two-dimensional data file of three inter-dependant variables in a fully automated data acquisition system

Explanation of an innovative programming approach to create a progressive two-dimensional data file of three inter-dependant variables in a fully automated data acquisition system

The Utilisation of Electrical Resistivity Imaging (ERI) for Geological Structures Mapping in Rock Mass: A Review

Electrical Resistivity Imaging (ERI) is an in-situ geophysical method widely used in environmental, engineering and hydrogeological explorations due to cost, time, and data coverage efficiency. Traditionally, geotechnical parameters such as discontinuities properties are obtained from rock samples acquired from a borehole. However, it only provides a single information point, costly and time-consuming. Thus, an approach using electrical resistivity to detect the presence of geological structure in a rock mass is an alternative method that can be applied. Discontinuities in rock mass include joints, bedding planes, blasting cracks, fractures, faults, and folds. This paper aims to present the utilisation of geoelectrical resistivity imaging for geological structures mapping in rock mass based on the review from previous research. The Electrical Resistivity Imaging (ERI) method is an effective tool to obtain structural differentiation of geological medium by interpreting 2D and even 3D electrical resistivity models. Electrical Resistivity Imaging (ERI) is an automated data acquisition system, inversion codes, non-invasive and user-friendly. The raw data received from data acquisition was analysed using Res2DINV software. The data inversion was done by applying the robust method and finite element grid to accommodate the steep topography. The data interpretation included numerical modelling to assess the suitability of all used electrode arrays in relation to the geological setting.

Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model.

Detecting early signs of plant diseases and pests is important to preclude their progress and minimize the damages caused by them. Many methods are developed to catch signs of diseases and pests from plant images with deep learning techniques, however, detecting early signs is still challenging because of the lack of datasets to train subtle changes in plants. To solve these challenges, we built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests. After obtaining 13,393 plant image data, our ensemble model shows a decent detection performance with an average of AUPRC 0.81. Also, this data acquisition and the detection process can be applied to other plant anomalies with the collection of additional data.

Performance evaluation of PV powered solar tunnel dryer integrated with a mobile alert system for shrimp drying

The study was aimed at evaluating the drying characteristics, modelling of drying behaviour, and performance of PV powered walk-in-type semi-cylindrical forced convection solar tunnel dryer integrated with a mobile alert system using shrimps (Metapenaeus dobsoni). The dryer was fabricated using UV stabilized polythene film, CPVC pipes, and GI frames and coupled with a PV panel. A mobile alert system consisting of an Arduino UNO, load sensor, and GSM module was evaluated as an automatic data acquisition and information support system for the dryer users. Fresh shrimp samples were dried to the moisture content of 10.54% (w.b) from 79.22% (w.b) in 6 h. The graph of drying rate against the time exhibited that shrimp drying took place under the falling-rate drying period. Mathematical modelling of the drying process revealed that the Page model could accurately predict the drying behaviour of shrimp. The maximum drying efficiency of 20.22% was attained for drying shrimp in the solar tunnel dryer under forced convection. PV panel obtained an average electrical efficiency of 11.64% and supported the load requirement of 0.636 kWh/day for drying. The mobile alert system was effectively tested as an automatic data acquisition and information support system for dryer users.

Performance of electrical energy monitoring data acquisition system for plant-based microbial fuel cell

Plant microbial fuel cell (Plant-MFC) is an emerging technology that uses the metabolic activity of electrochemically active bacteria (EABs) to continue the production of bioelectricity. Since its invention and to date, great efforts have been made for its application both in real-time and large-scale. However, the construction of platforms or systems for automatic voltage monitoring has been insufficiently studied. Therefore, this study aimed to develop an automatic real-time voltage data acquisition system, which was coupled with an ATMEGA2560 connected to a personal computer. Before the system operation started it was calibrated to obtain accurate data. During this experiment, the power generation performance of two types of reactors i.e. (i) Plant-MFC and (ii) control microbial fuel cell (C-MFC), was evaluated for 15 days. The Plant-MFC was planted with an herbaceous perennial plant (Stevia rebaudiana), electrode system was placed close to the plant roots at the depth of 20 cm. The results of the study have indicated that the Plant-MFC, was more effective and achieved higher bioelectricity generation than C-MFC. The maximum voltage reached with Plant-MFC was 850 mV (0.85 V), whereas C-MFC achieved a maximum voltage of 762 mV (0.772 V). Furthermore, the same reactor demonstrated a maximum power generation of 66 mW m¯2 on 10 min of polarization, while a power density with C-MFC was equal to 13.64 mW m¯2. S.rebaudiana showed a great alternative for power generation. In addition, the monitoring acquisition system was suitable for obtaining data in real-time. However, more studies are recommended to enhance this type of system.

Automation of monitoring of drying parameters in hybrid solar-electric dryer for agricultural products

ABSTRACT Increasing the efficiency of solar dryers with ensuring that the system remains accessible to all users can be achieved with their automation through low-cost and easy-to-use technique sensors. The objective was to develop, implement and evaluate an automatic system for monitoring drying parameters in a hybrid solar-electric dryer (HSED). Initially, an automated data acquisition system for collecting the parameters of sample mass, air temperature, and relative air humidity was developed and installed. The automatic mass data acquisition system was calibrated in the hybrid solar-electric dryer. The automated system was validated by comparing it with conventional devices for measuring the parameters under study. The data obtained were subjected to analysis of variance, Tukey test and linear regression at p ≤ 0.05. The system to turn on/off the exhaust worked efficiently, helping to reduce the errors related to the mass measurement. The GERAR Mobile App showed easy to be used since it has intuitive icons and compatibility with the most used operating systems for mobile devices. The responses in communication via Bluetooth were fast. The use of Arduino, a low-cost microcontroller, to automate the monitoring activity allowed estimating the mass of the product and collecting the drying air temperature and relative air humidity data through the DHT22. This sensor showed a good correlation of mass and air temperature readings between the automatic and conventional system, but low correlation for relative air humidity. In general, the automatic data acquisition system monitored in real time the parameters for drying agricultural products in the HSED.

Low-Cost Data Acquisition System for Solar Thermal Collectors

Solar thermal collectors are among the most popular renewable energy research subjects. Automatic Data Acquisition Systems (ADAS) have greatly facilitated their experimental testing, but their high cost is a drawback. In this paper, we present the design and testing of a decentralized, low-cost alternative ADAS based on the ESP32 microcontroller and on open-source software. The proposed system can be used for the experimental characterization of water (or air) operated solar thermal collectors in accordance with the ISO 9806:2017 requirements, but it is also compatible with sensors with lower specifications. We present also its performance results when applied for the testing of a solar thermal collector.

Research on Evolution Characteristics of Shale Crack Based on Simultaneous Monitoring of Multi-Parameters

Meso-crack evolution mechanism of shale is a key factor affecting the mechanical properties of shale. In order to explore evolution laws of cracks in shale during loading, a meso-crack monitoring system, loading test equipment and an automatic ultrasonic data acquisition system were set up. On this basis, a set of experimental apparatus simultaneous monitoring multi-parameters of shale micro-crack was designed, and destruction experiments of shale samples with different bedding angles were carried out to find out evolution characteristics of cracks. The results show the following: (1) The designed apparatus can monitor ultrasonic, mechanical and video information simultaneously of crack evolution in the entire process of shale destruction under load to provide information for analyzing acoustic and mechanical characteristic responses of crack propagation at key time nodes. (2) With an increase in load, shale will undergo four stages of destruction: crack initiation, propagation, penetration and overall failure. In the course of these stages, acoustic characteristics and mechanical characteristics are in good agreement, which proves the validity of predicting rock mechanical parameters with acoustic data. (3) During the loading process of shale, the main amplitude of acoustic wave is more sensitive than mechanical parameters to the change of rock cracks. Research results have important theoretical reference value for evaluating wall stability of shale gas horizontal well with ultrasonic data.

Equivalent method for obtaining concrete age on the basis of electrical resistivity

Concrete age is the time since the moment water is added to the cement, and the age of concrete comprehensively reflects the physical properties of the concrete when curing under standard conditions. For concrete under nonstandard conditions, its physical properties are directly related to both its age and temperature history. The equivalent age of concrete is the time at which concrete under nonstandard conditions reaches the same state as concrete under standard conditions. Most equivalent methods, such as the Nurse-Saul function and the Arrhenius function, are based on a maturity index. However, the accuracy of these methods breaks down when the curing temperature range is wide. In this paper, the electrical resistivity of concrete is used as the index to determine the equivalent age of concrete. This method is based on the assumption that concrete with the same mixture proportions has the same electrical resistivity when the maturity of the concrete is the same, regardless of the curing history. The proposed method is advantageous because it can be performed in real time and is nondestructive. To constantly measure the electrical resistivity of concrete, an automatic data acquisition system is developed to monitor the electrical resistivity of concrete and reduce the error caused by polarization as much as possible. Then, a model for predicting the electrical resistivity of concrete under different curing temperatures is proposed to conveniently calculate the equivalent coefficient. Finally, the results calculated by the proposed equivalent method are compared with those of the standard method (Nurse-Saul equation), and the results of the proposed model are found to be more reasonable.

Irrigation control and image acquisition for rice cultivation in UAE desert soil

Precision water management and crop growth monitoring are essential where water is a scarce, especially in desert soils. The purpose of the study was to control the irrigation and real-time image acquisition for monitoring the rice cultivation inside the net house under the UAE desert soil. An automated data acquisition system was constructed, installed, and tested in the experimental site at Al-Foah, Al-Ain. Soil water content sensors were placed in the different depths of desert soils, and an automatic irrigation logic was implemented to maintain the average of 30% desired water content level in desert soils. The irrigation rate was controlled based on the sensor data and the on/off of the pump and valves. When the average soil water content percentage level exceeds 30%, the pump and solenoid valve automatically turned off and vice versa. A Raspberry Pi operating system was used to control the irrigation, and a Raspberry Pi camera system was used to capture the real-time images for monitoring the rice growth and development. A web server was developed to upload and display the sensor values and images using python programing language through the embedded Wi-Fi network service. The web-based monitoring system was allowed to monitor the rice field situation from anywhere and download data from the site. The existing irrigation technique would help to grow the rice in UAE desert soil environments.

Optimization of laser processing parameters through automated data acquisition and artificial neural networks

Finding the optimal parameters in laser processing applications can be time-consuming given the large parameter space and various sources of error. This problem is exacerbated by day-to-day variation in laser beam characteristics and a large variety of materials that need to be processed. The ideal laser processing system should be “smart,” meaning that it can sense changes in the environment, make proper adjustment, and predict parameters for new materials. As a step toward this goal, we propose a method to efficiently extract the areas of a large number of laser-induced damages in situ using an automated data acquisition system that can control laser parameters, motorized stage movement, image capturing/processing, and feature extraction. The damage areas are extracted and compared with direct measurements. Damage areas are fed into an artificial neural network (ANN) for prediction. Various ANN structures and training functions are tested to create the optimal ANN for prediction. ANN predictions were found to be capable enough to accurately model and optimize the laser processing parameters that were investigated. With the capability of collecting a large amount of usable data in a short period of time, this acquisition system can be used to train sophisticated ANNs for complicated tasks such as quality control and failure prediction.