Data Acquisition System Research Articles

Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis.

STUDII PRIVIND DEZVOLTAREA UNUI DIAGNOSTIC DE MENTENANȚĂ LA CUTIILE DE VITEZE A TRACTOARELOR AGRICOLE

Faults and failures analyses consist of a very varied field of technique, so concepts and reliability indicators help us to determine the types of faults easier, their appearance in functioning and mathematical relations for determining the types of failures. Regarding to, the test stands help us to identify accelerated of noises, vibration and serious faults that occur in use and can lead to disposal of equipment, helping engineers quickly find the optimal solutions for most cases. Current methods used to determine agricultural tractors gearboxes reliability are those in open energy flow, they better simulate the real working conditions. We design a gearbox test stand for a rapid noise and vibration reliability diagnoses and tests. Considering the gearbox work conditions and its random possible failures, the stand will be equipped with transducers and a data acquisition system for data collection.

A Sensor-Based Data Acquisition System for Soil Parameters to Determine Suitable Crops

Soil parameters monitoring is significant in sustainable crop and food production. The standard strategy of soil parameters monitoring in developing and underdeveloped nations uses manual labor, resulting in wrong decisions in soil management. Inaccurate measurements due to sensor miscalibration or low sensor quality can lead to incorrect soil management decisions and negatively impact crop yield and environmental sustainability. Due to the mentioned challenges, this work aims to develop a Sensor-based Data Acquisition System for Soil Parameters that will enable users to observe various soil parameters like temperature, humidity, water level and soil pH. The system was developed using the combination of hardware and software components. The hardware component comprises of sensory and processing parts. The study calibrates sensors using known pH, moisture, and temperature values for specific crops to grow in Nigeria. The system will aid farmers in determining suitable crops for their farmland and increasing crop yield. The system collects data through a network of sensors installed in the soil and wirelessly transmits the data to a cloud-based server. The collected data is then analyzed and visualized in through a web-based dashboard, providing farmers with information about the state of their soil. The performance evaluation of the system was carried out using response time and accuracy. The average response time of the system was 4 seconds, and the percentage error for temperature and humidity readings when compared to weather forecast readings were 8.20% and 5.08%, respectively. The results show that the proposed system can provide accurate and reliable measurements of soil parameters and can be easily deployed and operated by small-scale farmers. Using this system can result in improved crop yields, reduced wastage, and better overall efficiency in agricultural operations

A Comprehensive Study of IoT Enabled Smart Grid

Systems that are used for monitoring and controlling are known as supervisory control and data acquisition systems. Not the entire control permissions, but rather the supervisory level is the primary focus of this. Several different kinds of sensors are being utilized in order to collect data in real-time. What is the Internet of Things (IoT)? It is a three-dimensional connectivity that can be used for anything, at any time, in any location. The comparison between the SCADA system and the Internet of Things is carried out in this study. In addition, this section of the study focused on the benefits of the Internet of Things (IoT) and offered some suggestions for integrating the IoT with the SCADA system.

Design of industrial equipment data acquisition system based on ZYNQ

Abstract In this paper, an industrial equipment data acquisition system based on a ZYNQ chip is constructed using an AD7607 data acquisition module containing a symmetric layout. Next, four modules of signal output, data writing, caching and communication are designed. Focusing on the design of the signal processing system after analog-to-digital conversion, the D-S evidence processing algorithm is utilized to judge the obtained data independently and fuse the data based on its D-S combination rule. Finally, the industrial equipment acquisition system design is tested, and the test includes system performance, accuracy, and stability. The frequency peaks obtained by the system in this paper are all 1000.000Hz, as shown by the results. The signal peak value of 95.18mV, the NI acquisition card signal peak value of 98.33mV, and the error is only 0.89%. The signal-to-noise ratio of the sampled signal is 50.12dB, and the effective number of bits reaches 10.40 bits. The performance of the system and the accuracy and stability of data acquisition on industrial equipment are verified.

Design of a multidimensional data collection system based on DRS4 scheme

A high-speed data acquisition system based on DRS4 waveform digitization is designed to meet the requirements of high sampling rate, multi-parameter measurements, and long-distance transmission in some particle physics experiments. The system consists of a waveform digitization front-end board, a high-speed data transmission board, and an upper computer software. Test results show that the system has a high data transmission rate and a low BER and that the collected data can reconstruct and restore the input signal waveform well. In addition, we have analyzed the single-electrode dual-mode sensor, excitation signal generation module, resistor/capacitor conversion circuit, and phase-sensitive demodulation unit in the system. The experiments show that the system is stable in operation, flexible in use, and can significantly extend the measurement range.

Advanced System-on-Chip Field-Programmable-Gate-Array-Powered Data Acquisition System for Pixel Detectors.

Particle detector systems require data acquisition systems (DAQs) as their back-end. This paper presents a new edge-computing DAQ that is capable of handling multiple pixel detectors simultaneously and was designed for particle-tracking experiments. The system was designed for the ROC4SENS readout chip, but its control logic can be adapted for other pixel detectors. The DAQ was based on a system-on-chip FPGA (SoC FPGA), which includes an embedded microprocessor running a fully functional Linux system. An application using a client-server architecture was developed to facilitate remote control and data visualization. The comprehensive DAQ is very compact, thus reducing the typical hardware load in particle tracking experiments, especially during the obligatory characterization of particle telescopes.

Green hydrogen production using bifacial solar photovoltaics integrated with high-albedo roof coating & micro-inverter

Green hydrogen is a key factor for the decarbonization of the energy sector and to foster the clean energy transition but has the highest cost when compared to grey and blue hydrogen. This is due to the high cost of the generation of energy from renewable sources and the efficiency of the water electrolyzer. Future innovative energy technologies are needed to boost the power production from solar PV systems, reduce the cost of renewable electricity and enhance hydrogen production from electrolyzers. The main objective of this study is to investigate green production enhancement using a bifacial solar PV system integrated with cool roof technology (high-albedo roof coatings). The best-reflecting surface needs to be determined to maximize the power output from the bifacial solar PV system and the green hydrogen production. The originality and novelty of this study lie in the fact that it integrates innovative technologies such as bifacial solar PV and building cool roofs in order to boost the power generation output and green hydrogen production. The experimental set-up is composed of bifacial solar PV, a building roof with high solar reflective material, a microinverter, an electrolyzer, a metal hydride hydrogen tank, and an integrated data acquisition system. The results show that the green, grey, and white reflecting surfaces exhibit the albedo of 16.76 %, 17.87 % and 24.45 % respectively resulting in 17.26 %, 19.72 % and 28.47 % enhancement in power production by bPV as compared to reference mPV. Further, whiteboard, patterned wallpaper and cool roof paint coating are assessed and have shown 24.09 %, 29.66 % and 30.27 % rear side irradiance on the bPV as compared to the front side, resulting in 28.10 %, 31.30 % and 33.02 % increase in energy generation. The results conclude that cool roof paint outperforms the others and enhances the power output of bPV by 33.02 % resulting in 30.26 g of H2 production as compared to 22.75 g by mPV under the same conditions. Hence, cool roof paint improves the energy production of bPV solar resulting in high production of H2.

Experimental study of temperature distribution and development of a power monitoring channel for the IPR-R1 TRIGA® research nuclear reactor

The natural convection and forced cooling experiments were conducted to confirm the cooling capability in the IPR-R1 TRIGA® nuclear research reactor. Temperature measurements were carried out in several locations such as: at the coolant inlet and outlet in the core channels, at various pool heights, and at the centre of an instrumented fuel element. The temperature monitored by the instrumented fuel element was performed with it positioned in all the core rings and at different operating powers. A new way of measuring the power released by nuclear fissions in the core using thermal processes has been developed. To monitor temperatures and power, a Data Acquisition System (DAS) was used. The information acquired during the experiments provides an exceptional representation of the IPR-R1 TRIGA® reactor’s thermal performance. The experiments confirm the efficiency of natural circulation in removing the heat produced in the reactor core by the nuclear fission.

FPGA-Based Implementation of SCADA System for Fuel Management

With the emergence of Digital Processors and Logic Design technologies, like FPGAs, the trend of using IC-based plug-and-play modules has been minimized. The focus is commercial off-shelf designing and developing modules and sub-modules on new FPGA technology. Some of the critical factors that make FPGA more suitable include enormous computational power, capabilities to perform logic operations, high-speed clocks, fast memory, and various built-in primitives for computation-intensive arithmetic operations. FPGA becomes suitable for implementing data capturing and processing systems with these characteristics. This paper is about designing and developing an FPGA-based Supervisory Control and Data Acquisition (SCADA) System to monitor and control the fuel level transition between two tanks that generally are designed using PLC. PLC-based SCADA systems are easy to implement, but when discussing performance, PLC cannot replace FPGA. The FPGA chosen is Spartan-3 due to its low cost and meeting the industrial temperature needs. At the same time, it may be translated into any advanced FPGA, producing fewer sources and power consumption benefits. The simulator is Xilinx 14.7 ISE, along with SDK. The implemented system provides the inherent amenities of SCADA along with the benefits of high speed, more accuracy, reduced and predictable delay, and a purely digitized system facilitated by the FPGA.

FiO2 prediction formula during low flow oxygen therapy in an adult model: a bench study.

During low-flow oxygen therapy, the true value of inspired oxygen fraction (FiO2) is generally unknown. Knowledge of delivered FiO2 values may be useful as well as to adjust oxygen therapy, as well as to predict patient deterioration. This study proposes a New FiO2 Prediction Formula (NFiO2) for low-flow oxygenation and compares its predictive value to precedent formulas. In a bench study, the O2 Flow rate was delivered through a T-piece connected to a dual-compartment artificial lung controlled by a mechanical ventilator. To test the NFiO2 formula, a set of ventilatory parameters were tested: Tidal Volume was set from 400 to 600 ml, Respiratory Rate (RR) was set from 18 to 30 CPM, Ti/Ttot was set at 0.33 and 0.25, and O2 flow rates from 3 to 10L/min. A data acquisition system measured all parameters. To quantify the accuracy of the NFiO2 compared to other FiO2 prediction formulas, Bland and Altman agreement analyses were performed. To make use of the Duprez Formula 2018 in clinical practice, we simplified the formula to estimate the FiO2 during oxygenation at low flow. This NFiO2 formula makes use of only O2 Flow Rate and RR. Bias and limits of agreement between predicted FiO2 and benchtop FiO2 highlighted consistent differences between different FiO2 prediction formulas. The NFiO2 and the Duprez Formula 2018 seemed to be the most accurate formulas, followed by the Vincent Formula, and lastly the Shapiro Formula. A New FiO2 Prediction Formula was developed using clinical readily available variables (RR and O2 Flow rate) which showed good accuracy in predicting FiO2 during oxygenation at low flow.

Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.

Preliminary Design of a Climate Controlled Environmental Test and Measurement

Climate chambers play an important role in the design and testing process. Several different chambers have been built over the years, specialising in different areas. It means there is a wide choice on the market and as a consequence, parameters necessary for us limit when choosing the ideal test chamber. Our instruments might be subjected to several environmental effects which require a lot of time and effort to be tested. An example is the Mars rover. NASA's Perseverance Mars rover was equipped with 2 pieces of Li-ion battery packs and the old solar charging was replaced by RTG (Radioisotope Thermoelectric Generator) thus a generator feeds the electricity consumers of the rover. Temperature on the surface of Mars varies from -150 C° to 20 C°. In addition, there are huge storms at regular interval lasting for month sometimes. These storms cause great difficulties because, on the one hand, the main power source on older types was the solar panels, and storms can cover them with Martian dust, limiting or eliminating their power supply. On the other hand, large sandstorms can completely cover the rover, and once the sandstorm is over, the Mars rover has to free itself from its trap. In addition, there were many other factors that engineers had to consider in the design. It is a well-known fact that we are not able to consider all the possible environmental conditions in design, and it is also known that some measurements may be inaccurate, so the prototypes produced have to be tested on the ground. The ground tests must ensure that the operating conditions on Mars are maintained. Special test chambers have been developed for this purpose. [1] The aim of the authors is to design and build a prototype of a climate chamber of their own design. An important component of the chamber is a measurement data acquisition system, which allows the collection of measured data and their preliminary processing.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Experimental study of decompression wave characteristics and steel pipe applicability of supercritical CO2 pipeline

High-strength pipeline steel is the optimal choice for continuously transporting large amounts of carbon dioxide (CO2) in the future Carbon capture, utilization and storage (CCUS) industrial chain, and supercritical CO2 (S–CO2) is the ideal phase state from an emitter to an onshore storage site. In CCUS projects, it is very indispensable to carry out the suitability of high steel-grade pipelines to ensure the safe operation of pipelines. It is considered a catastrophic incident for creatures and equipment once pipeline failure when fractures or propagation occurs. In this paper, the fracture propagation velocity of X70, X80, and X90 pipeline materials is calculated base on the high strength line pipe (HLP). Meanwhile, three sets of full-bore fracture (FBR) experiments of S–CO2 pipelines were conducted with similar initial status on an industrial-scale setup, and the decompression wave velocity was obtained with the data acquisition system. The result shows that the initial decompression wave velocity of S–CO2 is about 270 m/s, and the pressure plateau is between 6.8 and 7.3 MPa, corresponding to the decompression wave velocity, which is about in the range of 100–220 m/s. After that, the decompression wave velocity prediction model was established based on the homogeneous equilibrium method (HEM) and the equation of state (Eos), and the prediction results are more conservative than the experimental results. To ensure that the pipeline will not ductilely fracture in the event of a leakage or rupture, X70, X80 and X90 grade steel pipeline design wall thickness and minimum arrest toughness values are determined based on the two-curve method (TCM), respectively. Furthermore, a CO2 pipeline design model is developed based on the experimental data and decompression wave calculation method. The significant experimental data could be used for safety assessment and theoretical model validation in the CO2 transport pipeline and is useful in developing better fracture control models.

A real time condition based sustainable maintenance method for milling process

The manufacturing industry must reduce energy consumption during the machining process to comply with energy demand and usage restrictions. Previous research has employed various methods to take a optimization plan in machining process. However, these studies mostly rely on static machining conditions, without paying much attention for the condition variation, which may lead to a coarse energy efficiency optimization in machining process. Hence, this study introduces a real time condition based sustainable maintenance method for machining process. Firstly, a real-time data acquisition system is employed to collect and send data to the developed tool condition prediction model using deep learning. Then, the physical energy prediction model is employed to reflect the real time energy consumption behaviors based on the real time tool condition. Subsequently, a similarity analysis is employed to decide whether a new optimization is required. And, once it is required, an optimization using NSGA-II and TOPSIS is conducted. Experiments were conducted on a milling machine and compared with traditional methods, and verified using triple bottom line. Results indicated that the proposed method can reduce the energy consumption by 2.51% and 7.01%, and it can maintain a qualified product during the machining process.

Scalable Data Concentrator with Baseline Interconnection Network for Triggerless Data Acquisition Systems

Triggerless data acquisition systems (DAQs) require that the data stream is transmitted from multiple links and into the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g., PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO) for high-speed data routing. A thorough analysis of the network’s operation enabled increased scalability compared to the previously published concentrator based on an 8 × 8 network. The solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were performed for four and five layers (16 and 32 inputs). The FPGA implementation and tests in the actual hardware were successfully performed for 16 inputs. The pipeline registers may be added in each layer independently, shortening the critical path and increasing the maximum acceptable clock frequency.

Hybrid state-estimation in combined heat and electric network using SCADA and AMI measurements

State-estimation plays a vital role to monitor, observe and understand the combined heat and electric network. In this paper, a hybrid framework is presented to accurately estimate the system states of electric distribution network and heat network, using the limited non-redundant measurements obtained from supervisory control and data acquisition and advanced metering infrastructure systems. The presented hybrid framework involves two steps, namely, the state-forecasting and the state-estimation. The state-forecasting uses a deep neural network to forecast the system states at every fifteen minutes interval, while these forecasted states are further used by the hybrid estimator, which uses a robust extended Kalman filter to estimate the system states with help of both datasets corresponding to supervisory control and data acquisition and advanced metering infrastructure systems, at hourly interval. The proposed framework does not completely rely on the system model at different instants. The effectiveness of the method is validated through thorough comparisons with simulation studies carried out using the Barry Island test system, United Kingdom. Satisfactory performance is observed even with the presence of bad data in the measurements.

Arduino microcontroller based real-time monitoring of haemodialysis process for patients with kidney disease

The efficient and precise monitoring of the haemodialysis process is critical for the well-being of patients suffering from end-stage renal disease (ESRD). In this work, a comprehensive study on the utilization of Arduino microcontroller-based systems for real-time monitoring, data acquisition and control of the haemodialysis process is presented. Arduino instrumentation unit was developed for an in vitro Haemodialysis (HD) system using Atmega 328 and Arduino nano microcontrollers. Haemodialysis process was conducted using in vitro haemodialysis system. Temperature probes (model DS18B20), conductivity probes (model EC-PPM-TDS) and weight sensor (load cell type HX711, 10 kg max stain gauge type) were used for real-time measuring and monitoring blood and dialysate temperatures, conductivities, and blood weight respectively. Simulink interface embedded in MATLAB software R.2019 was used as microcontroller software interface. In vitro HD result of the real-time monitoring indicated that initial and final measurement for blood temperatures gave 32.8 – 36.5 °C at set dialysate temperature 37 °C respectively, blood and dialysate conductivity 2 - 3.5 mS/cm and intradialytic blood weight 187.5 g – 147.5 g. This paper offers valuable insights into the implementation of Arduino microcontrollers for real-time monitoring of the haemodialysis process, with a strong emphasis on patient safety, data generation and data security. The research presented contributes to the advancement of technology in the field of nephrology and has the potential to significantly impact the quality of life for ESRD patients undergoing haemodialysis.

Ultrahigh-Mass Resolution Mass Spectrometry Imaging with an Orbitrap Externally Coupled to a High-Performance Data Acquisition System.

Matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) is a powerful analytical tool that enables molecular sample analysis while simultaneously providing the spatial context of hundreds or even thousands of analytes. However, because of the lack of a separation step prior to ionization and the immense diversity of biomolecules, such as lipids, including numerous isobaric species, the coupling of ultrahigh mass resolution (UHR) with MSI presents one way in which this complexity can be resolved at the spectrum level. Until now, UHR MSI platforms have been restricted to Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers. Here, we demonstrate the capabilities of an Orbitrap-based UHR MSI platform to reach over 1,000,000 mass resolution in a lipid mass range (600-950 Da). Externally coupling the Orbitrap Q Exactive HF with the high-performance data acquisition system FTMS Booster X2 provided access to the unreduced data in the form of full-profile absorption-mode FT mass spectra. In addition, it allowed us to increase the time-domain transient length from 0.5 to 10 s, providing improvement in the mass resolution, signal-to-noise ratio, and mass accuracy. The resulting UHR performance generates high-quality MALDI MSI images and simplifies the identification of lipids. Collectively, these improvements resulted in a 1.5-fold increase in annotations, demonstrating the advantages of this UHR imaging platform for spatial lipidomics using MALDI-MSI.