Data Acquisition System Research Articles

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Assessment of bending waves in Torsion Hopkinson Bar experiments using Photon Doppler Velocimetry

A Photon Doppler Velocimetry system that measures the propagation of elastic shear waves in a Torsion Hopkinson Bar (THB) system is presented. The method uses multiple fiber optic probes located symmetrically on opposing sides of the apparatus bars, and provides data with high spatial (a laser irradiated spot size of 35μm) and temporal resolution that is ultimately limited by the data acquisition system and used electronic components. A series of validation experiments simulating the movement of the bar subjected to bending and misalignments demonstrated that this approach is effective in detecting and accounting for the bending waves. The THB experiment under non-ideal conditions, where a combination of shear and bending waves propagates in the system, conclusively confirmed that the disturbance in the acquired signals can be properly addressed with the proposed arrangement of the PDV probes. It was reflected in similar measurements of the component of tangential velocity to the strain gauges. This approach shown to be complementary to the conventional strain gauge technique, but can provide better precision and be more robust under loading and/or temperature conditions that may affect the reliability of strain gauge measurements.

CaloFlux: a tool to estimate fluxes in calorimeters at colliders

In high-granularity calorimetry, as proposed for detectors at future electron-positron Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and data acquisition systems. Estimating the data rate is a difficult task due to the very large number of channels, the complexity of the calorimeter system's geometry, and the intricacies of the readout system. The CaloFlux software package presented here eases this task by establishing the typical fluxes-deposited energy, number of cells above the electronics threshold, power consumption, and associated data-in rate histograms for selections of components, from fully simulated physics and background events by scaling with proper rates. The ILD calorimeter system is taken as a specific example, upon which different histograms are obtained for representative parts of the calorimeter and for various machine configurations. Examples of histograms are shown, along with details of the data used and the simulation.

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Experimental study on Start-Up and heat transfer characteristics in loop heat pipes with dual heat sources for battery thermal management system

Electric vehicles represent a breakthrough in sustainable transportation, significantly diminishing global emissions. The efficacy of lithium-ion batteries is significantly influenced by the operating temperature, with elevated temperatures potentially resulting in thermal runaway. A proficient battery thermal management system (TMS) must resolve this concern and uphold safe temperatures. Loop heat pipes (LHPs) offer passive cooling technology and have garnered the attention of researchers. Nonetheless, research on LHPs for battery thermal management systems is scarce, and prior investigations have concentrated on a singular heat source. A liquid heat pipe with a dual heat source evaporator has been engineered to simulate the battery arrangement of electric vehicles, which typically comprises multiple battery cells. This study seeks to examine the starting and heat transmission characteristics of LHP. Two battery simulators are fabricated from steel blocks equipped with cartridge heaters. A copper evaporator (121 × 56 × 20 mm) is equipped with a stainless-steel screen mesh capillary wick, and thermocouples are linked to a National Instruments data acquisition (NI DAQ) system for the LHP. Ethanol serves as the working fluid at a filling ratio of 60 %. The startup characteristics of the LHP are examined by altering the heat load, coolant flow rate, and coolant temperature. The experimental findings indicate that the LHP effectively initiates operation at heat loads ranging from 20 to 100 W. The LHP with multiple heat sources demonstrates reduced temperatures compared to a single heat source. The best performance of coolant flow rate and temperature for the quickest startup times are 1.5 L/min and 25 °C, respectively. The lowest thermal resistance achieved is 0.17 °C/W.

Ultra-short term wind power prediction method based on LightGBM and transformer

Abstract Accurate ultra-short-term wind power prediction is very important for the safe and stable operation of the power system. At present, most wind power forecasting systems use numerical weather forecast data and the data collected by the Supervisory Control And Data Acquisition (SCADA) system. However, these data have the characteristics of high data dimension, feature redundancy, and feature correlation. Therefore, in the process of ultra-short power prediction, it is necessary to select features with high power correlation and low cross-correlation between features. In this paper, a feature selection method for wind power prediction based on LightGBM is proposed to calculate the importance of all features and determine the preliminary selected features. Then, the maximum information coefficient is used to construct the correlation discriminant matrix, according to which the features with similar importance in a single screening are evaluated, and the input features with high similarity are discarded. In addition, in order to improve the accuracy of the system’s wind power prediction, this paper uses the effective features extracted by LightGBM to design an ultra-short-term power prediction method based on Transformer. The results show that the RMSE of ultra-short-term wind power prediction is 7.3%.

A Novel Method for Online Diagnostic Analysis of Partial Discharge in Instrument Transformers and Surge Arresters from the Correlation of HFCT and IEC Methods

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Development of a charged particle-[formula omitted] coincidence system for nuclear structure and reaction studies at TIFR

A new detector system for measurements of charged particles and γ-rays in coincidence has been developed at TIFR Mumbai, by coupling an annular double-sided segmented Si detector with an array of Compton-suppressed clover HPGe detectors. Digital data acquisition system has been configured for the setup, which has been calibrated and tested using the 229Th radioactive source that emits α-particles along with γ-rays. The functionality and performance of the coincidence apparatus for Doppler correction of γ-transitions emitted during the in-flight de-excitation of the projectile and the target in the 30Si + 197Au Coulomb excitation experiment have been demonstrated.

FPGA-based measurements of the relative arrival time of a high-repetition rate, quasi-cw fourth generation light source.

In this paper, we demonstrate the successful implementation of reconfigurable field-programmable gate array technology into a pulse-resolved data acquisition system to achieve a femtosecond temporal resolution in ultrafast pump-probe experiments in real-time at large scale facilities. As proof of concept, electro-optic sampling of terahertz waveforms radiated by a superradiant emitter of a quasi-cw accelerator operating at a 50kHz repetition rate and probed by an external laser system is performed. Options for up-scaling the developed technique to a MHz range of repetition rates are discussed.

Test Stand for Microjet Engine Prototypes

To investigate the functionality and performance of a prototype microjet engine, we constructed a versatile test stand tailored to the specifications of a 400 N prototype. This test stand facilitated a comprehensive study by enabling real-time recording of 45 essential parameters for analysis, encompassing temperatures, pressures, speed, fuel flow, thrust, vibration, and various other monitored metrics. All parameters and control elements were seamlessly integrated via a data acquisition and control system, utilizing a compactDAQ (Data Acquisition) system from National Instruments and a custom Virtual Instrument programmed with graphical language. The test stand offers both manual and automated operation modes, with the flexibility for hybrid operation. For instance, following the idle regime, manual control using a potentiometer can seamlessly transition from automated control via a proportional control (P control) mechanism. Before the experimental campaign, rigorous verification and validation tests were conducted to ensure the reliability and accuracy of the setup. The experimental campaign comprised a series of manual tests focusing on the fuel system and automated tests covering starting, idle, working, and stopping regimes. This structured approach allowed for a comprehensive evaluation across different operational scenarios, providing insights into the engine’s behavior and performance under varying conditions.

Wind turbine foundation ring fatigue reliability analysis under wind-load using SCADA system

Under the action of wind load, the foundation ring of the fan will generate stress concentration and alternating stress, leading to fatigue failure. Based on the average wind speed obtained from the supervisory control and data acquisition (SCADA) system, the orthogonal expansion method of random pulsating wind field and the number theory point selection method are used to calculate and simulate the corresponding pulsating wind speed time series, and then calculate the wind speed time series and wind load time series. Based on the ABAQUS finite element software, a model of a 5 MW wind turbine is established. The modal analysis is used to obtain the modal vibration pattern and the intrinsic frequency to verify the reasonableness of the structural modeling. Subsequently, the wind load time series is applied to the wind turbine model to obtain the stress time series using instantaneous modal dynamic analysis (Modal dynamics). The stress time series at the location of maximum stress concentration in the foundation ring is extracted, and the stress time series with the same stress amplitude is obtained through conversion, which has Wiener characteristics. After obtaining the stress amplitude using the rainflow counting method, the fatigue reliability is calculated based on the structural fatigue reliability analysis method considering the threshold crossing duration of the random process. This research holds reference significance for the calculation of fatigue reliability under approximate working conditions.

Analysis of Factors Influencing Traffic Sign Recognition Based on Multi-Sensor Perception Data

When traffic signs are blocked or faded, the ability of automated vehicles (AVs) to perceive and recognize them is lost or reduced. Previous research has improved and optimized the recognition algorithm but has not systematically analyzed the environmental and design factors that can lead to the AV’s failure to perceive and recognize traffic signs. This study uses the multiple sensors in the equipped vehicles of the Tongji University Road and Traffic Holographic Data Acquisition System to perceive and recognize traffic signs in six crash hotspots in Shanghai, China. Failure to recognize traffic signs was analyzed considering the influence of four sets of factors: traffic sign design, vehicle characteristics, environmental factors, and road design. A feature importance analysis was conducted utilizing the machine learning algorithm model CatBoost. Shapley additive explanation values were used to analyze the positive and negative relationships as well as the contribution rates of each influencing factor. The results revealed that the major factors influencing sign perception and recognition were positioning of traffic sign, lighting, blockage, cleanliness, fading, and reflectivity. Cleanliness had the most significant impact on the perception and recognition of traffic signs compared with the other influencing factors, and it also showed significant influencing interaction with the other factors. Fading was the second most important factor. The results of this study can guide designers of traffic signs to promote safety in mixed traffic environments that include both AVs and human-driven vehicles.

System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.

ATLAS upgrade for the HL-LHC

While the on-going Run-3 data-taking campaign will provide twice the integrated proton–proton luminosity currently available at the LHC, most of the data expected for the full LHC physics program will only be delivered during the HL-LHC phase. For this, the LHC will undergo an ambitious upgrade program to be able to deliver an instantaneous luminosity of 7.5⋅1034cm−2s−1, allowing the collection of more than 3 ab−1 of data at s=13.6(14)TeV. This unprecedented data sample will allow the ATLAS experiment to perform several precision measurements to constrain the Standard Model (SM) in yet unexplored regions of phase space, in particular in the Higgs sector, only accessible at the LHC. To benefit from such a rich data sample, it is fundamental to upgrade the detector to cope with the challenging experimental conditions that include radiation doses 10 times higher than what ATLAS experienced since its construction and increase in the number of interactions per bunch crossing from the current 50-60 to 200. The upgrade of the ATLAS detector comprises a completely new all-silicon tracker with extended rapidity coverage that will replace the current inner tracker detector; a redesigned trigger and data acquisition system for the calorimeters and muon systems, allowing the implementation of a readout system that can operate complex trigger algorithms at 40 MHz. Finally, a new subsystem called High Granularity Timing Detector will aid the track-vertex association in the forward region by providing timing information to the reconstructed tracks. An important ingredient, relevant to almost all measurements, is the precise determination of the delivered luminosity with systematic uncertainties below the percent level. This challenging task will be achieved by collecting the information from several detector systems using different and complementary techniques. This document will describe the ongoing ATLAS detector upgrade status and the main results obtained with the prototypes, giving a synthetic view of the whole upgrade project.

A Mixed Approach for Clock Synchronization in Distributed Data Acquisition Systems.

Proper timing synchronization is important when data from sensors are acquired by different devices. This paper proposes a simple but effective solution for System on Chip (SoC) architectures that integrates a general-purpose Field Programmable Gate Array (FPGA) with a CPU. The proposed approach relies on a network synchronization protocol implemented in software, such as Network Time Protocol (NTP) or Precision Time Protocol (PTP), and uses the FPGA to generate a clock reference that is maintained in step with the synchronized system clock. The clock generated by the FPGA is obtained from the FPGA oscillator via appropriate fractional clock division. Clock drift is avoided via a software program that periodically compares the FPGA and the system counters, respectively, and adjusts the fractional clock divider in order to slightly adjust the FPGA clock frequency using a Proportional Integral controller. A specific implementation is presented on the RedPitaya platform, generating a 1 MHz clock in step with the NTP synchronized system clock. The presented system has been used in a distributed data acquisition system for fast transient recording in the neutral beam test facility for the ITER nuclear fusion experiment.

Image De-noising Based on WMF Technique for Electrical Trees Structure in High Voltage Cable Insulation

Electrical treeing is a common problem during the pre-breakdown phenomenon in solid insulations due to the damage caused by Partial Discharge (PD) that progresses through stressed insulation via chemical degradation, which resembles the shape of a tree root. This resulted in a decrease in performance through degrading the insulation, which became a serious problem while dealing with electrical equipment. Hence, a deep understanding of electrical tree structure is vital to improving the quality of solid insulations. Ergo, optical microscopy is primarily used to examine tree structures, shapes, and fractal dimensions to reconstruct electrical tree structures for morphological study. However, optical microscopy images are frequently degraded by noise from readout procedures or image data acquisition systems, noise caused by occlusion, illumination, non-uniform intensity, destroying potential tree pixels, and a critical loss of information about the electrical tree structures. Therefore, this research proposed the Wiener Median Fusion (WMF) filter for electrical tree study. The performance of the WMF de-noising technique improves the image quality for the precise portrayal of the electrical tree structure based on thresholding segmentation algorithm analysis in terms of accuracy, sensitivity, and false positive rate. Based on the analysis of the thresholding segmentation algorithm, Otsu's thresholding exhibits the highest result compared to Niblack. The Otsu's overall percentage in terms of accuracy is 80.2934%, the sensitivity is 99.1513%, and the false positive rate is 82.6265%.

Adaptation of a Differential Scanning Calorimeter for Simultaneous Electromagnetic Measurements.

Although much information can be gained about thermally induced microstructural changes in metals through the measurement of their thermophysical properties using a differential scanning calorimeter (DSC), due to competing influences on the signal, not all microstructural changes can be fully characterised this way. For example, accurate characterisation of recrystallisation, tempering, and changes in retained delta ferrite in alloyed steels becomes complex due to additional signal changes due to the Curie point, oxidation, and the rate (and therefore the magnitude) of transformation. However, these types of microstructural changes have been shown to invoke strong magnetic and electromagnetic (EM) responses; therefore, simultaneous EM measurements can provide additional complementary data which can help to emphasise or deconvolute these complex signals and develop a more complete understanding of certain metallurgical phenomena. This paper discusses how a DSC machine has been modified to incorporate an EM sensor consisting of two copper coils printed onto either side of a ceramic substrate, with one coil acting as a transmitter and the other as a receiver. The coil is interfaced with a custom-built data acquisition system, which provides current to the transmit coil, records signals from the receive coil, and is controlled by a graphical user interface which allows the user to select multiple excitation frequencies. The equipment has a useable frequency range of approximately 1-100 kHz and outputs phase and magnitude readings at a rate of approximately 50 samples per second. Simultaneous DSC-EM measurements were performed on a nickel sample up to a temperature of 600 °C, with the reversable ferromagnetic to paramagnetic transition in the nickel sample invoking a clear EM response. The results show that the combined DSC-EM apparatus has the potential to provide a powerful tool for the analysis of thermally induced microstructural changes in metals, feeding into research on steel production, development of magnetic and conductive materials, and many more areas.

Application of photothermal beam deflection spectrometry for non-destructive evaluation of advanced materials: a state-of-the-art review

Abstract The progress made in lasers and data acquisition systems has paved the way for innovative non-destructive evaluation methods based on the photothermal phenomenon. Beam deflection spectrometry (BDS) is a photothermal spectroscopic technique that offers ultra-sensitivity, high signal-to-noise ratios, and reduced sample preparation requirements. These advantages provide precise assessment of advanced material (AM) properties. This review presents a comprehensive in-depth analysis, thus helping researchers to understand the potential and future perspectives of BDS along with the theory, instrumentation, and application supported by the literature data. The objective of this review is also to present the possibilities of BDS in the characterization of AMs, including organic, inorganic, and hybrid organic–inorganic materials in the form of thin films, coatings, composites, and nanomaterials.

An efficient and automatic method based on monocular camera and GNSS for collecting and updating geographical coordinates of mileage pile in highway digital twin map

Abstract The high-precision positioning of mileage piles on a digital map ensures accurate data for high-speed event releases, toll audits, and road condition monitoring. This paper introduces an efficient and automatic system for positioning highway mileage piles. The self-developed data acquisition system collects mileage pile images and road trajectory. Considering the limited variety of mileage piles, a Simplified-YOLOv5m (S-YOLOv5m) is proposed. Then a high-precision character detection network of S-YOLOv5m is proposed to enhance feature extraction and improve the accuracy of character detection on mileage piles. Thirdly, the end-to-end monocular distance measurement combines the target detection with the distance estimation, enabling simultaneous object detection and distance measurement. Fourthly, by combining with the geographical coordinates of the acquisition point, the direct solution to geodetic problems is applied to calculate the spatial coordinates of the mileage piles. Finally, the missing mileage piles are compensated for using the road curve and equal distance interpolation. The complete mileage piles and geographical information list of the inspection trajectory are output. Through a series of verification tests, the average positioning error of the mileage pile system is 1.265 m. The contribution of the automatic positioning system of mileage piles is to construct the relationship between mileage piles and geographical coordinates, match mileage piles with digital maps to realize the full-featured, real-scene, and high-dynamic management of road attributes.