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Metallic magnetic calorimeters are a class of low-temperature particle detectors based on calorimetry，utilizing metallic paramagnetic temperature sensors to convert the temperature rise of an absorber upon the absorption of incident particle energy into a change in magnetic flux，which detected by a superconducting quantum interference device (SQUID). This process enables high-resolution measurements of radiation with exceptional precision. However，metallic magnetic calorimeter signals are inherently weak and highly susceptible to noise，making the extraction of pulse amplitude a significant challenge. Digital signal processing (DSP) plays a crucial role in the data processing of metallic magnetic calorimeter by employing digital filtering techniques to shape the signals and enhance energy resolution. This paper introduces a data acquisition system for metallic magnetic calorimeter based on a field-programmable gate array (FPGA)，designed for high-speed signal acquisition，real-time control，signal processing，and display of spectra through an upper computer. Testing of the system yielded successful acquisition of both Co-60 spectra data and noise spectrum of metallic magnetic calorimeter. The linear correlation coefficient of R2 reached 0.9781，demonstrates the system’s effective signal acquisition capability. In offline mode，the shaping effects and energy resolution enhancement of traditional nuclear detector signals，simulated，and actual metallic magnetic calorimeter signals were compared using three filtering methods: trapezoidal，cusp，and gaussian filtering. The cusp filter proved to be the most effective，significantly improving the energy resolution of high-purity germanium spectra from 0.598 keV to 0.536 keV. This enhancement validates the correctness of digital signal processing and provides a reliable basis for the selection of digital filtering methods for metallic magnetic calorimeter. In summary，our research presents a comprehensive approach to metallic magnetic calorimeter signal acquisition and processing，highlighting the potential of digital signal processing to overcome the problem of the extraction of weak signals submerged in strong noise. The FPGA-based data acquisition system developed in this study offers an efficient solution for high-resolution radiation measurements.

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation.

Owing to the continuous usage of centrifugal pump machines (CPM), health-monitoring systems are important for improving the hydraulic machine industry. While operating the CPM, supervision is carried out by either the operator or the online health-monitoring system. The Health Monitoring System (HMS) helps to understand the conditions of the pump and maintenance prospectus. For breakdown and accidents in the CPM industry, the prime reasons are impeller failure, bearing failure, cavitation, and shaft misalignment; therefore, it is necessary to supervise the parameters to provide a better life to the plant and enhance its efficiency using Data Acquisition (DAQ). The independent features and dependent features are selected and experimentally investigated to examine the characteristics of the CPM plant. The feature selection and extraction process should be compatible with the DAQ system which will help to reduce the curse of data biases and data preparation. Based on that three different DAQ systems based on Arduino, Raspberry Pi, and Dewsoft FFT are used in the experiments and compared based on 5-Point summary, Standard Deviation, cost analysis, flexibility, and maintenance. This paper presents the various techniques and procedures for acquiring the data from CPM using all three DAQ systems and selecting one of them through a statistical approach.

IntroductionIndoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory.MethodsA linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction.Results and discussionThe performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

A novel data acquisition system for condition monitoring and predictive maintenance of mechanical parts, machinery, and industrial plants is presented. Current commercial solutions rely on an analog architecture and a star topology, in which all transducers are connected to a centralized acquisition unit. Usually this requires long shielded cables, which are sensitive to electromagnetic disturbances, always present in industrial environments. The proposed solution makes use of a digital bus implemented on an Unshielded Twisted Pair to connect one or more Acquisition Nodes to a data storage system (e.g., a laptop or an industrial computer). The wiring is simplified, cabling cost is reduced, high disturbance rejection is obtained, at the same time ensuring synchronization between all signals, mandatory for the computation of the most advanced diagnostic metrics. The performance and effectiveness of the developed system are proved in comparison with a top-quality, laboratory-grade commercial solution. A 10-days experiment was performed on a radial bearing mounted on a bearing test bench, by employing both systems side-by-side. Early-stage damage identification will be demonstrated with the described solution, despite costing a fraction and offering numerous advantages for industrial applications with respect to products currently available on the market.

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded.

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

This work presents the instrumentation, modeling, and parameterization of an electric vehicle used for public transport. The aim is to characterize the mathematical model for its application in control systems design. A system identification technique based on a gray box approach is used to estimate specific parameters of the longitudinal model that cannot be measured directly. For this purpose, a data acquisition system was designed using high-amperage current sensors and an Odroid XU4 embedded system that records the vehicle’s input current, displacement, and velocity. Additionally, a semi-automatic acceleration system was developed to introduce a pseudo-random binary-type acceleration signal to excite all possible vehicle frequencies to perform the parameter identification.

The objective of this study was to assess the effectiveness and practicability of an activity index combining acceleration and location data for automated estrus detection in dairy cows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity records of each cow. The maximum-minimum distance clustering (MMDC) method was used to divide hourly activity data into low, medium, high, and intensity level groups. The weighted sum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation in hourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The study showed that the power consumption and communication effects of the neck tags were acceptable for indoor-housing conditions. For the two consecutive time periods, the activity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision, 100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. On the basis of these results, it can be concluded that the combination of acceleration and location in the activity index can promote estrus detection in dairy cows.

Mechanical action can damage physical objects, such as building structures. The transfer of energy to the ground through the falling mass, forming a crater, can cause kinematic forces in the soil medium. An effect of these forces, connected with the technology of geotechnical machines, is vibrations of the ground and building structures. When finding the energy impact on the object is difficult or even impossible, vibration monitoring and indirect estimation of the negative energy impact effects is possible. Commercial off-the-shelf solutions exist for vibration monitoring and recording, but they are dedicated to one application type and are relatively expensive. This paper presents a proprietary system for monitoring and recording vibrations of building objects transmitted through the ground. This allows for local registration and remote administration and presentation of data via a computer network. Vibrations can be analysed according to the requirements of developers and clients. The developed system shows that creating an effective approach for collecting data on building vibrations using components from different suppliers is possible. The waveforms collected using the system confirm the effectiveness and flexibility of the presented solution, providing a possibility of using the results to decide whether the vibrations are within the standard limits.

In terms of manned aircraft, pilots usually detect icing conditions by visual cues or by means of ice detector systems. If one of these cues is seen by the crew or systems detect icing conditions, they have to apply the evasive procedure as defined within the aircraft flight manual (AFM). However, as regards unmanned aircraft, there are not pilots on board and, consequently, nobody can act immediately when icing conditions occur. This article aims to propose new techniques of sending information to ground which make possible to know the aircraft performance correctly in icing conditions. For this goal, three contributions have been developed for the unmanned aircraft Milano. Since icing conditions are characterized quantitatively by the droplet size, the liquid water content, and the total air temperature, when these parameters are between certain limits ice formation on aircraft may occur. As a result of these contributions, in that moment, high-quality images of the wing leading edge, tail leading edge and meteorological probes will be captured and sent to ground making possible that remote pilots or artificial intelligent (AI) systems can follow the appropriate procedures, avoid encounters with severe icing conditions and perform real-time decision making. What is more, as information security is becoming an inseparable part of data communication, it is proposed how to embed relevant information within an image. Among the improvements included are image compression techniques and steganography methods.

Electrical impedance tomography (EIT) is an advanced visualization detection technique with non-invasive, radiationless, and fast-response characteristics. As an important means in dredging engineering, EIT-based measurement can realize the estimate and computation of key flow parameters such as the flow velocity and solid phase fraction of solid-liquid two-phase flow in a pipe. Despite progress, both the data acquisition rate and the signal-to-noise ratio (SNR) of the existing EIT system are too low to meet many practical requirements. In this study, efforts are made concerning the EIT acquisition system by optimizing the sensor array, data acquisition system, and data communication module. Experimental results show that (1) the optimized system has an SNR of 73 dB, which is about 40% higher than the original system, and (2) the optimization of data transmission methods can achieve a maximum allowable transmission rate of 316.4 Mbps, sufficient to support data transmission over 1000 frames per second. Consequently, the key problems of the existing EIT acquisition system are substantially overcome.

The use of portable devices is increasing day by day. Mainly these devices play a vital role in medical field when the doctors are unavailable. In that one of the important emergency medical portable systems is Electro Cardiogram (ECG). ECGs are used to measure the abnormalities of the heart by performing electrical activities. It can be done by attaching electrodes on the body for few minutes. To collect the information from electrodes data acquisition system is used in ECGs or in any biomedical systems. The major challenge in implementing data acquisition system is the increase in power dissipation due to the excessive contact impedance of electrodes. As a solution to this, A CMOS 12 lead ECG data acquisition circuit is designed by using analog multiplexers is proposed in this paper. The proposed system is designed by using improved DTMOS based analog multiplexers and these works with a power supply of ±0.2 V with a dynamic voltage range from 1uv to 200mv with the Power dissipation of 140 nW.

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.

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future.

Aiming at problems of complicated wiring, large energy consumption, low overall reliability of the current underground coal mining data acquisition system, an intrinsically safe low power consumption data acquisition system was designed. The system is mainly composed of coordinator and terminal equipment. Based on the XBee3 module, a wireless sensor network with a cluster topology is established. The terminal equipment uses the XBee3 module as the main control unit. The coordinator can perform some functions such as real-time analysis, storage, upload and display of data. The experimental results show that the system can accurately collect the sensor data of underground coal mines. It has the characteristics of long transmission distance, low power consumption, short delay.

The paper adopts the architecture of "ADC+FPGA", designs and develops a 1 GS/s, 16-bit high-speed and high-precision data acquisition system(DAS), which realized the purpose of using a single channel to measure large dynamic range(DR>1 000) signals. The study uses two sysref modes, which are periodic sysref mode and pulse sysref mode, to establishe a stable JESD204B link with a deterministic delay. The differences in the sampled data spectrum between the two modes is presented, and the considerations of hardware design and firmware design are given. The paper recommends that the periodic sysref mode be used to establish JESD204B link. By analyzing the time-domain waveform and frequency spectrum of the sampled data, the conclusion that the ADC chip contains 4 on-chip ADC channels is verified.

The Shanghai Synchrotron Radiation Facility Phase-II Project is now under construction and it consists of 16 newly built advanced beamlines. The experiments carried out at these new beamlines tend to be characterized by high flux, multi-mode, ultra-fast frequency, in situ and dynamic loading. To meet these emerging experimental demands of SSRF Phase-II beamlines, the authors have developed a unified and extensible data acquisition system based on Bluesky suite. It has been applied at the BL07U Spatial-resolved and Spin-resolved ARPES and magnetism beamline. The experiment results show that with up to 5 channel signals, the system is capable of collecting total electron yield signals of experimental sample and standard sample, and incident photon flux signals simultaneously. It improves the user experimental efficiency and quality. In addition, thanks to Bluesky suite, all devices can be abstracted as motors or detectors and then represented by a Python object. Therefore, with the architecture, modules and interface developed for BL07U, the system can be extended to more beamlines efficiently and easily.

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

The data acquisition system of the Alvand tokamak is a very old system of 32 channels. The data capacity of each channel is 2 kilobytes, altogether 64 kilobytes, which store the experimental data of selected channels in binary format. The most important deficiency of this system is the use of the CGA graphic adapter resolution of pixels of 4 bits in-depth. By considering the old graphical user interface, this deficiency has made displaying the experimental data in more detail impossible. Due to the old software used to display the experimental data, updating to an appropriate graphical environment, that can display the data on a typical modern computer at this laboratory, is necessary. For this purpose, a software package was developed to speed up the data analysis of experiments. The core of this package includes 400 lines and the graphical user interface consists of 3000 lines of Matlab code.

In the development of acoustic camera, aiming at the need of high-precision acquisition of acoustic field signals and real-time transmission of multi-sensor information, a modularized data acquisition system is designed, which integrates PGA, Σ-Δ ADC, FPGA as well as Gigabit Ethernet communication, and the condition of channel extension is given. According to the performance test, it shows that the sampling error of this module is 0.000 2%, the self-noise is 9 μV, and it has the advantages of 110 dB dynamic range under the bandwidth of 55 kHz, not only its performance better than the most often used multi-channel data acquisition device of vibration acoustics, but also has the characteristics of good extendibility and lower development cost.

The National Renewable Energy Laboratory manufactured, instrumented, and deployed thermoplastic composite blades and a data acquisition system (NDAQ) on one of Verdant Power’s Gen5d 5 m diameter tidal turbines in New York’s East River. The thermoplastic blades had internal strain gages, and the NDAQ was a stand-alone system for monitoring and recording the strain and angular position of the blades. The turbine with thermoplastic blades operated and produced power successfully for 3 months, contributing energy to the New York City electric grid. The NDAQ hardware, instrumentation, and structure all survived the deployment and were still functional upon retrieval of the system, but no data were collected. Even though the data retrieval was not a success, data acquisition for deployed subsea marine renewable structures is a new undertaking, and it is critical to share lessons learned from national laboratory experiences. The successful deployment of thermoplastic composite blades marks a significant advancement toward improved materials for subsea components, as well as an advancement in recyclable composite materials. This article outlines the methodology and lessons learned for the instrumentation and data acquisition system.

An anchoring frame beam is a very common form of support for reinforced slopes, especially in alpine regions. Centrifugal tests have proved to be an intuitive and effective means of investigating the mechanism of action of frame beams. However, the data acquisition system of the geotechnical centrifuge in service has the problem of a small number of acquisition channels. A multi-channel selector based on the existing acquisition system was proposed, designed, processed, and manufactured, and it was debugged, tested, and applied in a no-load centrifugal test, static pressure model test, and centrifugal model test. The results show that the acquisition mode of the multi-channel selector connected with a maximum of 288 sensors has been changed from “one-to-one” to “one-to-many”. Its influence on various sensor signals is negligible. The multi-channel selector can work normally, which communicates and feeds back with the remote controller in the 1–120 g no-load centrifugal test. In the static load model test, 162 sensor signals were well collected through it. And only 51 channels were used to effectively obtain the signals of 187 sensors in a 70 g centrifugal model test of an anchoring slope with a frame beam. The multi-channel selector can be successfully applied in different use environments, saving time and reducing the cost of obtaining a single set of data.

In recent years, cities are experiencing changes in the ways of moving around, increasing the use of micromobility vehicles. Bicycles are the most widespread transport mode and, therefore, cyclists’ behaviour, safety, and comfort have been widely studied. However, the use of other personal mobility vehicles is increasing, especially e-scooters, and related studies are scarce. This paper proposes a low-cost open-source data acquisition system to be installed on an e-scooter. This system is based on Raspberry Pi and allows collecting speed, acceleration, and position of the e-scooter, the lateral clearance during meeting and overtaking manoeuvres, and the vibrations experienced by the micromobility users when riding on a bike lane. The system has been evaluated and tested on a bike lane segment to ensure the accuracy and reliability of the collected data. As a result, the use of the proposed system allows highway engineers and urban mobility planners to analyse the behaviour, safety, and comfort of the users of e-scooters. Additionally, the system can be easily adapted to another micromobility vehicle and used to assess pavement condition and micromobility users’ riding comfort on a cycling network when the budget is limited.

The safety of road users is one of the priority issues raised by those involved in vehicle design, latest passive and active safety systems, traffic organization or driver education. Nowadays, an important road safety problem is the behaviour of drivers in emergency situations. In order to measurably estimate the driving quality, parameters such as velocity, acceleration, the way and frequency of using the control pedals are quite often used. This article describes how to assess driver's behaviour based on measurements taken on the road. The frequency of different acceleration ranges during the vehicle drive was determined based on the results obtained. For the arbitrarily adopted acceleration range of - 0.5 - 0.5 m/s2, the driver's working time was over 77 percent on average, with the difference varying significantly between different route sections. Similarly, the study compares the driving times for other ranges of acceleration.

The current and voltage of a solar cell can be measured by optimal load method. However, this method does not automatically adjust the load to ensure maximum power output as solar irradiance varies, hence power output losses. This study investigated the losses in power output that occur in solar cell when characterised by optimal load method. This was done by exposing the solar cell at different illumination intensities (between 300 and 1,000 W/m2 in the interval of 100 W/m2 ) and measuring current and voltage using Keithley Source-meter as well as optimal load (102 mΩ) method. From current and voltage, maximum power output of the solar cell for both methods were calculated and compared. It was found that the power output calculated from optimal load method (102 mΩ) were in good agreement with those from Keithley 2340 Source-meter only between 500 to 700 W/m2 . As a result, the solar cell lost between 1.3 to 33.4% of its maximum power when illumination intensities were outside the operating range of the optimal resistive load. Conversely, when a resistive load of 45 mΩ (which is not optimal) was used, the solar cell lost between 0.8 to 81.4% of its maximum power at various illumination intensities.

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.

A FPGA-based data acquisition system is designed，which is operated near space. In order to make the system run stably, this paper designs the system from both hardware and software. On the hardware side, FPGA is used as the controller of the acquisition system due to its strong anti-interference ability. Both the modules of sensor data acquisition and the implementing agency are redundantly processed and divided into main board and slave board to ensure the effectiveness of the system. In terms of software, based on the results of signal classification, it performs modular processing, analyzes the possible problems in the code and designs the state machine to deal with complex tasks. After testing, the data acquisition system can operate stably for a long time, can send sample data in real time, and can also respond to commands from the ground command station.

The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

The utilization of a hybrid energy system (combined solar water heater (SWH) and an air source heat pump (ASHP) water heater) can result in over 80% reduction in the electrical energy consumed as the system is capable to operate with an energy factor of above 4.0. A major challenge is to develop credible methodology or mathematical model to predict energy savings. The research focused on the design and installation of a hybrid energy system and a data acquisition system to monitor its performance. The average weekday volume of hot water consumed, thermal energy gained by water in the tank of the air source heat pump (ASHP) water heater, electrical energy consumed, and the COP were 225.03 L, 5.25 kWh, 1.52 kWh, and 3.50. The average weekday global solar radiation, ambient temperature, solar fraction of the solar water heater (SWH) and the energy factor of the hybrid energy system were 579.67 W/m2, 23.58°C, 0.52, and 4.02, respectively. A multiple linear regression model was developed to predict the energy factor of the hybrid energy system. Both the modelled and validated results showed very good determination coefficients of 0.952 and 0.935, with the trained and validated dataset. Hence, by employing both multiple linear regression model and a multiple 2D contour plot simulation, the energy factor and the variation of the input parameters can be accurately determined. The developed model can help homeowners, energy service companies, and policy makers to appreciate and confidently support the rollout of the technology for sanitary water heating.

Injection molding is a complicated process, and the final part quality depends on many machine and process parameters settings. To increase controllability of the injection molding process, acquisition of the process data is necessary. This paper describes the architecture and development of a prototype of an open application programming interface (API) for injection molding machines (IMMs), which has the potential to be used with different IMMs to log and set the necessary process parameter values. At the moment, the API includes an implementation of EMI data exchange protocol and can be used with ENGEL IMMs with CC300 and RC300 controllers. Data collection of up to 97 machine and process parameters (the number might vary depending on the type of machine at hand), obtained from sensors installed in the machine by the manufacturer is allowed. The API also includes a module for the acquisition of data from additional 3d party sensors. Industrial Raspberry Pi (RevPi) was used to perform analog-to-digital signal conversion and make sensors data accessible via the API prototype. The logging of parameters from the machine and from sensors is synchronized and the sampling frequency can be adjusted if necessary. The system can provide soft real-time communication.

Induction motors are used in the industries for various applications because they are reliable and rugged. However, late detection of faults and inappropriate maintenance of the machine often leads to damage of the windings which results in production losses and outright replacement. This study presents the use of a data acquisition system (DAS) to acquire current and vibration data of re-designed and re-wound burnt three-phase induction motor for the purpose of the motor faults analysis. The designed number of turns per pole per phase is 253 turns which were distributed in four slots per phase at 64, 63, 63 and 63 turns respectively. The DAS developed was used to acquire the current and vibration data at both normal and fault conditions through tapings prepared for the purpose. The average value of normal current data in the red, yellow and blue phases from the sampled data are 3.4 A, 3.1 A and 2.7A respectively. The average currents during short circuit between phase A and phase B are 11.3 A and 12.2 A respectively while vibration fault data stands at 1.62. Also, for the inter-turn short circuit in phase A, the average value of the currents in the phases are 12.1A, 3.1A and 2.7A respectively. The Mean Square Error value of the acquired and measured data is 0.00002. Keywords: three-phase induction motor, data acquisition system, stator winding design, microcontroller

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

The off-road vehicles are a necessary in today’s world for rescuing operations, military, racing and farming applications creating a huge demand for the AllTerrain Vehicle in the automobile market. The market size is estimated to be worth about $ 9.2bn (₹594Cr) by 2020. The drive-train of an All-Terrain Vehicle(ATV) is one of the major component in propelling the vehicle. Continuously Variable Transmission(CVT) coupled to a constant reduction gearbox, provides ease of driveability and the required high torque and top speed. In case of an ATV, high torque and high-top speed is the requirement, since it should be able to negotiate various terrains such as bumps, hill-climb, etc. Several authors have discussed, methods in designing CVT components of commercial vehicles which run on tarmac, and only few researchers have discussed methods to develop a CVT for an ATV requiring high velocity and high torque. The aim is to develop a lightweight, compact CVT system with high torque and top speed without any compromise on reliability. The force balance method is used to develop the analytical model. This model is used to design the various components of the CVT operating with gear ratios between 4:1 to 0.7:1. The structural analysis of all the CVT components are carried out. The designed and fabricated CVT components were then assembled on the ATV and performance studies were done by testing in various terrain conditions such as bumps, drops, slush and pulling weight from zero speed. All the components performed well, with no failures in all these conditions.

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.

BackgroundHigh energy physics (HEP) experiments aimed at studying elementary particles and their interactions need to acquire and analyze large amount of experimental data to discover new particles or measure the properties of known particles. With the development of HEP experiments, the energy and luminosity of accelerators are increasing, the scale of experiments is expanding, and consequently the acquisition, processing and analysis of large amount of data will be more challenging.PurposeThis study aims to design and implement a more advanced or effective distributed data acquisition framework to acquire and process the huge amount of data generated by tracking detectors for future HEP experiments where the number of channels and data volume of tracking detectors is extremely large.MethodsThe distributed data acquisition framework in HEP experiment divided into data flow software and online software was redesigned by using Hadoop big data framework, mainstream open source big data processing components was adopted to develope a new data acquisition framework—BigDataDAQ. Finally, this framework was applied to the prototype of the time projection chamber for verification.Results & ConclusionsResults of performance test show that the framework has high performance in data throughput and data processing, and can be easily deployed and managed. It provides a feasible solution for the future data acquisition system of high energy physics experiment.

A hardware and software implementation of compact data acquisition system for pulsed nuclear quadrupole resonance spectrometer is proposed. The developed system is based on multi-protocol converter USB-245FIFO FT2232H, which provides data transfer speeds up to 480 Mb/s. For nuclear spin induction visualization and data signal processing using graphical tools of the object oriented programming a LabVIEW virtual instrument is synthesized. To synchronize data acquisition system with start of the exciting pulse the FPGA configuration structure is developed. The experimental results are showed possibility of the broadband nuclear quadrupole resonance spectrums imaging with frequency resolution 1.6 kHz, which confirms the high accuracy of the developed data acquisition system. Two-channel transmitter provides simultaneous operation of the two independent data channels in a single hardware USB interface. The use of the proposed data acquisition system for portable nuclear quadrupole resonance spectrometers will significantly reduce the cost of laboratory equipment for radio physical experimentation.

This research presents a novel technique to synchronise a modular control and data acquisition system for the testing of multi-level power hardware-in-the-loop (PHIL) amplifiers. The methodology is inspired by the precision timing protocol, used to compensate for communications latency between distributed control nodes. It uses a non-conventional digital pulse-width modulation (PWM) technique, phase accumulator carrier pulse-width modulation to achieve carrier synchronisation across the system. A prototype design has been demonstrated on a three-node test bench, showing single clock cycle (10 ns) precision when synchronising a 25 kHz PWM carrier wave, with minimal sensitivity to communications link propagation delays.

This study presents a newly developed modular system for control of power electronic amplifiers in high-power, high-frequency power hardware in the loop experiments. The proposed design comprises computational nodes connected via multi-gigabit fibre-optic links. Nodes are modular and reconfigurable, allowing interfacing with a range of sensors and actuators, typically voltage and current sensors, and switching converters. The system has been designed for scalability to permit arbitrarily high power ratings and fidelity to be achieved through paralleling and interleaving of multiple power converter modules, targeting an ultimate sample and control rate of 1 MHz. Experimental validation is presented using a two-node configuration to facilitate a power electronic experiment operating at 120 V(rms) and 10 A(rms), with a fundamental frequency of 50 Hz and frequency content up to the 11th harmonic.

Real-time spatio-temporal meteorological and oceanographic data, from the Ocean moored observatories, are essential for the precise forecast of the ocean state, climate variability studies and reliable weather prediction. Precise spatio-temporal measurement of subsurface parameters such as temperature, salinity and current are essential to understand the intra-seasonal and inter-annual evolution of monsoons and tropical cyclones. To cater to this time-critical information, moored observatories have to continuously be operational in the harsh marine environment to measure these essential ocean variables. However, bio-fouling and corrosion limits the life time and accuracy of the highly precise measuring instruments. Thus, best practices in these moored observations are essential for long term accurate and cost-effective ocean observation. The Indian moored buoy network which has been operational since 1997, has been providing quality data over the past decade. This paper describes the best operational practices and quality control processes followed in the Indian moored buoy system design, sensor calibration, testing, integration, deployment, retrieval, and data quality control over the past two decades, which has helped to achieve an average meteorological data return of 90%.

This paper presents an improvement that cominate the algorithm of edge computation and the Internet of things (IoT) data acuisiton system. Firstly, the environment data acquisition system of the agriculture IoT was analyzed and the distributed data processing approach was proposed to handle the abnormal data. we perform the simply processing of the raw acquisition data via the CC2530 by its spare time computation competence. The results prove our algorithm improve the data acquisition efficiency.

A new material testing reactor Jules Horowitz Reactor is under construction at CEA Cadarache. The materials to be irradiated will be placed into experimental devices around the reactor. Process and measurements of experimental devices will be carried out by command control. A data acquisition system having processing performances will be associated to the programmable logic controller. The challenge is to design and realize for twenty experiment devices a high availability data acquisition system architecture for 50 years of sustainability. The real time target will achieve 24/7 data acquisition and real time processing. This scalable architecture could be use as well for JHR experimental devices with high availability as for testbed. This architecture could be run on a standalone station for a measuring bench or deployed on cluster for high availability. CAREDAS’s design is modular and use proven widely used open source solutions. All parts are independent from each other and can be replaced with another technology with the same functionalities. This ensures sustainability and control of software sources.

Wastewater treatment plants and power generation constitute inseparable parts of present society. So the growth of wastewater treatment plants is accompanied by an increase in the energy consumption, and a sustainable development implies the use of renewable energy sources on a large scale in the power generation. A case study of the synergy between wastewater treatment plants and photovoltaic systems, aiming to improve the energetic, environmental and economic impacts, is presented. Based on data acquisition, the energy consumption analysis of wastewater treatment plant reveals that the highest demand is during April, and the lowest is during November. The placement of photovoltaic modules is designed to maximize the use of free space on the technological area of wastewater treatment plant in order to obtain a power output as high as possible. The peak consumption of wastewater treatment plant occurs in April, however the peak production of the photovoltaic is in July, so electrochemical batteries can partly compensate for this mismatch. The impact of the photovoltaic system connectivity on power grid is assessed by means of the matching-index method and the storage battery significantly improves this parameter. Carbon credit and energy payback time are used to assess the environmental impact. The results prove that the photovoltaic system mitigates 12,118 tons of carbon and, respectively, the embedded energy is compensated by production in 8 ½ years. The economic impact of the photovoltaic system is analyzed by the levelized cost of energy, and the results show that the price of energy from the photovoltaic source is below the current market price of energy.

Hydraulic blind backfilling is used to reduce subsidence problems above old underground water-logged coal mines. This paper describes experimental research on a fully transparent model of a straight underground mine gallery. An automatic data acquisition system was installed in the model to continuously record the sand and water flowrates along with the inlet pressure of the slurry near the model's inlet. Pressure signature graphs and pressure loss curves with bed advancement under different flow conditions are examined. Pressure signature analyses for various flowrates and sand slurry concentrations are conducted to evaluate a pre-jamming indication parameter, which could be used to indicate the arrival of the final stage of filling.

The Article presents the design, development and implementation of a virtual instrument for the generation of temperature profiles in the cooking process in ovens, obtaining a characteristic curve of the actual cooking process, through a virtual platform coupled to a data acquisition system, The purpose is to obtain a real pattern of the thermal behavior of the cooking process in its transitory state, to later compare it with an ideal topic and thus determine the failures in the process with the implementation of technologies available, the selection of temperature sensors and data acquisition cards.

Existing acoustic test benches are usually costly devices based on proprietary designs, sensors, and acquisition devices. In this paper, a low-cost test bench for acoustic purposes is introduced. The design of the test bench takes into account not only the low-cost mechanical design, but also uses low-cost sensors and control boards. This test bench has been designed for a range of signals compatible with those used by thermoacoustic engines, but it can be useful for applications with similar requirements. Taking advantage of an auxiliary pressure reference, low-cost unidirectional differential pressure sensors can be used to significantly increase the accuracy of the sampling system. The acoustic and mechanical design and development are presented along with the sampling system and the sensors arrangement implemented. Both the sensor and sampling system are evaluated by comparison with a high-fidelity sound acquisition system. An unexpected effect on the time error values distribution of the low-cost acquisition system is found and described. Finally, the errors introduced by the system and the sensors in terms of time and pressure sampling are characterized. As a result, the low-cost system’s accuracy has been satisfactory assessed and validated for the conditions expected in thermoacoustic experiments in terms of frequency and dynamic pressure.

Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the effectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.

This article presents a novel and reliable low-cost data acquisition solution for high frequency and real-time applications in vehicular dynamics. Data acquisition systems for highly dynamic systems based on low-cost platforms face different challenges such as a constrained data retrieval rate. Basic data reading functions in these platforms are inefficient and, when used, they limit electronics acquisition rate capabilities. This paper explains a new low-cost, modular and open platform to read different types of sensors at high speed rates. Conventional reading functions are avoided to speed up acquisition rate, but this negatively affects data reliability of the system. To solve this and exploit higher data managing rates, a number of custom secure layers are implemented to secure a reliable acquisition. This paper describes the new low-cost electronics developed for high rate acquisition applications and inspects its performance and robustness against the introduction of an increasing number of sensors connected to the board. In most cases, acquisition rates of the system are duplicated using this new solution.