Data Acquisition System Research Articles

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

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

Investigation of current density and temperature distribution characteristics under transient conditions based on a 108 cm2 segmented proton exchange membrane fuel cell

Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis.

Experimental study on the elastic response characteristics of a rotating body under an oblique water impact load

In this study, an integrated experimental model was designed and a data acquisition system was established to capture the structural response of an elastic rotating body throughout the oblique water impact process. This established experimental methodology offers a dependable approach for gathering structural response data during the water impact process. The axial impact loads and effective stress on the rotating body throughout water impact were measured using acceleration sensor and strain gauge rosettes installed in the water impact zone of the structure, while a high-speed camera system captured the evolution of the flow field. Results from the arbitrary Lagrangian-Eulerian (ALE) numerical model were compared with those from repeated experiments, revealing good agreements. The article conducted water impact tests at different impact initial conditions, analyzing the response of the body to the impact load during water entry process across different water impact velocities and angles. Observations during the oblique water impact process revealed that the stress response at the center of the rotating body impact surface is approximately equal to that of the first water impact point, with this effect becoming more pronounced at higher velocities. This study contributes valuable experimental data for numerical investigations into the water impact of elastic structures.

Wind turbine gearbox condition monitoring using AI-enabled virtual indicators*

Abstract At present, the condition monitoring of wind turbine gearbox mostly involves selecting monitoring indicators in advance and establishing normal behavior models based on deep learning. However, single physical monitoring indicator cannot fully characterize the operating status of the wind turbine gearbox. To address the above issues, a wind turbine gearbox condition monitoring method using convolutional neural network (CNN)-long and short term memory network (LSTM)-autoencoder (AE) enabled virtual indicators is proposed in this paper. This method first establishes a cleaning principle to preprocess the data of supervisory control and data acquisition (SCADA) system and screen out effective SCADA data further. A CNN-LSTM-AE monitoring model was trained using SCADA data during normal operating process, various characteristic parameters of the gearbox are integrated to construct the AI-enabled virtual indicators, and meanwhile warning thresholds was determined based on the probability density distribution of virtual indicators to analyze the operating status of the gearbox. Finally, the proposed method was validated using real SCADA data from two wind turbines in a cooperated wind farm with my group. Compared with a single physical indicator, the virtual indicator enables to detect gearbox early faults 5 d in advance, indicating that the proposed method can effectively alert wind turbine gearbox failures.

Energy and time characterization of the newly acquired clover detector in Jordan

Abstract A comprehensive overview of the Balqarad clover detector acquired by Al-Balqa Applied University in Jordan is given in detail. The detector consists of four high-purity germanium crystals, accompanied by a composite scintillation active shield. Through meticulous testing involving a range of radioactive sources such as 137Cs, 60Co, and 241Am, we evaluate the performance of the clover detector. The associated digital data acquisition system incorporates specialized and unconventional time registers, operating in event-by-event mode. Our objective is to elucidate the energy and time characteristics exhibited by the new system under different operational modes, employing specifically designed offline software. Our findings encompass vital aspects. These aspects include the determination of the optimal time window value for processing data obtained from gamma detection measurements. Another issue that has been considered is the analysis of hit pattern distribution within each individual crystal. Additionally, we calculate the addback factor for the 60Co source, accounting for gamma energies of 1173 keV and 1332 keV while also considering the influence of time window increment.

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems.

Initial Study of the Onsite Measurement of Flow Sensors on Turbine Blades (SOTB)

This paper presents a new framework using MEMS flow sensors on turbine blades (SOTB) to investigate unsteady flow features of a rotating wind turbine. Self-heating flow sensors were implemented by the U18 complementary metal-oxide semiconductor (CMOS) MEMS foundry provided by Taiwan Semiconductor Research Institute (TSRI). Flow sensor chips with a size of 1.5 mm × 1.5 mm were parylene-coated, output via a wireless data acquisition system (WDAQ), and mounted at the root, middle and tip of a 1.2 m diameter semi-rigid turbine blade of a 400 W horizontal axis wind turbine (HAWT). The instantaneous angles of attack (AOAs) of the SOTB were found to be 46~62°, much higher than the general stall AOA of 15°, but were accurate considering the normal detection of the flow sensors. The computational fluid dynamics (CFD) simulation of the HAWT was also compared with the SOTB output. The onsite measurement herein revealed that the 3D secondary flow increment, mostly obvious near the middle part of the turbine blades, degraded both the sensor and the turbine performance and initially justified the onsite measurement application.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors

This research explores how random events influence the respiration rate in Hass avocado beyond deterministic models in order to develop better strategies for extending its shelf life. Understanding these factors can enhance the accuracy of postharvest management strategies. The Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables (ARIMAX) is an alternative stochastic probability model which is capable of modeling complex, externally influenced phenomena such as respiration. This study aimed to elucidate the effect of three exogenous variables, namely temperature, relative humidity, and ambient illumination, on the respiration rate of Hass avocado fruits. Data on the respiration rate and exogenous variables were obtained using sensors coupled to a data acquisition system in a prototype of continuous airflow. The Box–Jenkins methodology was employed to construct the ARIMA models. The temperature, relative humidity, ambient illumination, and respiration rate variables were adjusted to the ARIMA models (3,1,2), ARIMA (1,1,2), ARIMA (1,1,2), and ARIMA (1,1,3), respectively. The ARIMAX (1,1,3) models were obtained from the pre-whitened respiration rate series. The impact detected in the transfer functions indicates increases in the respiration rate of 0.34%, 1.52%, and 0.99% for each unit increase in the temperature, relative humidity, and ambient illumination variables, respectively. In this regard, ARIMAX modeling is reliable for explaining the physiological response of Hass avocado fruits due to external factors. In future research, it is intended to extrapolate this stochastic modeling procedure to measure the effect of dynamic loads on the respiratory metabolism of fruits during transportation, where there is a considerable loss in the quality of fresh products.

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

A gait phase recognition method for obstacle crossing based on multi-sensor fusion

The analysis of human motion patterns and gait phase detection holds significant importance for both human motion analysis and precise control of exoskeleton devices. Presently, research on gait during obstacle crossing primarily focuses on the medical rehabilitation domain, with limited studies concerning gait phase analysis during obstacle crossing. This study conducts a detailed analysis of gait data during obstacle crossing and proposes a CNN-PCA-LSTM algorithm that fuses multi-sensor data to accurately identify gait phases when crossing obstacles in the horizontal direction. A wearable multi-sensor data acquisition system was designed, utilizing flexible capacitive pressure sensors to collect pressure data from the soles of both feet and IMUs to gather foot motion data. Kinematic simulation analyses of human obstacle-crossing motions were performed using Anybody software to determine various gait phases, validating the efficacy of the simulation model. Subsequently, one-dimensional and two-dimensional convolutional neural networks were separately employed to extract features from sole pressure data and foot motion data. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of these feature datasets, which were then inputted into LSTM networks. Finally, the Softmax function was employed for the classification and recognition of gait phases when crossing obstacles in the horizontal direction. The experimental results indicate that employing the CNN-PCA-LSTM algorithm integrating multiple sensor data achieves a recognition accuracy of 97.91 % for identifying gait phases during horizontal obstacle crossing.

A Novel Scalable Digital Data Acquisition System for Industrial Condition Monitoring

A Novel Scalable Digital Data Acquisition System for Industrial Condition Monitoring

Impact energy in Heat-Treated aluminium alloy 6061 and 7075

Instrumented Charpy impact is one of the promising methods to assess the impact energy; toughness and resilience of a material. The test consists of the Charpy machine and instrumentation equipment which is the strain gauge and the data acquisition system. Impact energy evaluations are important to understand the material behaviour subjected to sudden impact loading. Aluminium 7075 has good strength strength-to-weight ratio than aluminium 6061 and hence the AL 7075 is usually used in the aerospace industry, whilst the AL 6061 is commonly used in the automotive industry. The objective of the study is to observe and analyse the impact response of the Aluminium 6061 and 7075 at different heat- treated conditions; O-anneal and T6 temper. In addition to the impact energy absorbed value, the impact strain signal is recorded too. Theoretical impact energy is calculated as well based on the information given by the Charpy machine software. Aluminium 7075 exhibits a lower value of impact energy absorbed and a smaller area under the curve compared to Aluminium 6061. The energy absorption capacity for each material grade and heat-treated condition are different from one another. O-anneal aluminium has a lesser energy absorbed value and a smaller area under the curve than the T6 temper aluminium. The outcome reveals that the heat treatment process alters the material microstructure and mechanical properties, whereas in this study the impact energy responses are influenced by these circumstances. Moreover, the chemical composition of the aluminium alloy distinguishes these two grades from one another, thus resulting in different impact responses.

Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring.

With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first device, termed CEDAS_acc, integrates the ADXL355 MEMS accelerometer with a RaspberryPi mini-computer, ideal for measuring strong ground motions and assessing structural modal properties during forced vibration tests and structural monitoring of mid-rise buildings. The second device, CEDAS_geo, incorporates the SM24 geophone sensor with a Raspberry Pi, designed for weak ground motion measurements, making it suitable for seismograph networks, seismological research, and early warning systems. Both devices function as acceleration/velocity Data Acquisition Systems (DAS) and standalone data loggers, featuring hardware components such as a single-board mini-computer, sensors, Analog-to-Digital Converters (ADCs), and micro-SD cards housed in protective casings. The CEDAS_acc includes a triaxial MEMS accelerometer with three ADCs, while the CEDAS_geo uses horizontal and vertical geophone elements with an ADC board. To validate these devices, rigorous tests were conducted. Offset Test, conducted by placing the sensor on a leveled flat surface in six orientations, demonstrating the accelerometer's ability to provide accurate measurements using gravity as a reference; Frequency Response Test, performed at the Gebze Technical University Earthquake and Structure Laboratory (GTU-ESL), comparing the devices' responses to the GURALP-5TDE reference sensor, with CEDAS_acc evaluated on a shaking table and CEDAS_geo's performance assessed using ambient vibration records; and Noise Test, executed in a low-noise rural area to determine the intrinsic noise of CEDAS_geo, showing its capability to capture vibrations lower than ambient noise levels. Further field tests were conducted on a 10-story reinforced concrete building in Gaziantep, Turkey, instrumented with 8 CEDAS_acc and 1 CEDAS_geo devices. The building's response to a magnitude 3.2 earthquake and ambient vibrations was analyzed, comparing results to the GURALP-5TDE reference sensors and demonstrating the devices' accuracy in capturing peak accelerations and modal frequencies with minimal deviations. The study also introduced the Record Analyzer (RECANA) web application for managing data analysis on CEDAS devices, supporting various data formats, and providing tools for filtering, calibrating, and exporting data. This comprehensive study presents valuable, practical solutions for SHM, enhancing accessibility, reliability, and efficiency in structural and seismic monitoring applications and offering robust alternatives to traditional, costlier systems.

Performance Analysis of Permanent Magnet Synchronous Generator for Small Scale Wind Energy Applications

This paper presents a study of the performances of a permanent magnet synchronous generator (PMSG) used in small scale wind energy applications. The d-q model is adopted for the performance analysis of the PMSG under variable operating conditions. In addition, in order to validate the results obtained with the theoretical model, an experimental test bench based on a small PMSG equipped with a data acquisition system has been developed. The study carried out on the experimental test bench made it possible to determine both the dynamic and the steady state performances of the PMSG under variable loads and rotational speeds.

Performance Analysis of Permanent Magnet Synchronous Generator for Small Scale Wind Energy Applications

This paper presents a study of the performances of a permanent magnet synchronous generator (PMSG) used in small scale wind energy applications. The d-q model is adopted for the performance analysis of the PMSG under variable operating conditions. In addition, in order to validate the results obtained with the theoretical model, an experimental test bench based on a small PMSG equipped with a data acquisition system has been developed. The study carried out on the experimental test bench made it possible to determine both the dynamic and the steady state performances of the PMSG under variable loads and rotational speeds.

Design and Research of a Strain Elastic Element with a Double-Layer Cross Floating Beam for Strain Gauge Wireless Rotating Dynamometers

Cutting force is one of the most basic signals that can reflect the information of the cutting process, so it is very necessary to study the strain elastic element of strain gauge wireless rotating dynamometers. This paper proposes a strain elastic element with a double-layer cross floating beam that can be applied to the strain gauge wireless rotating dynamometer, which can simultaneously obtain the four-component cutting force/torque information of FX, FY, FZ, and MZ. Based on the proposed strain elastic element, a compact strain gauge wireless rotating dynamometer is designed, which is composed of a tool holder, upper connection flange, strain elastic element, lower connection flange, tool base, and data acquisition and wireless transmission system. The static model of the double-layer cross floating beam on the strain elastic element is established by the segmented rigid body method, and the relationships between the material, force, structural parameters, and the strain and deformation of the floating beam are obtained. The static model is consistent with the finite element solution, which proves the rationality of the static model. Based on the established static model, the sequential quadratic programming algorithm is used to optimize the structural parameters of the double-layer cross floating beam to maximize the sensitivity of the floating beam. The overall structure of the strain elastic element is analyzed by finite element software, and the strain of the structure under simulation conditions is obtained, which provides a reference for subsequent calibration tests and circuit design. The calibration matrix and dynamic performance of the strain elastic element are obtained by the static calibration test, dynamic calibration test, and cutting test. The results show that the proposed strain elastic element has high sensitivity and low cross-sensitivity error, and can be applied to the strain gauge wireless rotating dynamometer to measure medium- and low-speed cutting forces.

ENHANCING ROAD SAFETY FOR VULNERABLE ROAD USERS – THE ROLE OF AUTONOMOUS EMERGENCY BRAKING SYSTEMS

Vulnerable road users are the largest group of road accident victims in the world. At the same time, it should be noted that most road accidents are caused by the motor vehicle driver. An opportunity to increase the level of road safety is emergency braking systems installed in vehicles. Their task is to detect the risk of a collision with another object, issue a warning to the driver and, in the absence of reaction, perform emergency braking. The publication presents the results of research conducted by the Motor Transport Institute as part of the PEDICRASH project. As part of the work, a series of tests of the autonomous emergency braking system were carried out to check unusual but probable cases of events in which it should work. The tests were carried out in a closed area using a few selected models of popular passenger car brands. An additional data acquisition system and dummies imitating vulnerable road users: pedestrians and cyclists were used. The constructions represent the silhouette of an adult person and meet the requirements of the Euro NCAP AEB Protocol. The time of the collision at the time of issuing a warning to the driver was analyzed. Assuming that the vehicle was moving uniformly, the distance from the obstacle was calculated at the time of issuing the warning and after the driver's reaction time (0.7 s and 1 s were assumed) from this signal. The deceleration necessary to brake the vehicle was calculated and it was determined whether the driver would have a chance to brake the vehicle before a pedestrian or a cyclist in a given situation. For cases in which the driver would not be able to brake in time, the speed of the vehicle with which it would hit the vulnerable road users was calculated. Possible injuries and accident costs were then estimated.

ENHANCING ROAD SAFETY FOR VULNERABLE ROAD USERS – THE ROLE OF AUTONOMOUS EMERGENCY BRAKING SYSTEMS

Vulnerable road users are the largest group of road accident victims in the world. At the same time, it should be noted that most road accidents are caused by the motor vehicle driver. An opportunity to increase the level of road safety is emergency braking systems installed in vehicles. Their task is to detect the risk of a collision with another object, issue a warning to the driver and, in the absence of reaction, perform emergency braking. The publication presents the results of research conducted by the Motor Transport Institute as part of the PEDICRASH project. As part of the work, a series of tests of the autonomous emergency braking system were carried out to check unusual but probable cases of events in which it should work. The tests were carried out in a closed area using a few selected models of popular passenger car brands. An additional data acquisition system and dummies imitating vulnerable road users: pedestrians and cyclists were used. The constructions represent the silhouette of an adult person and meet the requirements of the Euro NCAP AEB Protocol. The time of the collision at the time of issuing a warning to the driver was analyzed. Assuming that the vehicle was moving uniformly, the distance from the obstacle was calculated at the time of issuing the warning and after the driver's reaction time (0.7 s and 1 s were assumed) from this signal. The deceleration necessary to brake the vehicle was calculated and it was determined whether the driver would have a chance to brake the vehicle before a pedestrian or a cyclist in a given situation. For cases in which the driver would not be able to brake in time, the speed of the vehicle with which it would hit the vulnerable road users was calculated. Possible injuries and accident costs were then estimated.

Microcalorimetric study of the bactericidal action of titanium tetrachloride on the Pseudomonas aeruginosa growth. Potential implications in orthopaedic surgery

With the rising use of metallic implants worldwide, bacterial infection has become a major concern due to its significant impact on patient health and national healthcare budgets. This study aimed to evaluate the sensitivity of Pseudomonas aeruginosa, a common bacterium responsible for implant surgery infections, to different concentrations of titanium tetrachloride (TiCl4) solutions. Microcalorimetry was used to analyze the heat output produced by bacteria metabolism when exposed to TiCl4 at concentrations ranging from 0 to 10 mM, as well as a saturated solution. The inner chamber of the calorimeter was set to the physiological temperature of the human body (309.65 K), and the heat output produced by bacteria metabolism was collected at intervals of 22.2 s for 48 h using a data acquisition and processing system. The data was then represented as thermograms. The results showed that higher concentrations of TiCl4 had a bactericidal effect on the growth of Pseudomonas aeruginosa. This study supports the potential use of higher concentrations of this compound in surgical implants to reduce the risk of infection development due to its bactericidal properties.