Low Cost Data Acquisition System Research Articles

Real-time multi-physical system identification and virtual sensing for a lab-scale chemical stirred tank using parallel estimators

In the initial stages of developing a new chemical process, chemists usually use a lab-scale stirred tank to analyze the yield amount and study the mass balance. However, understanding the energy balance is challenging. Monitoring the energy balance requires separating device and process-related effects, which is time-consuming and requires insight into the dynamics of the device and process. As a result, most lab-scale studies often overlook it. A real-time multi-physical virtual sensing and system identification approach is proposed in this study to address this issue by estimating desired unknown electro-thermo-mechanical parameters, states, and disturbances. The estimated unknowns include heat transfer coefficients, motor temperature, stirring torque, and heat generation rate. For this aim, a multi-physical lumped model is developed, which covers the electro-thermal and electro-mechanical responses of the device, as well as the process-related thermo-mechanical effects. The model is decoupled, and three parallel extended Kalman filters are employed. The developed model, estimation procedure, and measurement system are implemented in two experimental examples, including a hydrogenation reaction and the reductive catalytic fractionation of biomass. Both cases use a low-cost data acquisition system designed and manufactured to perform the measurements in this research. Comparing the logged measurements with the estimated values indicates considerable agreement in both cases. Based on the results, decoupling the model and using parallel estimators make the tuning easier, improve observability, and reduce the estimation time.

Performance Analysis of Bluetooth Audio Devices As Wireless Data Acquisition Systems

Data acquisition is an ubiquitous process to gather information about real-world phenomena to be processed in the digital domain. Currently, portable and wireless devices have become attractive devices for measuring several processes. However, when a high-sampling-rate is required, data acquisition systems (DAS) are still expensive and targeted at a very small niche market. Moreover, low-cost DAS are affordable but they are resource-limited mainly resulting in a low-sample-rate. Interestingly, audio-based systems are versatile, their sampling frequency ranges from 1 kHz to 192 kHz, and support input voltages from ±1.0 V at 16-bit resolution. Furthermore, Bluetooth audio devices (BTAD) are currently, popular in personal devices, which stream bunches of data using electromagnetic waves in the radio-frequency band. Nonetheless, audio-focused devices are optimized to respond to signals within the audible spectral range (20 Hz to 20 kHz), which represents a limitation to work as general-purpose DAS. In this work, we assess the advantages and limitations of low-cost BTAD to work as wireless DAS by increasing its performance and reliability. For this purpose, we introduce a robust data processing technique based on spectral analysis. Finally, the functionality of different BTADs is evaluated over a frequency range out of their native limits, and a compensation method for the frequency bias is proposed.

An Automated Easy-To-Fabricate Approach to Characterize Various Diodes Using Arduino UNO

This paper introduces a fully automated Arduino based low-cost data acquisition system for studying I-V characteristics of diodes. For that, a Virtual instrument has been developed using the LabVIEW platform and LabVIEW LINX toolkit. The VI incorporates a 12-bit digital-to-analog converter (DAC) with model number MCP4921, which is connected to an Arduino UNO board to generate analog voltage signals for real-time visualization of the current-voltage (I-V) characteristics of both a silicon diode (1N4007) and a germanium diode (1N34).

Early Detection of Cavitation in Centrifugal Pumps Using Low-Cost Vibration and Sound Sensors

The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was acquired using the embedded accelerometer sensor of a smartphone device. Sound signals were obtained using the embedded microphone of the same commercial smartphone. The analysis was based on comparing the signals in different operating conditions with reference to the best efficiency operating point of the pump. In the case of vibrations, data was acquired for all three directional axes. The signals were processed by computational methods to extract the relative features in the frequency domain and use them to train an artificial neural network to be able to identify the different pump operating conditions while the cavitation phenomenon evolves. Three different classification algorithms were used to examine the most preferable approach for classifying data, namely the Classification Tree, the K-Nearest Neighbor, and the Support Vector Data algorithms. In addition, a convolutional neural network was utilized to examine the success rate of the classification when the datasets were formed as spectrograms instead. A detailed comparison of the classification algorithms and different axes was conducted. Comparing the results of the different methods for vibration and sound datasets, classification accuracy showed that in the case of vibration, the detection of cavitation in real conditions is possible, while it proves more challenging to identify cavitation conditions using sound data obtained with low-cost commercial sensors.

SDAV 1.0: A Low-Cost sEMG Data Acquisition & Processing System For Rehabilitatio

Over the last two decades, myoelectric signals have been widely used in fields including rehabilitation devices and human-machine interfaces. This study aimed to develop an algorithm for surface electromyography (sEMG) data acquisition utilizing low-cost hardware and validate its performance using English vowels as silent speech content. The sEMG data were collected from the three facial muscles of one healthy subject. The sEMG signals were pre-processed, and various time-domain and statistical features were extracted in real time. The raw data and features were then used to train and test three customized machine learning classifiers: k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN). All customized classifiers achieved almost equivalent accuracy rates of 0.83 ± 0.01 in recognizing the English vowels with an improvement of 27.27% (KNN), 3.75% (SVM), and 51.85% (ANN) utilizing the same low-cost data acquisition hardware. Our findings are substantially closers to the results of commercial hardware setups, which raise the possibility of potential usage of low-cost sEMG data acquisition systems with the proposed algorithm in place of commercial hardware setups for rehabilitation devices and other related sectors of human-machine interaction.

Development of a low-cost data acquisition system for a vapor compression refrigeration experimental workbench

Refrigeration and air conditioning systems are essential in residential, commercial, and industrial segments. The data acquisition and the control of equipment results in an improvement in efficiency and a decrease in energy consumption, considering that the data obtained bring a better visualization and understanding of the components of the system. This work aims to automate data acquisition from a vapor compression refrigeration workbench by developing a low-cost data acquisition system. The system was developed using Arduino and LabView with the assistance of LINX software. After the communication between the controller and the program, six thermocouples were installed on the refrigeration workbench to acquire the temperatures in the program of the data acquisition system. For reading system pressure values, a camera was used to capture images from six analog pressure gauges. From reading these data, the pressure-enthalpy diagram of the thermodynamic cycle was obtained. The experimental refrigeration workbench has been used in teaching and research activities. The data acquisition system developed for the refrigeration workbench improved the realization of experiments, allowing a better comparison between experimental and theoretical results.

Low-Cost Data Acquisition System for Automotive Electronic Control Units.

The vehicle testing-validation phase is a crucial and demanding task in the automotive development process for vehicle manufacturers. It ensures the correct operation, safety, and efficiency of the vehicle. To meet this demand, some commercial solutions are available on the market, but they are usually expensive, have few connectivity options, and are PC-dependent. This paper presents an IoT-based intelligent low-cost system for vehicle data acquisition during on-road tests as an alternative solution. The system integrates low-cost acquisition hardware with an IoT server, collecting and transmitting data in near real-time, while artificial intelligence (AI) algorithms process the information and report errors and/or failures to the manufacturing engineers. The proposed solution was compared with other commercial systems in terms of features, performance, and cost. The results indicate that the proposed system delivers similar performance in terms of the data acquisition rate, but at a lower cost (up to 13 times cheaper) and with more advanced features, such as near real-time intelligent data processing and reduced time to find and correct errors or failures in the vehicle.

Identificação dinâmica de uma passarela estaiada utilizando um sistema de aquisição de dados de baixo custo

Abstract This study describes the dynamic identification of a cable-stayed footbridge using an alternative data acquisition system and output-only modal identification methods. Data were collected during dynamic tests performed using an acquisition system based on the Arduino platform, consisting of low-cost devices with on-board Micro-Electro-Mechanical System (MEMS) accelerometers. The Peak-Picking (PP) method was used for the stay cables. The Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification - Unweighted Principal Components (SSI-UPC) methods were used for the complete structure. The proposed data acquisition system efficiently recorded the time series required for Operational Modal Analysis and the acceleration acquisition process provided stable results. At least four mode shapes were identified in all tests. A minimum of four high energy peaks in the 0 - 24 Hz frequency range of the spectrum were obtained by the acquisition system in all of the cable tests and selected by the method. In the complete structure tests, the low-cost data acquisition system and the identification methods provided the first four flexural mode shapes, within the 0 - 9 Hz frequency range. Results for the frequency domain method showed a maximum difference of 2.37% in the first experimental frequency when compared to a 3D finite element numerical model, while in the other frequencies the difference was between 1 and 2%. For the time domain method, the maximum difference was 1.74% in the fourth frequency, with differences of between 0.1 and 0.7% in the other frequencies. The mode shapes were evaluated using the Modal Assurance Criterion (MAC) index, and the results varied between 0.8513 and 0.9990.

Raspberry Pi-Based Low-Cost System for Electrical Parameters Gathering and Analysis for Performance Evaluation of Electric Vehicles

In recent years, both the manufacture and sales of electric vehicles have significantly grown compared to traditional vehicles. This growth has favored the creation of tools that measure and evaluate this class of vehicles’ mechanical and electrical operation quickly, simply, and efficiently. Nevertheless, the measurement of electric-vehicle parameters can be a challenge due to the fact that the required DAQ equipment is expensive and thus of limited availability. This work describes a low-cost data acquisition system to simplify and reduce the cost of measuring the current and voltage parameters of electric vehicles. The proposed system is based on a Raspberry PI 3b+ microcomputer and it was evaluated during different driving cycles. The results were compared with the data obtained using a professional Dewesoft Sirius system for validation. The Raspberry Pi-based low-cost system achieved comparable performance to commercial systems (with a measurement error of less than 1%), despite having a lower sampling rate. Furthermore, unlike other systems, the proposed platform is an easy-to-set universal solution that can store the measured data, display them in real-time, graph them, and export the information for analysis with other specialized statistical tools. The main contribution of this work lies in the fact that the proposed low-cost DAQ system can be easily cloned, and the components cost of the prototype is about 10 times less than the price of the commercial systems.

Development of Special Geomagnetic Data Acquisition System with Low Cost

It is known to all that the wide monitoring of geomagnetic anomalies requires geomagnetic detection equipment to be of low cost, low power, and high stability. On the basis of the weak magnetic signal detection technology, a weak magnetic signal acquisition system with low-cost magnetic data acquisition, data processing, and data analysis is established by using a three-dimensional SEN-R65 magnetic sensor, magnetic signal acquisition chip PNI 11096, and P89C668 chips. Firstly, the hardware of a low-cost geomagnetic data acquisition system is designed, and the corresponding data processing and analysis programs are written. Based on the designed hardware circuit and data processing for functions such as acquisition, transmission, storage, and real-time display of geomagnetic signals, it is found that the developed instrument meets the expectations of the low-cost geomagnetic data acquisition system designed in this paper, indicating that the system is applicable.

Design and Implementation of Low-Cost Data Acquisition System for Small and Medium Enterprises (SMEs) of Pakistan

This paper presents the development of low-cost and robust industrial IoT based data acquisition system primarily focused on domestic manufacturing industries striving to achieve goals and benefits of “Industrial 4.0”. This proposes aims to promote DAQ System integration in traditional manufacturing process of the small and mid-sized industries of Pakistan with limited capacity of investment. Proposed method comprises of Arduino and it’s IoT features for Data Collection, along with a self-developed PC based Centralized Software for Collection of Data, Graphical User Display and Storing collected Data in Local SQL Database. PC based Software replaces requirement of multiple software in case of traditional low-cost DAQ systems, like OPC Software for collecting data from industrial hardware, Java or PHP based any GUI and SQL Data storage. The analysis of work is done with the help of the Message Queue Telemetry Transport (MQTT) protocol. This project will be in further stages evaluated to add features of Supervisory Control, along with Data Acquisition hardware with minimum increase in cost and further upgrading PC Software to add more features of Industry 4.0, as compared to costly commercial solutions available in the market. A machine learning algorithm, k-nearest neighbors algorithm has been used to classify sensitive and non-sensitive data for improvising cloud security. K-Nearest Neighbors is also called KNN algorithm which is supervised machine learning classifier.

Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research.

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.

Low-cost DSP-based vibration data acquisition system for bearing fault detection in electric motors

This paper presents a low-cost DSP-based vibration data acquisition system with a high data storage capacity for fault detection in electric motors. An outer race bearing fault detection case study for a three-phase induction machine using vibration signals provided by the DSP-based acquisition system from a test rig is detailed. The fault detection scheme is performed based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT).

Irradiance Acquisition in Real Time With Long Term Data Logger and Postprocessing Using Data Mining Methods

Nowadays, there has been an increase in the usage of renewable energies, due to the environmental situation in our planet. Among the existing renewable energies, the photovoltaic energy is an attractive alternative. However, it has an intermittent production due to the environmental factors involved in its process. Particularly partial shading can make photovoltaic arrays consume energy instead of producing it. Also, it is useful to register the environmental factors, in order to implement control measurements. For this reason, in order to measure and register real-time temperature and irradiance precisely, a low-cost Data Acquisition System (DAS) is proposed. The implemented DAS has a wireless communication system, which allows the user to have a database through an interface. Subsequently the available power is calculated through a simplified math model, and used along with the irradiance and temperature as attributes. Finally, the data is classified through Data Mining techniques, such as k-means and ID3, and later used to predict whether the bypass diode remains connected or not.

Development of a low cost data acquisition system and training of J48 algorithm for classifying faults in cutting tool

The personalized use of the single-point cutting tool plays an crucial role in the economy of machining operations. Eventually, any machining defect in the cutting tool leads to the deterioration of the complete machining activity. Such faults are not traceable to conventional condition monitoring practices. The characterization of such in-process tooling defects must be approached intelligently. This would also contribute to the “self-monitoring” requirement in Industry 4.0. In this regard, the following is an introduction to a supervised machine learning (ML) classifier for designing empirical classification models for monitoring of tool condition. During the process of turning performed on the lathe, the vibration responses in the time domain of various defective and sound configurations of the single cutting tool were collected. By designing an algorithm which is event-based in Python programming, additional statistical features were extracted. J48 algorithm was used to display the features in the decision tree (DT) for developing data acquisition systems (DAQ). The DT Classifier affords accuracy of as much as 95 % and resulted to be a satisfactory classifier algorithm. The approach of implementation is maximum appropriate for actual time implementation of fault diagnosis. The system is small, low cost and additionally portable.

Low-Cost, Open-Source, Emoncms-Based SCADA System for a Large Grid-Connected PV System.

This article describes a low-cost Supervisory Control and Data Acquisition (SCADA) system for a PV plant with local data logging. Typically, SCADA systems that are available on the market are proprietary (commercial), which are expensive and individually configured for a particular site. The main objective of this paper is to design a low-cost and open-source monitoring solution (hardware and software) to meet the requirements. The hardware used for this SCADA consisted of Arduino, Raspberry Pi, sensors, serial communication cables, and an open-source web view platform. This open-source platform manipulates, logs, and visualizes PV and environmental data. Emoncms runs on the Debian operating system. Field instruments were connected to two remote terminal units (RTUs). A PV array provided data to the RTU1, while an inverter output provided data to the RTU2, and the Raspberry Pi received the collected data in JSON format. As these data arrived, Emoncms used Emonhub as its main module, which refines data and then displays it on Emoncms’s WebView. The Raspberry Pi also stores data locally. Data logging was tested for 6 h, but the final results showed that data logging can last much longer. From an hour to a year, the data trend can be viewed on a user-friendly dashboard.

A Low-Cost Online Data Acquisition and Processing System for Industrial Pollutants’ Monitoring

Recently, the prevention and control of industrial pollution has attracted extensive attention. Many studies based on emission data have achieved great success, but there are relatively few studies on the emission data acquisition. To this end, we propose a data acquisition and processing system (DAPS) for online monitoring of industrial pollutants. The system receives real-time emission data through the RS485, automatically generates periodic data, and saves it in the SQL Server database. Real-time Ethernet (RTE) is used to transmit control signals between the system and programmable logic controllers (PLCs). Emission data is transmitted to local industrial control systems and remote monitoring centers (RMCs) via a wireless network. We tested the acquisition function and communication function of the system through experiments and evaluated the overall performance of the system by observing the memory and CPU usage. The system is characterized by simple implementation and versatility and can be applied to continuous monitoring of various industrial pollutants.

Design and Validation of an Accurate Low-Cost Data Acquisition System for Structural Health Monitoring of a Pedestrian Bridge

Structural health monitoring (SHM) is an effective operating technique devoted to enhance the robustness of an infrastructure, and to validate its safety requirements. The aim of SHM is to determine a structure’s reaction when subjected to any type of excitation, by means of identifying modifications in basic vibration measurements and modal parameters such as natural frequencies, damping and mode shapes. Consequently, sensors are mounted on a structure intending to record data on equal time intervals basis prior to, during and after an induced stimulation. Therefore, the necessity to adopt a computer-based data acquisition (DAQ) technique is required in this analytical approach in order to evaluate vibrational signals collected by sensors placed on a structure. In this work an accurate microcontroller-based DAQ system is proposed to monitor a pedestrian bridge located in Athens Greece for the purpose of characterizing the system state and evaluate the modal properties of the investigated structure. Four low-cost yet accurate triaxial accelerometers were systematically placed along the bridge intending to report the system response toward different generated perturbations. The proposed monitoring and computational system was tested in laboratory conditions prior to the bridge assessment. Three triaxial accelerometer were installed on a steel cantilever beam. A comparative analysis between the results of the suggested DAQ system and that of the standard laboratory DAQ system National Instrument DAQ was performed to test the accuracy of the suggested framework.

A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping

Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, and physiological processes with the target of achieving higher crop yields by the proper crop management. The assessment can be done by utilizing two- and three-dimensional image reconstructions of the inhomogeneities. The quality of the reconstructed image is required to maintain a high accuracy and a good resolution, and it is desirable to reconstruct the images with the lowest possible noise. In this work, an electrical impedance tomography (EIT) data acquisition system is developed for the reconstruction and evaluation of the inhomogeneities by utilizing a non-destructive method. A high-precision EIT system is developed by designing an electrode array sensor using a cylindrical domain for the measurements in different planes. Different edible plant slices along with multiple plant roots are taken in the EIT domain to assess and calibrate the system, and their reconstructed results are evaluated by utilizing an impedance imaging technique. A non-invasive imaging is carried out in multiple frequencies by utilizing a difference method of reconstruction. The performance and accuracy of the EIT system are evaluated by measuring impedances between 1 and 100 kHz using a low-cost and rapid electrical impedance spectroscopy (EIS) tool connected to the sensor. A finite element method (FEM) modeling is utilized for image reconstruction, which is carried out using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). The reconstruction is made successfully with the optimized results obtained using Gauss–Newton (GN) algorithms.

Development of Ultra Low-Cost Data Acquisition System (DAS) for Developing Countries

The Data Acquisition System (DAS) is an important equipment in measurement systems especially in weather monitoring and also very crucial in many areas such as agricultural, communication, transportation, sport and industrial processes. This work proposes a weather monitoring system based on an Arduino Mega 2560 microcontroller board. The microcontroller has the ability to monitor, record and display the information of atmospheric sensor parameters connected to its analogue and digital pins. The outputs of analogue sensors are connected to the microcontroller through separate ADC for sampling. An LCD attached to the microcontroller displays the measured parameters. For analysis, the data is stored in excel format and can be copied directly from microSD card. The DAS was tested and examined through intensive experimental work. The tables and plots obtained from the experiment for standardizing the sensors, it is clear that there is a close conformity between the data collected by the developed system and the existing standard systems. The need for extra cost baud rate and expensive third-party computer software for interfacing to download data from the logger have been eliminated. There is no need of internet for any linking or configuration. It is evident that the DAS is of better choice in terms of cost and maintenance. The system will be helpful in monitoring and recording of atmospheric parameters.
 HIGHLIGHTS
 
 Only a few weather monitoring stations in Nigeria are functioning as a result of the high cost, maintenance and repair services of imported DAS systems. Also, imported DAS devices are often not suitable for the tropical environment
 Other problems include: proprietary software that lacks flexibility, the necessity for third-party software to extract data and difficulties in getting usable parts and unexpected changes in production designs
 Developed DAS device offers a higher resolution compared to existing devices
 Low-cost and flexible meteorological DAS system with ease of installation
 Convenience in deployment for weather data acquisition and research purposes in relevant fields
 
 GRAPHICAL ABSTRACT