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The data acquisition system is a system which collects the analog signal from the sensor, converts it into digital signal, then sends it to the computer for processing, and outputs the processing results in the form of demand. Based on the research of USB technology, this paper describes a data acquisition system based on data fusion technology, including hardware design, firmware design, device driver design and host application design. In the part of hardware design, this paper first describes the performance and characteristics of data acquisition chip, FPGA and USB2.0 interface chip, and then gives the specific hardware design scheme. In the part of firmware design, this paper first introduces the firmware architecture of FX2, and then introduces the firmware design of GPIF interface mode in detail. In the driver development part, this paper first introduces the WDM driver development model, and then completes the USB device driver design of the data acquisition system. Finally, the device performance data acquisition system based on data fusion method is completed with driver.

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second‐order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources. Information processing ability of physical reservoirs utilizing electrochemical reactions in faradic current is investigated. Strong redox reactions of the acidic molecule exhibit advantage for the interpretation of periodic signals. Distilled water exhibits great computing ability for solving higher‐order nonlinear problems. The simplicity of such a system will blaze a trail for developing computing systems based on electrochemical ions reactions.

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.

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system’s characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system’s performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.

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.

The paper presents several theories related to definitions of powers and power factors in non-sinusoidal and non-symmetrical regimes. The theories must meet some requirements: (a) to facilitate the measuring of power quantities by using acquired electrical waveforms; (b) to support the correct quantification of powers and power factors for a fair charge; (c) to support solutions for efficient compensation of non-sinusoidal and non-symmetrical regimes, simultaneous with the power factor compensation along the fundamental harmonic. Only theories meeting the above-mentioned requirements are approached. Aspects specific to power definitions are discussed and commented. Three theories rely on the Fourier decomposition of non-sinusoidal waveforms, valid only for steady signals, whilst the fourth relies on the Discrete Wavelet Transform (DWT) and can also be applied to unsteady signals. Dedicated original data acquisition systems were used to acquire experimental data for three case studies. Data were analysed with original software tools, based on the Fast Fourier Transform and Discrete Wavelet Transform, implementing the approached theories. Comparisons between results yielded for analogue quantities proved that the approached theories satisfy the requirements for which they were created, except for the fourth theory, which can be used only for compensation purposes.

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.

In order to solve the problem of high precision acquisition of weak signals, a high accurately weak signal acquisition system has been designed in this paper. According to the proposed design requirements of the data acquisition system, the hardware acquisition circuit and the drive circuit as well as the processing of the upper computer are divided into modules. As the central work of this paper, the hardware and software of the drive modules are designed with specific flowcharts are given. And the designs of the system were debugged, analyzed and tested through experiment tests, and the high precision data acquisition of 0.5V DC signal has been obtained with specific datum are provided. The effectiveness of the proposed design to data acquisition of weak signal has been validated by the experimental results, and this design could be used in engineering projects.