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Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %. [ABSTRACT FROM AUTHOR]

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries. [ABSTRACT FROM AUTHOR]

An article describes an early fault diagnostic technique based on acoustic signals. The presented technique was used for a single-phase induction motor. The authors measured and analysed following states of the motor: healthy single-phase induction motor, single-phase induction motor with faulty bearing, single-phase induction motor with faulty bearing and shorted coils of auxiliary winding. A feature extraction method called MSAF-20-MULTIEXPANDED (Method of Selection of Amplitudes of Frequency – Multiexpanded) was discussed. The MSAF-20-MULTIEXPANDED was used to create feature vectors. The obtained vectors were classified by NN (Nearest Neighbour classifier), NM (Nearest Mean classifier) and GMM (Gaussian Mixture Models). The proposed technique can be used for diagnosis of the single-phase induction motors. It can be also used for other types of rotating electric motors. [ABSTRACT FROM AUTHOR]

In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils. [ABSTRACT FROM AUTHOR]

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications. [ABSTRACT FROM AUTHOR]

The authors of this manuscript present development of a prototype protective UV-C half-face mask. The first stage of this study focuses on proposing a UV-C half-face mask design and the second phase investigates the quality of printings, 3D/2D roughness and porousness of three different printed samples of PA12. Development of the protective half-face mask used the non-destructive technology of 3D scanning of the human body by the Ein Scan scanner. As a part of the experiment, three samples were prepared with Sinterit Lisa, EOS Formiga and HP jet fusion printers. SLS and MJF technology were used during the experiment. The experimental observation of the structure of the surface was secured using the Alicona Infinite focus G5 device. The conclusions present the study's results and the authors' recommendations for other developers dealing with the development of the protective face masks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow water. The IoT devices have limited power due to their size. Many routing protocols have been proposed in applications, as mentioned above, in different aspects, but timely action and energy make these a challenging task for marine research. Therefore, this research presents a routing technique with three sub-sections, Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER): Layer-Based Adjustment (LBA-TANVEER), Data Packet Delivery (DPD-TANVEER), and Binary Inter Nodes (BIN-TANVEER). In TANVEER, the path is selected between the source node and sonobuoys by computing the angle three times with horizontal, vertical, and diagonal directions by using the nearest vector-based approach to avoid the empty nodes/region. In order to deploy the nodes, the LBA-TANVEER is used. Furthermore, for successful data delivery, the DPD-TANVEER is responsible for bypassing any empty nodes/region occurrence. BIN-TANVEER works with new watchman nodes that play an essential role in the path/data shifting mechanism. Moreover, achievable empty regions are also calculated by linear programming to minimize energy consumption and throughput maximization. Different evaluation parameters perform extensive simulation, and the coverage area of the proposed scheme is also presented. The simulated results show that the proposed technique outperforms the compared baseline scheme layer-by-layer angle-based flooding (L2-ABF) in terms of energy, throughput, Packet Delivery Ratio (PDR) and a fraction of empty regions. [ABSTRACT FROM AUTHOR]

The present study investigates the effect of TiO2 nanoparticles on the stability of Enhanced Oil Recovery (EOR)-produced stable emulsion. The chemical precipitation method is used to synthesize TiO2 nanoparticles, and their properties were determined using various analytical characterization techniques such as X-ray Diffraction (XRD), High-Resolution Transmission Electron Microscopy (HRTEM), and Field Emission Scanning Electron Microscopy (FESEM). The effect of TiO2 nanoparticles is evaluated by measuring oil/water (o/w) separation, rag layer formation, oil droplet size, and zeta potential of the residual EOR produced emulsion. The laser scattering technique is used to determine the o/w separation. The results showed that spherical-shaped anatase phase TiO2 nanoparticles were produced with an average particle size of 122 nm. The TiO2 nanoparticles had a positive effect on o/w separation and the clarity of the separated water. The separated aqueous phases' clarity is 75% and 45% with and without TiO2 nanoparticles, respectively. Laser scattering analysis revealed enhanced light transmission in the presence of TiO2 nanoparticles, suggesting higher o/w separation of the ASP-produced emulsion. The overall increase in the o/w separation was recorded to be 19% in the presence of TiO2 nanoparticles, indicating a decrease in the stability of ASP-produced emulsion. This decrease in the stability can be attributed to the improved coalescence' action between the adjacent oil droplets and improved behavior of o/w interfacial film. An observable difference was found between the oil droplet size before and after the addition of TiO2 nanoparticles, where the oil droplet size increased from 3 µm to 35 µm. A similar trend of zeta potential is also noticed in the presence of TiO2 nanoparticles. Zeta potential was −13 mV to −7 mV, which is in the unstable emulsion range. Overall, the o/w separation is enhanced by introducing TiO2 nanoparticles into ASP-produced stable emulsion. [ABSTRACT FROM AUTHOR]

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Intelligent Transport Systems (ITS) are anticipated to be one of the key technologies for the next decade and their deployment can benefit from the recent developments in the domain of Visible Light Communication (VLC). Light Emitting Diode (LED)-based low-cost VLC is considered in this work to provide a practical approach towards the implementation of an ITS by addressing the major issues of channel noise, free-space optical multipath reflections and interference from light sources. An analytical model is presented for the proposed Multiple-Input–Single-Output (MISO)-based VLC, and simulations are performed to analyze the performance of the system for various transmission distances. Results show that the proposed optimal receiver for 4 × 1 MISO can provide considerable improvement in the bit error rate for the forward error correction (FEC) threshold of 3.8 × 10−3 in the presence of optical interference, and is suitable to support an ITS with an inter-vehicle transmission approach. The comparison of achieved performance with existing solutions for VLC-based ITS depicts that the proposed framework provides much higher data rates, three times longer transmission distance and improved receiver sensitivity. [ABSTRACT FROM AUTHOR]

An organic Rankine cycle (ORC) system with R123 working fluid has been utilised for generating electricity from low-temperature geothermal resources. The degree of superheated vapour warrants attention to be studied further. This is because the degree of superheated vapour is the last point to absorb heat energy from geothermal heat sources and influence the amount of expansion power produced by the expander. Therefore, achieving high ORC system efficiency requires a parameter of superheated vapour degree. This paper presents an experimental study on a binary cycle, applying R123 as the working fluid, to investigate the effect of variation in superheated vapour degree on the ORC efficiency. Geothermal heat sources were simulated with conduction oil as an external heat source to provide input heat to the ORC system. The temperature high inlet (TH in) evaporator was designed to remain at 120 °C during the experiment, while mass flow rate was adjusted to make superheated vapour variations, namely set at 278, 280, 282, 284, and 286 K. Furthermore, the effect was observed on heat transfer inlet, pinch, heat transfer coefficient, expander work output, isentropic efficiency, expander shaft power, power generation, thermal efficiency, and ORC efficiency. The experimental results showed that the mass flow rate nearly remained unchanged at different degrees of superheated vapour. The ranges of heat transfer inlet, pinch temperature, and heat transfer coefficient were 25.34–27.89 kJ/kg, 9.35–4.08 °C, 200.62–232.54 W/m2·K, respectively. In conclusion, ORC system efficiency can be triggered by various parameters, including the temperature on the exit side of the evaporator. The superheated vapour of R123 working fluid to higher temperatures has caused a decrease in ORC system efficiency due to the decrease in heat transfer inlets, although theoretically, the work total increased. Further investigation has found that the magnitude of the mass flow rate affects the behaviour of the components of the ORC system. [ABSTRACT FROM AUTHOR]