Search Results (182)

High-performance flexible sensors capable of direct integration with biological tissues are essential for personalized health monitoring, assistive rehabilitation, and human–machine interaction. However, conventional devices face significant challenges in achieving conformal integration with biological surfaces, along with sufficient biomechanical compatibility and biocompatibility. This research presents an in situ 3D biomanufacturing strategy utilizing Direct Ink Writing (DIW) technology to fabricate functional bioelectronic interfaces directly onto human skin, based on a novel annealing PEDOT:PSS/PVA composite bio-ink. Central to this strategy is the utilization of a novel annealing PEDOT:PSS/PVA composite material, subjected to specialized processing involving freeze-drying and subsequent thermal annealing, which is then formulated into a DIW ink exhibiting excellent printability. Owing to the enhanced network structure resulting from this unique fabrication process, films derived from this composite material exhibit favorable electrical conductivity (ca. 6 S/m in the dry state and 2 S/m when swollen) and excellent mechanical stretchability (maximum strain reaching 170%). The material also demonstrates good adhesion to biological interfaces and high-fidelity printability. Devices fabricated using this material achieved good conformal integration onto a finger joint and demonstrated strain-sensitive, repeatable responses during joint flexion and extension, capable of effectively transducing local strain into real-time electrical resistance signals. This study validates the feasibility of using the DIW biomanufacturing technique with this novel material for the direct on-body fabrication of functional sensors. It offers new material and manufacturing paradigms for developing highly customized and seamlessly integrated bioelectronic devices. Full article

Heterogeneous photocatalysis has emerged as a versatile and sustainable technology for the degradation of emerging contaminants in water. This review highlights recent advancements in photocatalysts design, including band gap engineering, heterojunction formation, and plasmonic enhancement to enable visible-light activation. Various reactor configurations, such as slurry, immobilized, annular, flat plate, and membrane-based systems, are examined in terms of their efficiency, scalability, and operational challenges. Hybrid systems combining photocatalysis with membrane filtration, adsorption, Fenton processes, and biological treatments demonstrate improved removal efficiency and broader applicability. Energy performance metrics such as quantum yield and electrical energy per order are discussed as essential tools for evaluating system feasibility. Special attention is given to solar-driven reactors and smart responsive materials, which enhance adaptability and sustainability. Additionally, artificial intelligence and machine learning approaches are explored as accelerators for catalyst discovery and process optimization. Altogether, these advances position photocatalysis as a key component in future water treatment strategies, particularly in decentralized and low-resource contexts. The integration of material innovation, system design, and data-driven optimization underlines the potential of photocatalysis to contribute to global efforts in environmental protection and sustainable development. Full article

In this paper, the tensile behavior of the power cable used for charging electric machines was analyzed. It is known that such a cable, consisting of several conductors, polymeric sheaths and textile core wire, can be subjected to mechanical and thermal stresses that lead to failure to operate at the desired parameters or to total interruption of operation. The mechanical stresses to which the cable is subjected are, in general, bending and tensile stresses, with the development of normal stresses, torsional stresses where tangential stresses occur and, possibly, shock stresses produced by several causes. The present paper proposes to determine some mechanical characteristics of the mentioned conductors resulting from tensile stress for testing, using a special device built for this purpose. In order to obtain other mechanical characteristics also, a finite element analysis has been carried out, the results of which are compared with those obtained from the experiment. Another type of determination was also carried out using the tensile device: the variation of the electrical resistance of one of the electrical conductors of the cable during tensile stress was recorded. Full article

This work presents the development of an adaptive thermal protection system for synchronous machines (SMs), taking into consideration the final cooling temperature and the operation point of the machine. This system aims to improve current thermal protections, which consist of a fixed alarm and trip thresholds regardless of the generator’s operating point or ambient temperature. A recurrent neural network (RNN)-based approach has been employed to predict SM temperatures during operation. Multiple tests have been conducted on a specially designed test bench. Inside the windings and iron core of the 5.5 kVA generator, multiple Pt100 sensors have been installed to train the neural network with real temperature values, enabling accurate predictions. The selected RNN model is Long Short-Term Memory (LSTM). Its inputs include electrical variables and the inlet and outlet air temperatures of the SM’s cooling system. The results show that the model accurately defines warning and trip thresholds, significantly improving thermal protection, as these thresholds are no longer fixed values. Additionally, the study suggests validating the model under cooling system failures and exploring its application in water-cooled systems. This research is supported by a patent on real-time thermal diagnostics for synchronous machines, highlighting its potential contribution to predictive maintenance and the monitoring of power generation systems. Full article

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The use of IME in a human–machine interface (HMI) provides a favorable experience to the users and helps reduce production costs due to a smaller list of parts and lower material costs. A few functional components that are compatible with one another are crucial to the final product’s properties in the IME structure. Of these components, the dielectric layers are an important component in the smart surface industry, providing insulation for the prevention of leakage currents in multilayered printed structures and capacitance sensing on the surface of specially designed shapes in IME. Advanced dielectric materials are non-conductive materials that impend and polarize electron movements within the material, store electrical energy, and reduce the flow of electric current with exceptional thermal stability. The selection of a suitable dielectric ink is an integral stage in the planning of the IME smart touch surface. The ink medium, solvent, and surface tension determine the printability, adhesion, print quality, and the respective reaction with the bottom and top conductive traces. The sequence in which the components are deposited and the heating processes in subsequent thermoforming and injection molding are other critical factors. In this study, various commercially available dielectric layers were each printed in two to four consecutive layers with a mesh thickness of 50–60 µm or 110–120 µm, acting as an insulator between conductive silver traces overlaid onto a polycarbonate substrate. Elemental mapping and optical analysis on the cross-section were conducted to determine the compatibility and the adhesion of the dielectric layers on the conductive traces and polycarbonate substrate. The final selection was based on the functionality, reliability, repeatability, time-stability, thickness, total processing time, appearance, and cross-sectional analysis results. The chosen candidate was then placed through the final product design, circuitry design, and plastic thermoforming process. In summary, this study will provide a general guideline to optimize the selection of dielectric inks for in-mold electronics applications. Full article

Current approaches in the process of evaluating the quality of the machined surface during wire electrical discharge machining (WEDM) generally do not include the assessment of micro- and macro-geometric indicators of both parts of the cut. In practice, however, there are specific cases when it is necessary to use both halves of the cut. In such cases, it is necessary to choose a special approach not only in the machining process but also when evaluating the quality indicators of the machined surface. Therefore, experimental measurements were aimed at the identification of these micro- and macro-geometrical indicators in symmetrical WEDM. Within them, qualitative indicators of flat and curved surfaces were assessed. The identification of individual characteristics was carried out using Suftes, Roundtest Mitutoyo, and a 3D coordinate measuring device. The design of the experiment followed the full DoE factorial design method, and the obtained results were processed using the Taguchi method. Based on the obtained results, the response of macro and micro-geometric parameters was characterized by means of multiple regression models (MRM) in symmetrically machined surfaces of tool steel EN X37CrMoV5-1 (Bohdan Bolzano, Kladno, ČR) by WEDM technology. They revealed the mutual dependence of the output qualitative indicators of the eroded area on the input variables’ main technological parameters (MTP). Subsequent multi-parameter optimization resulted in a suitable level of setting of the MTP input variable parameters I, t_on, U, and t_off (9 A, 32 μs, 15 μs, and 70 V), through which the greatest agreement of macro and micro-geometric output indicators of symmetrically machined surfaces can be achieved. By applying the optimized levels of MTP settings for symmetrical WEDM of tool steel EN X37CrMoV5-1, their agreement was achieved at the level of 95%. Full article

This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks. Full article

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics. Full article

The Ecuadorian electricity sector, until April 2024, presented losses of 15.64% (6.6% technical and 9.04% non-technical), so it is important to detect the areas that potentially sub-register energy in order to reduce Non-Technical Losses (NTLs). The “Empresa Eléctrica de Ambato Sociedad Anónima” (EEASA), as a distribution company, has, to reduce NTLs, incorporated many smart meters in special clients, generating a large amount of data that are stored. This historical information is analyzed to detect anomalous consumption that is not easily recognized and is a significant part of the NTLs. The use of machine learning with appropriate clustering techniques and deep learning neural networks work together to detect abnormal curves that record lower readings than the real energy consumption. The developed methodology uses three k-means validation indices to classify daily energy curves based on the days of the week and holidays that present similar behaviors in terms of energy consumption. The developed algorithm groups similar consumption patterns as input data sets for learning, testing, and validating the densely connected classification neural network, allowing for the identification of daily curves described by customers. The results obtained from the system detected customers who sub-register energy. It is worth mentioning that this methodology is replicable for distribution companies that store historical consumption data with Advanced Measurement Infrastructure (AMI) systems. Full article

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to $27.7\%$, and the reduction in Mean Absolute Percentage Error (MAPE) was up to $45.7\%$ compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to $10.0\%$ compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods. Full article

The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge process remain unresolved. This work aims to study the influence of discharge location and bubble expansion on the occurrence of subsequent discharges. A high-speed video camera observation system was constructed to capture images of each discharge. From the acquired images, an algorithm was devised to determine the discharge location based on grayscale analysis. Moreover, the voltage and current waveforms of the discharges and the framing signals of the high-speed video camera were then obtained using an oscilloscope. Synchronizing the observation images and signals allowed for calculating the delay time for each single discharge. The results indicate that most of the discharges occurred near the boundary of the bubble and during bubble expansion. This finding has been observed for a variety of machining conditions and can be explained by the effect of the debris particles concentrated at the bubble boundary. This study provides useful information for better understanding the discharge process in WEDM. Full article

There are numerous methods for detecting interturn faults (ITFs) in the field winding of synchronous machines (SMs). One effective approach is based on comparing theoretical and measured excitation currents. This method is unaffected by rotor temperature in static excitation SMs. However, this paper investigates the influence of rotor temperature in brushless synchronous machines (BSMs), where rotor temperature significantly impacts the exciter excitation current. Extensive experimental tests were conducted on a special BSM with measurable rotor temperature. Given the challenges of measuring rotor temperature in industrial machines, this paper explores the feasibility of using stator temperature in the exciter field current estimation model. The theoretical exciter field current is calculated using a deep neural network (DNN), which incorporates electrical brushless synchronous generator output values and stator temperature, and it is subsequently compared with the measured exciter field current. This method achieves an error rate below 0.5% under healthy conditions, demonstrating its potential for simple implementation in industrial BSMs for ITF detection. Full article

Human–machine interactions (HMIs) have penetrated into various academic and industrial fields, such as robotics, virtual reality, and wearable electronics. However, the practical application of most human–machine interfaces faces notable obstacles due to their complex structure and materials, high power consumption, limited effective skin adhesion, and high cost. Herein, we report a self-powered, skin adhesive, and flexible human–machine interface based on a triboelectric nanogenerator (SSFHMI). Characterized by its simple structure and low cost, the SSFHMI can easily convert touch stimuli into a stable electrical signal at the trigger pressure from a finger touch, without requiring an external power supply. A skeleton spacer has been specially designed in order to increase the stability and homogeneity of the output signals of each TENG unit and prevent crosstalk between them. Moreover, we constructed a hydrogel adhesive interface with skin-adhesive properties to adapt to easy wear on complex human body surfaces. By integrating the SSFHMI with a microcontroller, a programmable touch operation platform has been constructed that is capable of multiple interactions. These include medical calling, music media playback, security unlocking, and electronic piano playing. This self-powered, cost-effective SSFHMI holds potential relevance for the next generation of highly integrated and sustainable portable smart electronic products and applications. Full article

An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied. Full article

Assessment of the current state of the near-surface part of the geological environment and understanding of its layered structure play an important role in various scientific and applied fields. The presented work is devoted to the application of phasometric modifications of geoelectric control methods to solve the problem of the detailed complex study of the underground layers of the environment in the process of drilling operations with the use of special equipment. These studies are based on the analysis of variations in phase parameters and characteristics of an artificially excited multiphase electric field to assess poorly distinguishable details and changes in the layered structure of the medium. The proposed method has increased accuracy, sensitivity and noise proofness of measurements, which allows for extracting detailed information about the heterogeneity, composition and stratification of underground geological formations not only in the zone where the drill makes contact with the medium, but also in the entire control zone. This paper considers practical mathematical models of phase images for basic scenarios of drill penetration between the layers of the near-surface part of the geological medium with different characteristics, obtained by means of approximation apparatus based on continuous piecewise linear functions, and also suggests the use of modern machine learning methods for intelligent analysis of its structure. Studying the phase shifts in electrical signals during drilling highlights their value for understanding the dynamics of soil response to the process. The observed signal changes during the drilling cycle reveal in detail the heterogeneity in soil structure and its response to changes caused by drilling. The stability of phase shifts at the last stages of the process indicates a quasi-equilibrium state. The results make a significant contribution to geotechnical science by offering an improved approach to monitoring a layered structure without the need for deep drilling. Full article