Search Results (1,070)

This paper addresses the torque generated in a six-phase permanent-magnet synchronous machine (6PMSM) when it is reused as a three-phase integrated on-board battery charger in electric vehicles. The inequality stator-phase disposition produces unequal equivalent inductances among the windings, which unavoidably creates electromagnetic torque. A novel six-phase open-end winding topology is first introduced: during charging, both sides of every open-winding act as grid-side harmonic filters; under ideal balanced conditions, the two halves carry currents that are equal in magnitude and opposite in direction, so the counter-rotating fields cancel and no net torque is produced. However, this perfect condition is difficult to achieve in the real system in practice. More than a 5% difference (inequality) in stator winding inductance can be observed at different rotor positions. Consequently, a dedicated current-control strategy is developed in order to compensate the unequal inductance, force the winding currents back into balance, and thereby eliminate the undesired torque while providing additional harmonic attenuation. In the proposed charging mode, the system operates at 0.99 power factor with zero average torque and a total grid current harmonic distortion (THD) of 3.47%. Experimental results verify that the proposed topology and control algorithm successfully keep the 6PMSM torque-free even when the machine is operated as grid filter inductance. Full article

All rotating electrical machines are susceptible to vibrations arising from electromagnetic (EM) forces, electrical faults, mechanical defects, imbalance, and structural resonance. In Doubly Fed Induction Generators (DFIGs), such electromechanical vibrations are especially important because they can degrade reliability, increase noise, and lead to severe damage if resonance-prone operating conditions are not identified in time. Although fault diagnosis in DFIGs has been widely investigated using current, voltage, and flux signatures, comparatively fewer studies have examined fault-specific vibration behaviour under stator and rotor interturn faults (ITTFs), particularly through a coupled EM structural framework. In addition, prior vibration-based studies have not examined the influence of end winding ITTFs, its location, severity, and modal interaction investigating resonance risk. This paper considers vibration characteristics of a variable-speed 2.8 MW DFIG used in a grid-connected Type-3 wind turbine unit (WTU) at no-load operating condition. The DFIG is modelled in ANSYS Academic Research v 2022 R2 Maxwell for EM behaviour assessment for ITTFs in both stator and rotor windings along with modal analysis (MA) in ANSYS Workbench to examine the undamped stator and rotor modes over a range of frequencies. This coupled approach enables identification of vibration signatures associated with different ITTF types. The results show the magnetic flux density near faulty end-winding region increases with fault severity and ranges from 4.19 T to 4.39 T in proximity to faulty windings. A dominant modal frequency band of 60–65 Hz is identified, where stator and rotor modes coincide, creating probable resonance conditions. A severe vibration response is observed for single-phase stator ITTF, showing an amplitude of 2116 mm/s at 480 Hz for a larger number of shorted turns, indicating that asymmetric faults can produce stronger EM excitation than multi-phase faults. The main contribution of this paper is demonstration of a fault-specific, MA and vibration-based Condition monitoring system (CMS) implementation workflow for a DFIG. Unlike prior vibration-based studies that primarily focus on general machine vibration, mechanical faults, bearings, etc., this paper links stator and rotor ITTF induced EM excitation to modal characteristics, resonance behaviour, and measurable vibration signatures, establishing vibration analysis (VA) as a practical complementary technique for CMS of ITTFs in DFIGs. Full article

Voltage disturbances occur frequently in power systems. The most important voltage disturbances are voltage unbalance, voltage deviation, and voltage waveform distortions. Voltage waveform distortions are usually considered harmonics, but subharmonics and interharmonics may also occur. Voltage subharmonics are components with frequencies lower than the fundamental frequency. In contrast, voltage interharmonics are components of the frequency spectrum that are higher than the fundamental frequency and are not integer multiples of it. Voltage fluctuations are the superposition of the first voltage harmonic and subharmonic components. This work analysed the shaft torque pulses of an induction motor under single-subharmonic action or under periodic voltage fluctuations combined with voltage unbalance. The experimental results were compared with results from previous work. We also analysed the influence of voltage disturbances on the selection of the coupling connecting the induction motor to the working machine. Full article

Background/Objectives: To date, no validated scoring system can accurately predict the responses of acute myeloid leukaemia (AML) patients to induction chemotherapy (CTX). Current risk assessment relies on complex cytogenetic and molecular abnormalities and focuses on mutations in genes considered fundamental to leukaemogenesis. Methods: We performed bioinformatic analysis of targeted sequencing (TS) data from 111 genes in 1552 AML patients, focusing on mutational patterns derived from single-nucleotide variant (SNV) catalogues. The SNV catalogues were analysed using non-negative matrix factorisation (NNMF), a linear dimensionality-reduction approach, to extract risk-defining recursive signatures (RSs) and to distinguish responders from resistant patients following induction CTX. To enable patient-level prediction, we complemented NNMF with a Random Forest (RF) model. Given the class imbalance between responders and resistant cases, model performance was improved by applying the Synthetic Minority Over-sampling Technique (SMOTE) and by incorporating germline variants alongside somatic mutations. Results: NNMF-derived RSs captured clinically relevant structures in patients’ mutational profiles and clustered patients by treatment response, indicating that the diagnostic targeted sequencing data contain sufficient information for risk stratification and treatment response prediction. At the single-patient level, RF models incorporating balanced data and germline variation improved predictive performance compared with unbalanced somatic-only models. Conclusions: These findings demonstrate that machine learning applied to targeted sequencing data can extract clinically informative mutational structures and improve risk stratification in AML, supporting its potential integration into precision treatment decision-making. Full article

The review presents a comprehensive review of the influence of power quality indicators and operating conditions at industrial enterprises on the technical condition and reliability of electrical equipment. Harmonic distortion, voltage fluctuations and sags, load surges, overvoltages, and voltage unbalance are considered factors that increase thermal, electrical, and mechanical stresses in transformers, induction motors, cable lines, and overhead power lines. It is shown that these disturbances can increase RMS currents, additional losses, hot-spot temperature, vibration, and insulation aging rate, reducing equipment service life and increasing failure probability. The review links power quality disturbances with thermal aging models, remaining useful life assessment, and probabilistic reliability models, including the Weibull distribution. It is established that a correct remaining service life assessment requires considering not only individual disturbances but also the combined influence of voltage and current quality, load conditions, ambient temperature, and humidity. Particular attention is paid to modern monitoring and forecasting technologies, including IoT systems, multi-agent models, machine learning, and predictive diagnostics. These technologies enable the transition from scheduled maintenance to continuous multiparameter monitoring. A structure for quantitative risk assessment and practical recommendations for predictive maintenance of industrial electrical equipment are proposed. Full article

Wireless power transfer (WPT) systems for electric vehicles require reliable foreign object detection (FOD) mechanisms both during and prior to power transfer to ensure operational safety and efficiency. The primary purpose of this study was to develop a foreign object detection system to ensure that no objects are present in the area of magnetic coupling (between primary and secondary coils) prior to initiating power transfer. Conventional FOD techniques based on impedance, visual light, or thermal monitoring provide limited spatial information and are sensitive to coil misalignment. This paper proposes a machine learning-based FOD approach using a planar Magnetic Inductance Tomography (MIT) sensor array that enables spatial electromagnetic sensing for early detection and localisation of conductive foreign objects. A dataset comprising 17,800 measurement frames was collected using a custom STM32-based data acquisition system in the absence of (prior to) power transfer. Likewise, a dataset comprising 300 sets of measurement frames was collected during power transfer, in which each frame contains 120 electromagnetic sensor readings. This capture methodology coincides with the detection requirements of live WPT systems. Four classification models, including Random Forest, Support Vector Machine, XGBoost, and Multi-Layer Perceptron, were evaluated. To enhance robustness against sensor drift and environmental variations, feature-engineering techniques incorporating statistical, temporal, frequency-domain, and derivative-based features were developed. Experimental results demonstrate high detection accuracy under both controlled and real-world conditions. The proposed approach demonstrates the feasibility of integrating machine learning-based MIT sensing into wireless EV charging infrastructure for reliable foreign object detection. Full article

Induction motors are critical in modern industry, powering over 70% of industrial processes. Reliable operation is essential to minimize downtime and ensure production continuity. This paper proposes an integrated multimodal methodology for fault diagnosis and prognosis in induction motors, based on an extended Pearson and Gain feature fusion framework. The approach preprocesses vibration, current, voltage, torque, and speed signals through denoising, normalization, synchronization, and sliding-window segmentation. Over 200 features per window are extracted across time, frequency, envelope, wavelet, harmonic, slip-based, and MCSA domains. A key innovation is correlation-driven multimodal fusion, combining Pearson correlation, spectral coherence, cross-spectral energy, and mutual information to produce Gain-enhanced features with improved discriminative capability. Fault diagnosis is performed using RF, SVM, XGBoost, and MLP models, with time-aware data splitting to avoid temporal leakage. Prognosis employs a continuous Degradation Index (DI) modeled via Gaussian Process Regression for uncertainty-aware prediction, with failure probability and Remaining Useful Life (RUL) estimated from DI thresholds. Experimental results demonstrate that the proposed methodology achieves diagnostic accuracy above 97%, enhances feature relevance, and provides stable long-term prognostic performance, offering a robust framework for predictive maintenance of induction motors. Full article

The success of large neural networks trained for sequential prediction via log-loss minimization over massive and diverse datasets has sparked debate regarding the fundamental limits of this paradigm. While these models are not explicitly programmed to perform planning and search, their behavior increasingly resembles complex reasoning and adaptive problem-solving. This paper reviews a series of theoretical and empirical works, aiming to bridge the gap between the practical success of LLMs and formal theories of computation and intelligence—that is, algorithmic information theory and Universal Artificial Intelligence. Grounded in the framework of memory-based meta-learning, the main argument is that training sequence models to predict the next token across diverse tasks implicitly meta-trains them to perform algorithmic compression, thereby performing (amortized) Bayesian inference over the task in-context. Consequently, when pretrained on a sufficiently rich data distribution, the resulting neural networks behave as if compressing by inferring the generative algorithm producing the observed data. We discuss recent theoretical and empirical evidence demonstrating that this approach can approximate Solomonoff induction in the theoretical limit, match exact Bayesian inference on complex sources in practice, achieve strong compression on out-of-distribution data, and synthesize complex in-context algorithms like chessboard evaluations. As models become more capable and general, the theoretical understanding through the lens of algorithmic information theory, including hard theoretical limits and how far practical models are from them, becomes increasingly relevant. We thus conclude our paper by outlining a number of open research questions to further bridge the gap from well-understood theory to modern machine learning practice. Full article

Controlling deposition parameters is fundamental to obtaining the desired properties of cathodic arc physical vapor deposition (PVD) coatings. Achieving uniform coatings on tools with complex, sharp geometries remains a significant challenge due to localized ion flux concentration. Pulsing the substrate bias is an effective way of controlling deposition energy. However, while widely used in cathodic arc PVD, the relationship between the actual bias waveform, coating integrity on sharp tool geometries, and resulting machining performance has not been systematically established. This study investigates the effect of pulsed bias duty cycle (20% to 90%) and frequency (1 to 20 kHz) on the microstructural evolution, residual stress state, and machining performance of AlTiN coated tools. Real-time oscilloscope measurements demonstrated that system inductance and capacitance significantly distort the ideal bias waveform. Microstructural analysis via Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) cross-sectioning confirmed that all bias parameters generated a dense microstructure. While pulse frequency had no significant influence on micromechanical properties or residual stress states, the duty cycle was the dominant variable. High-energy deposition (90% duty cycle) increased hardness to 33.9 GPa but generated severe compressive residual stresses (−5.2 GPa). This extreme compressive stress led to catastrophic edge delamination on sharp solid carbide endmills. Conversely, a low-energy 20% duty cycle generated a coating with lower hardness (29.4 GPa) and a near-neutral stress state (0.5 GPa), effectively preserving the edge integrity. Unlike the endmills, the turning inserts maintained their edge integrity across all deposition conditions. During the high-speed (350 m/min) dry turning of AISI 304 stainless steel, all evaluated coatings exhibited comparable tool life and cutting forces. Wear progression was characterized by rake cratering, combined with abrasion and adhesion-induced attrition on the flank. The results indicate that tool life in this extreme environment is governed primarily by high-temperature thermo-chemical stability rather than initial room-temperature hardness. Lower-energy pulsed bias deposition therefore represents a robust strategy for coating a wide range of tool geometries, delivering equivalent high-speed machining performance while preventing stress-induced delamination on sharp features. Full article

With the increasing demand for higher efficiency and power density, innovative winding techniques have become crucial in modern electric machines. Hairpin windings are increasingly used in electric machines, particularly in high-current applications. A novel analytical model is proposed to estimate slot leakage inductance in hairpin windings. Traditional models are limited to random windings, which fail to capture the complex mutual inductance between multiple coil layers. This paper derives a generalized model to estimate specific permeance and total mutual specific permeance for the hairpin windings, which are key factors in determining slot leakage inductance. The proposed model is also valid for fractional-pitch windings. The derived analytical model is validated through finite element analysis (FEA) on an electric motor similar to that employed in Tesla Model S. In addition, experimental validation is performed to further validate the proposed model. Furthermore, parametric analysis is conducted to analyze the influence of slot geometry and conductor dimensions on the slot leakage inductance. This paper contributes an accurate method for predicting slot leakage inductance in hairpin windings; this provides electrical machine designers with a valuable tool for precise modeling and optimization for improved efficiency and performance in various applications. Full article

Reliable fault detection and quantification are essential for the operational integrity of electric machines. While traditional current-based analysis relies on harmonic signatures or wavelet-based time-frequency representations, this study investigates modern learning formulations that capture spectral, multiscale, and temporal characteristics of fault-affected signals. Moving beyond conventional models, including our earlier CNN-based approaches, we develop sequence-based and operator-learning architectures within a multi-output formulation for eccentricity fault analysis. Three models are investigated: Mamba for temporal dynamics, the Fourier Neural Operator for global spectral mapping, and the Wavelet Neural Operator for localized multiscale decomposition. Evaluated on induction, salient pole synchronous, and inverter-based reluctance synchronous machines, each model maps stator current waveforms to multiple diagnostic quantities, including voltages, operating conditions, and fault severity. With time-delay embedding, all three achieve low prediction errors, with severity RMSE reaching the ${10}^{-4}$ scale for the induction machine, a notable reduction from the $0.04$ errors of our earlier hierarchical CNN models. These results show that modern sequence-based and operator-learning formulations can broaden machine fault analysis by enabling simultaneous prediction and estimation of multiple aspects of machine condition within a single model. Full article

Chirality is a cornerstone of biological systems and pharmaceutical activity, driving a critical need for rapid and sensitive enantioselective analytical methods. Covalent organic frameworks (COFs) have emerged as versatile porous materials, and their chiral counterparts, chiral COFs (CCOFs), uniquely combine high surface area, pre-designable pores, and a confined chiral microenvironment, making them exceptional platforms for enantioselective fluorescence sensing. This review systematically summarizes recent advances in the construction and application of CCOFs for enantioselective fluorescence sensing. We first outline the primary synthetic strategies for CCOFs, including direct synthesis, post-synthetic modification, and chiral induction. Subsequently, based on the direction of fluorescence signal change upon analyte binding, we classify the sensing mechanisms into three categories: “turn-off” (quenching via static complexation or photoinduced electron transfer), “turn-on” (enhancement through rigidification or suppression of electron transfer), and ratiometric (self-calibrating dual-emission response). Representative examples for the detection of amino acids, amino alcohols, terpenes, and saccharides are highlighted for each mode. Special emphasis is placed on structure–property relationships, such as the synergistic roles of hydrogen bonding, π–π stacking, and framework confinement in amplifying enantioselectivity. Finally, we discuss current challenges and future perspectives, including the rational design of ratiometric sensors, integration into practical devices, and the convergence with machine learning to advance the field of smart chiral sensing. Full article

The design of special induction motors for variable-speed drives in pumping systems is carried out using the Design of induction machines for adjustable-speed drives (DIMASDrive 2022) software, based on the motor efficiency criterion. The quality of a variable-speed drive is fully determined by an innovative criterion of equivalent costs, which takes into account not only the cost and energy efficiency of the drive, but also the costs of compensating for reactive power and distortion power, which characterize the drive’s energy and electromagnetic compatibility with the grid. The MATLAB program enables the calculation of the innovative criterion of the drive’s reduced costs. Currently, the cost component of distortion power compensation is not taken into account in the reduced cost criterion; consequently, the quality of the drive in monetary terms is determined incompletely and is underestimated. A method is proposed for calculating this component and incorporating it into the reduced cost criterion. The presented results were obtained entirely through simulations conducted using validated software. Experimental studies of the prototype will provide the final answer regarding the solution. Full article

This study addresses the need to transform enterprise scientific and technological intelligence (STI) services from a discrete resource-supply model toward a more systematic value-creation approach, an important challenge in the digital transformation of knowledge-intensive industries. As an exploratory qualitative inquiry, this work combines large language model-assisted analysis with grounded theory to examine the construction logic and operational mechanisms of an embedded intelligent STI service system. Drawing on in-depth interviews with STI professionals, a qualitative corpus was analyzed using human–machine collaborative coding to systematically derive and organize key constructs. The findings yield a preliminary three-layer conceptual framework: “supply-demand interactive matching, organizational embedded services, and digital-intelligent platform support.” Specifically, the supply–demand matching layer facilitates targeted alignment through demand insight, dynamic response, and quality closed-loop management; the organizational embedded service layer delivers intelligence through scenario integration, process integration, and responsibility–authority integration; and the digital-intelligent platform support layer enables core capabilities via data element induction, intelligent diffusion, and tacit knowledge conversion. The proposed framework offers an initial, structured perspective on how embedded intelligent STI services may operate, providing a foundational reference for both research and practice in this emerging domain. Full article

Natural products display exceptional chemical diversity and a broad range of mechanisms of action that are not adequately captured by traditional classifications based on target class, pharmacological phenotype, or chemical scaffold. Such classification schemes often lead to fragmented understanding of mechanisms of action, obscuring the unified principles underlying different target systems while failing to recognize the stage-dependent mechanisms exhibited by the same molecule in varying contexts. Here, we propose a unified “space–interface–time” framework to classify the mechanisms of action by examining the physical principles through which natural products reshape the functions of different biomolecules. Within this framework for unifying the classification of natural product mechanisms of action, geometry-driven binding site occupancy and conformational constraints are assigned to the spatial dimension; induction or stabilization of multicomponent complexes and kinetic regulation of state lifetimes are assigned to the interfacial and temporal dimensions, respectively. Finally, we discuss the conceptual and technical challenges of bridging static structural snapshots with dynamic in vivo pharmacology, and highlight emerging opportunities offered by time-resolved structural methods and the integration of molecular dynamics, machine learning, and biophysical workflows for mechanism-guided drug discovery. Full article