Search Results (127)

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

This paper investigates whether event-level pitch-bearing fatigue damage can be estimated directly from turbine measurements, and whether these mechanical damage metrics leave measurable fingerprints in the generator DC-link voltage and current. To achieve this, a case study was performed using SCADA and structural load data from the 45 kW Chalmers (Björkö) research turbine. This data was segmented into 223 park-run-park pitch events. For each event, blade-root flapwise and edgewise bending moments were converted into radial and axial loads at the pitch bearing; an equivalent dynamic bearing load $P_{\mathrm{eq}}\left(t\right)$ was reconstructed using SKF and DG03 formulations; and rainflow counting with an S–N curve and Palmgren–Miner’s rule was used to compute event-level damage indices compatible with the International Standard Organization basic rating life concepts. In parallel, DC-link voltage and current were summarized into time-domain features, combined with operating-condition descriptors, and clustered using PCA-based k-means. The resulting clusters captured distinct electrical regimes that, across several event batches, corresponded to different levels of accumulated fatigue damage: regimes with sustained high DC-link voltage and longer duration tended to exhibit higher mean damage indices than lower, steadier DC regimes, indicating an electromechanical link. The results show that physics-based lifetime estimation and unsupervised analysis of existing electrical traces can be combined into a hybrid workflow for pitch-bearing condition assessment without additional sensors. Full article

Surface discharge-induced degradation poses a significant threat to the operational reliability of high-voltage insulation systems. This research investigates the dielectric endurance of polyester-based nanocomposites reinforced with seven distinct nano-additives: iron oxide (Fe₃O₄), copper oxide (CuO), titanium oxide (TiO₂), aluminum oxide (Al₂O₃), silicon dioxide (SiO₂), zinc borate (ZnB) and graphene oxide (GO). Specimens were fabricated at 0.5% and 0.75% weight concentrations and subjected to constant AC electrical stress of 4.5 kV at 50 Hz until failure using the first-plane tracking method. To accurately monitor the aging process, a sophisticated signal processing framework involving Hankel-matrix-enhanced Robust Principal Component Analysis (RPCA) was developed to extract high-frequency discharge features from captured leakage current signals. The degradation characteristics were quantified using various statistical metrics, including Kurtosis, RMS and Burst Discharge Index (BDI). Experimental findings demonstrate that the incorporation of nanoparticles significantly extends the time-to-failure compared to neat polyester, although the effectiveness is highly dependent on both additive type and concentration. At 0.5 wt.%, ZnB exhibited the superior performance in delaying carbonized track formation. However, at 0.75 wt.%, Al₂O₃ emerged as the most effective additive, achieving a maximum endurance time of 31.61 min. In contrast, certain additives like TiO₂ showed a performance decline at higher loadings, likely due to nanoparticle agglomeration. The Hankel-RPCA methodology successfully isolated discharge-specific signatures from background noise, establishing a strong correlation between signal features and material failure stages. This study confirms that the synergy between advanced nanomaterial modification and robust signal processing provides an effective diagnostic tool for monitoring insulation health, offering a vital pathway for the designing of high-performance dielectrics for real-world power system applications. Full article

This study presents the first watershed-scale assessment of the impact of thermal spring discharges on the hydrochemistry and water quality of the Soummam basin (northeastern Algeria). Fourteen stations were monitored during three campaigns (October 2024, December 2024 and March 2025), combining physicochemical analyses, hydrochemical diagrams, and water quality indices (WQI and IWQI). The results reveal a clear spatial gradient in water composition, from low-mineral Ca-HCO₃/Ca-SO₄ facies in upstream areas to highly mineralized Na-Cl facies associated with thermal springs (Sidi Yahia and Sillal). Electrical conductivity reaches up to 27,359 µS/cm, reflecting intense mineralization driven by evaporite dissolution and deep water–rock interaction. This thermomineral signature propagates downstream through mixing and ion exchange processes, leading to progressive salinity enrichment. Water quality indices highlight significant degradation in thermally influenced zones, with approximately 50% of samples unsuitable for drinking (WQI > 300) and more than 60% classified as highly restricted for irrigation (IWQI < 40). Cluster analysis further confirms the distinction between severely impacted, moderately affected, and relatively preserved waters. Overall, the findings demonstrate that thermal discharges represent a major and persistent driver of salinization, emphasizing the need to incorporate geothermal influences into water resource management strategies in semi-arid environments. Full article

Niobium is a strategic critical mineral that supports emerging energy and high-end manufacturing. The geophysical parameters of carbonatite-alkaline rock-type niobium deposits constitute essential baseline data for regional geophysical exploration and prospecting target delineation. To clarify the geophysical response characteristics and exploration the significance of the Dagele niobium deposit in the Eastern Kunlun Region (western China). This study focuses on drill hole ZK3202. Samples from ore bodies, mineralized zones, and wall rocks of different lithologies were continuously measured. Combined with 1001.8 m of full-hole core digital logging data, statistical methods, including box plots, histograms, multi-parameter cross-plots, and correlation coefficient analysis, were applied to quantitatively investigate the physical property responses of lithologies such as calcite-biotite rock (ore body), calcite-bearing pyroxenite (mineralized zone) and amphibolite in the vertical profile. Lithological identification thresholds were established to divide the drill-hole into lithological and mineralized ore layers. The results show that the ore-bearing lithofacies exhibit a distinctive geophysical signature characterized by high density, strong magnetism, medium-low resistivity, high polarizability, and slightly elevated natural radioactivity, which clearly distinguishes them from surrounding from wall rocks. Based on five key parameters—density, magnetic susceptibility, resistivity, polarizability, and natural gamma—a lithological identification model for amphibolite and mineralized altered rock assemblages was established. This study also summarizes the multi-parameter coupling mechanism of ore-bearing lithofacies, which can effectively delineate favorable niobium-bearing horizons. This work fills a gap in the geophysical property characterization of carbonatite-alkaline complex-type niobium deposits in the Eastern Kunlun region and provides data support and regional reference for integrated gravity-magnetic-electrical-radioactive geophysical exploration, prospecting target delineation, and the exploration of similar niobium deposits in western China. Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article

This study investigates the ongoing electrical activity of local neural networks—referred to as neurodynamics—across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5–4 Hz and 33–80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional “fingerprint” for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis. Full article

A water-rich muscle-equivalent tissue-mimicking phantom within a polymeric matrix was experimentally evaluated through a multimodal characterization methodology to determine whether it reproduces the coupled dielectric–thermal behavior of hydrated biological tissue under exposure to electromagnetic waves. The material was analyzed using thermogravimetric analysis, microwave dielectric spectroscopy from 1.5 to 4.0 GHz, VIS–NIR spectroscopy between 350 and 1200 nm, and terahertz time-domain reflection. The thermogravimetric results confirmed dominant water content, with primary mass loss below 200 °C, establishing hydration as the governing factor of its thermal response. Next, the microwave dielectric measurements show that the phantom exhibits a relative permittivity of 37.4 and an electrical conductivity of 2.4 S/m. On the other hand, the VIS–NIR spectra show smooth broadband absorption with limited spatial variability, and principal component analysis reveals macroscopic optical homogeneity without structural spectral distortion. In the THz regime, strong broadband attenuation characteristic of water-rich matrices is observed, and reflection-mode measurements enable robust assessment of temporal stability through time- and frequency-domain signatures. Finally, a microwave thermal validation demonstrates stable behavior under low-power excitation, whereas under hyperthermia-level irradiation, a significant thermal drift of −3.985 °C/h was reached under non-adiabatic conditions, identifying hydration-mediated moisture redistribution as the principal limitation during prolonged high-power exposure. Collectively, these results demonstrate cross-regime dielectric–thermal consistency while explicitly defining the hydration-driven constraints governing long-term stability, providing a validated reference material for broadband electromagnetic and thermal biomedical experimentation. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

Geophysical investigations were conducted utilizing Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) to assess a Non-Aqueous Phase Liquid (NAPL) contaminated site in the southeast of China. Traditional drilling and sampling methods combined with geochemical analysis are limited in deriving reliable spatial interpolations due to low sampling density and high heterogeneity of shallow groundwater. Variations in soil physical properties, such as conductivity and dielectric properties, resulting from NAPL infiltration, provide the physical basis for geophysical detection. While the combined use of ERT and GPR is established in geophysics, its effective application to NAPL sites remains challenging due to complex site conditions and ambiguous signatures. Our ERT results reveal high resistivity anomalies potentially indicative of NAPL contamination, and overlaying GPR attribute analysis (including amplitude, phase, coherence, and texture) onto these results enhances subsurface characterization and anomaly discrimination. The integrated approach demonstrates its capability to clarify subsurface contamination patterns under heterogeneous conditions, providing a spatially continuous interpretation framework that complements sparse direct sampling. Full article

Minimizing cogging torque is critical for surface-mounted permanent magnet synchronous motors (SPMSMs) in high-precision applications, such as electric power steering and robotics. While skewing techniques are typically applied to mitigate cogging torque, anomalous cogging torque harmonics frequently arise in mass production due to manufacturing tolerances and the inherent magnetic anisotropy of non-oriented electrical steel. This paper proposes a systematic analysis approach to distinguish the physical origins of these additional harmonics. By decoupling the effects of geometric and material properties, this study reveals that magnetic anisotropy interacts with slot harmonics to generate a distinct harmonic signature—specifically, the 16th-order harmonic for the 8-pole 12-slot SPMSM. Notably, 3D finite element analysis and experimental validation confirm that this anisotropy-induced cogging torque persists even after applying conventional step-skewing. These findings demonstrate that accounting for magnetic anisotropy is essential for the accurate prediction of cogging torque and the design of low-cogging-torque motors. Full article

In recent years, the digital emulation of power systems, such as induction machines, has increased, driven by advances in computational resources and the processing capabilities of digital platforms. These platforms offer a versatile approach to the design, analysis, and optimization of solutions in electric machine drive research. This work presents the design of an Induction Machine (IM) using a digital twin, simulating its performance and behavior under failure conditions using the DQ model. Additionally, this study presents the design and real-time digital emulation of an IM, incorporating a bearing-fault model. The implementation on an FPGA platform enables high-fidelity simulation and analysis of the machine’s performance under both healthy and faulty operating conditions. This approach introduces a distinctive and critical tool for pre-experimental validation, enabling the precise identification of key fault signatures and system responses under real-time conditions, a capability that is not explicitly addressed in existing studies. Quantitative results demonstrate that the digital model implementation is highly accurate in replicating the theoretical IM with a relative error below (<1%). Additionally, through frequency-domain analysis, the signatures of the injected fault can be observed. Full article

The advent of electric propulsion for new aircraft designs necessitates the optimization of propeller aerodynamic performance and the reduction of acoustic signatures. Variable-pitch propellers present a promising solution, offering the flexibility to adjust blade angles in response to different flight conditions. The study investigates the performance of blade pitch configurations tailored to specific flight conditions. Rather than a dynamic pitch change, the research evaluates discrete pitch settings coupled with corresponding advance ratios to identify optimal operating points. Findings show that increasing collective pitch in response to a higher advance ratio (forward flight) successfully maintains aerodynamic efficiency and thrust, with an expected increase in torque. While this adjustment leads to an anticipated rise in noise due to higher aerodynamic loading, results reveal that a collective pitch increment of $+5°$ actively suppresses broadband noise at frequencies above 2 kHz. Analysis of the flow field and surface pressure fluctuations indicates this suppression is directly attributed to the mitigation of outboard propeller stall. Ultimately, this work demonstrates the feasibility of using collective pitch adjustments not only to enhance flight performance but also to actively control and suppress components of the propeller noise signature, such as the broadband noise. Full article