Search Results (697)

Northwest China is characterized by severe water scarcity, and cloud water represents an important potential supplement to regional water availability. Cloud water path is a key physical parameter for quantitatively characterizing cloud water resources; understanding its variability and associated influencing factors is essential for the scientific assessment and sustainable utilization of regional cloud water resources. In this study, ERA5 and JRA-3Q reanalysis datasets (1960–2025) and satellite cloud products from MODIS and Cloud_cci (2003–2016) were used to evaluate the consistency of multi-source datasets in capturing cloud water path variations. Following the assessment of ERA5 applicability through multi-source comparisons, trend analysis, change-point detection, and ensemble empirical mode decomposition (EEMD) were applied to characterize the multi-timescale variability of cloud water path. Furthermore, the associations of cloud water path variability with climatic and environmental factors, including atmospheric circulation, aerosol optical depth (AOD), global mean surface temperature (GMST) anomaly, and the El Niño–Southern Oscillation (ENSO), were investigated. The results indicated these datasets generally agreed on the temporal variability of cloud water path, whereas differences were found in the absolute ice water path (IWP) and total cloud water path (CWP) values and estimated long-term trends. IWP contributed substantially to CWP variability across most timescales, while the long-term evolution of CWP reflected changes in both liquid water path (LWP) and IWP. LWP, IWP, and CWP showed gradual increasing tendencies, with no significant change points detected. EEMD analysis suggested variability components at approximately 3-year and 7–9-year timescales with relatively large variance contributions, although these components were not statistically significant. Cloud water path variability showed different relationships with climatic and environmental factors. The IWP IMF1 component, with an approximately 3-year timescale, exhibited a weak positive association with the mid-latitude westerly index, whereas no stable linear relationships were detected between cloud water path and summer monsoon or ENSO variability. IWP and CWP showed significant seasonal correlations with AOD, which may largely be attributable to their shared seasonal variations. After detrending, IWP exhibited a weak negative correlation with GMST anomaly. Full article

Accurate vibration prediction is critical for condition monitoring and predictive maintenance of hydropower units, yet it remains challenging due to strong noise interference, severe non-stationarity, and multi-scale coupling characteristics of raw vibration signals. This paper proposes a novel hybrid prediction framework that integrates cooperative denoising, multi-scale signal decomposition, and deep learning-based sequential modeling to achieve high-precision long-term vibration forecasting. First, a two-stage cooperative denoising stategy combining wavelet threshold denoising (WTD) and singular spectrum analysis (SSA) is designed to suppress high-frequency noise while effectively preserving the global trend and critical transient features. Then the denoised signal is decomposed into a set of physically interpretable intrinsic mode functions (IMFs) and a residual component via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), which alleviates mode mixing and improves decomposition completeness. Subsequently, each IMF component is independently predicted using a gated recurrent unit (GRU) network optimized by the Adam algorithm with adaptive learning rate decay, enabling efficient capture of nonlinear temporal dependencies. The proposed framework is validated using 3.5-year real-world vibration data from an lower guide bearing of a pumped-storage hydropower unit. Experimental results demonstrate that the model achieves MAE = 0.3831, RMSE = 0.6964, MAPE = 0.2832%, and $R^2=0.9911$, compared to 0.6666 for the conventional CEEMDAN-GRU model, a 32.45 percentage point increase and a 54% reduction in unexplained variance. Ablation studies and comparative analyses verify the superiority and statistical significance of the cooperative denoising mechanism and the overall hybrid architecture. This work provides a reliable, interpretable, and deployable tool for the condition monitoring and predictive maintenance of hydropower units, supporting proactive operation and reducing unplanned downtime in clean energy systems. Full article

Fault feature extraction for rotate vector (RV) reducers is hindered by weak impulsive components masked by noise, empirical parameter selection in conventional variational mode decomposition (VMD), and mode redundancy. To address these issues, an improved wild horse optimizer-based variational mode decomposition (IWHO-VMD) framework with effective-mode reconstruction is proposed. A two-stage search strategy and a composite fitness function integrating squared envelope spectrum (SES) negentropy, reconstruction error, and an effective-mode penalty are used to optimize the VMD mode number and penalty factor. Fault-related intrinsic mode functions are then selected using SES negentropy and normalized energy ratio, followed by effective mode reconstruction and Hilbert envelope spectrum analysis. The method is validated on two self-built RV reducer datasets involving rolling-element wear and planetary gear tooth-surface wear, together with a public crankshaft wear dataset, and compared with several optimization methods. It achieves an average characteristic-frequency identification accuracy of 98.95% across the three datasets. On the comparative dataset, IWHO-VMD achieves the lowest fitness value and reduces the number of convergence iterations by 25.0–50.0%; with effective-mode reconstruction, its average identification accuracy reaches 99.03%. The proposed framework improves VMD parameter adaptivity and enhances fault-feature representation, providing a reliable basis for RV reducer condition monitoring. Full article

In response to the severe wear of centrifugal pumps, Workbench workflow is utilized to adjust the blade inlet and outlet angles, aiming to reduce the wear of the impeller and volute of the centrifugal pump and optimize the pump’s efficiency and head. Orthogonal experiments are conducted by varying the inlet and outlet angles. The original sample points are expanded and optimized in combination with the support vector machine and grid search. The optimization results indicate that under the condition of spherical particles, the efficiency at the rated operating condition increases by 1.71%, and the head rises by 0.35%. The appropriate eddy currents formed by increasing the impeller inlet angle alleviate the particle deposition phenomenon in the centrifugal pump, resulting in a smoother particle flow. The wear of the centrifugal pump blades decreases from 40.76 × 10⁻⁷ mm to 7.77 × 10⁻⁷ mm. After optimization, the overall entropy generation rate of the volute decreases, while that of the blade suction surface and the impeller outlet area increases. Additionally, through empirical mode decomposition analysis, it is found that the optimized design reduces high–frequency interference and the pulsation amplitude, making the flow field more stable. The frequency distribution also shifts from being dominated by high–frequency components to concentrating energy in the medium- and low-frequency regions. Full article

In recent years, advances in exoskeleton technology have increased the demand for human motion classification in terms of both movement diversity and recognition accuracy, making the effective classification of more complex asymmetric movements increasingly important. This study integrated surface electromyography (sEMG) and inertial measurement unit (IMU) signals and employed mutual information (MI) and muscle symmetry features (MSF) to analyze the characteristic differences between symmetrical muscles in both legs during asymmetric movements, with the aim of improving the accuracy of asymmetric motion classification. sEMG and IMU signals were collected from six movements, including three asymmetric postures: asymmetrical stance, single-knee kneeled position, and crouching advance. The acquired signals were processed through energy envelope analysis, active segment extraction, empirical mode decomposition (EMD), feature extraction, MI extraction, and MSF extraction. The relevance based on weight feature selection (RWFS) combined with conditional mutual information (CMI) method was then applied to reduce feature dimensionality, prioritize features with significant fluctuations, and preserve key characteristics. Finally, the CNN-LSTM-Attention algorithm was used for classification. Experimental results showed that fusing sEMG and IMU signals achieved 96.30% accuracy in lower limb motion recognition. The proposed method improves asymmetric movement classification and may provide a potential basis for exoskeleton motion classification in special environments. Full article

The development of Motor Imagery (MI) Brain–Computer Interfaces (BCIs) is systematically constrained by low signal-to-noise ratios (SNRs), signal non-stationarity, and acute data scarcity. While complex Blind Source Separation (BSS) methods optimize signal clarity, their computational overhead introduces propagation delays that challenge real-time constraints. This study addresses this engineering trade-off by introducing a localized architectural framework to evaluate whether a lightweight pipeline operating without BSS (No-BSS) is sufficiently efficient for real-time control when compared against two BSS-equipped pipelines utilizing Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD). Validated across the BCI Competition IV Dataset 2A and the PhysioNet MI dataset, all three pipelines share an identical processing chain designed to maximize efficiency. To mitigate low SNRs, an Adaptive Laplacian spatial filter isolates neural intent across target sensorimotor electrodes (C3, C4, and Cz). Data scarcity is countered via a Gaussian noise injection data augmentation strategy, while session-to-session variability is addressed during feature extraction using Wavelet Packet Decomposition (WPD) paired with a Fisher Score criterion to dynamically isolate subject-specific time-frequency nodes. Redundant features are subsequently eliminated using a Genetic Algorithm (GA) before classification. Experimental evaluation reveals a distinct performance stratification: while the ICA (92.80%) and EMD (92.69%) pipelines yield the highest average accuracy for the PhysioNet dataset by isolating non-stationary and physiological noise, the No-BSS baseline (90.28%) remains the superior framework for the BCI Dataset 2A. Across all pipelines across both datasets, a stable classification hierarchy emerges wherein the Support Vector Machine (SVM) leads performance due to its maximum-margin decision boundary, followed by k-Nearest Neighbors (kNN), a modified EEGNet, and Decision Trees. The No-BSS baseline achieves classification accuracies highly competitive with its BSS counterparts while entirely bypassing their algorithmic overhead. Given the strict latency constraints of live BCI control loops, these findings establish the optimized No-BSS pipeline as a highly viable alternative for low-latency, real-time implementations. Full article

The load characteristics and dynamic behavior of shearer drums under complex geological conditions—particularly coal seams with gangue interlayers, roofs, and floors—remain insufficiently understood due to the lack of systematic comparative studies across varying cutting scenarios. In this study, a three-dimensional finite element model of drum cutting was established using SolidWorks and HyperMesh, and explicit dynamic simulations were performed with LS-DYNA to investigate the triaxial loads (cutting resistance, traction resistance, and lateral force) under five distinct operating conditions. Theoretical calculations of cutting resistance for pure coal cutting yielded 91 kN, while the simulation result was 87.0245 kN, with a relative error of 4.37%, validating the reliability of the numerical model. Results reveal that gangue position exerts a differential influence on load components: upper gangue maximizes traction resistance (mean: 43.01 kN), whereas lower gangue leads to the highest cutting resistance (mean: 38.45 kN). Floor cutting, with the highest uniaxial compressive strength (75 MPa), produces the most severe load fluctuations. To further characterize the nonlinear dynamics, Ensemble Empirical Mode Decomposition (EEMD) coupled with sample entropy analysis was applied to the load signals. The high-frequency intrinsic mode functions (IMF1–2) of the floor-cutting traction resistance exhibited the highest sample entropy values, indicating pronounced impact characteristics and complex non-stationary behavior. These findings provide a quantitative basis for distinguishing cutting media (coal, gangue, roof, floor) and offer actionable insights for drum structural optimization and adaptive cutting control in intelligent mining operations. Full article

Signal representation underpins modern signal processing, yet many existing methods primarily transform signals into alternative domains without explicitly modelling how informative signal structure evolves during recursive localisation. This paper presents the Linear Series Decomposition Learner (LSDL), a multi-geometric theory of signal structure and representation founded on recursive support localisation. The LSDL is formulated as a recursive localisation operator acting on a fixed amplitude-reference domain, thereby establishing a mathematically rigorous framework for analysing the evolution of signal support across successive localisation levels. Theoretical analysis characterises the fundamental properties of the operator, including support evolution, monotonicity, finite recursion, perturbation stability, and admissible localisation, and it thereby provides a formal foundation for recursive signal decomposition. Building upon this operator-theoretic formulation, the proposed framework establishes that recursive support localisation induces multiple complementary geometries of signal structure. These comprise support geometry, which describes the organisation of retained signal support; discriminative geometry, which characterises class separability under recursive localisation; information geometry, which quantifies entropy redistribution and information concentration; persistence geometry, which models the emergence, evolution, and lifetime of localised signal structures across recursive filtrations; and spectral geometry, which describes recursion-induced reorganisation within the frequency domain. Collectively, these complementary geometries provide a coherent multi-geometric representation that captures structural, statistical, topological, and spectral characteristics within a common mathematical framework. The proposed theory is supported through analytical development and empirical evaluation using synthetic benchmark signals, real-world electromyographic (EMG) datasets, and comparative analyses against established signal representation approaches, including the short-time Fourier transform (STFT), wavelet transforms, empirical mode decomposition (EMD), variational mode decomposition (VMD), and sparse coding. Experimental results demonstrate that recursive support localisation produces interpretable multi-geometric representations while maintaining competitive classification performance and low online computational cost. By establishing recursive support localisation as a principled mechanism through which complementary signal geometries emerge, the LSDL provides a mathematically grounded framework for interpretable signal representation, structural analysis, and representation learning across diverse signal-processing applications. Full article

Rolling-element bearings are a leading cause of unplanned downtime in continuous-process manufacturing, and the migration toward Industry 4.0 condition-based maintenance (CBM) has intensified the need for diagnostic methods evaluated on real in-service assets. This paper reports an exploratory, longitudinal single-asset industrial field demonstration of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Hilbert spectral analysis for rolling-element bearing condition monitoring. An in-service bearing on a critical production machine was monitored over eight measurements spanning approximately four months and analyzed with the Hilbert–Huang Transform, using CEEMDAN in place of the classical Empirical Mode Decomposition to suppress mode mixing. The Hilbert spectra tracked the evolution of the bearing’s vibration signature as a growing concentration of vibration amplitude in a stable band of the 0–400 Hz analysis window (approximately 280–380 Hz); because the analysis characterizes the distribution of amplitude within the band rather than resolving discrete defect lines, this is reported as band-amplitude trending, and the band is treated as compatible with bearing-related excitation rather than attributed to a specific kinematic fault frequency. At the final pre-replacement measurement (M8), a bounded consistency cross-check against a conventional single-sided Fast Fourier Transform (FFT) amplitude spectrum computed from the same exported waveform recovered co-located dominant content. This establishes cross-method consistency for the measurement examined, but does not independently validate diagnostic correctness or the full campaign trend. Interpretability of the representation is argued qualitatively and remains to be tested. The contribution is therefore the documented field application and its explicit consistency cross-check procedure; applicability beyond this asset and its operating conditions requires multi-asset evaluation. Full article

A fault-section location method based on waveform-difference analysis of transient zero-sequence currents is proposed for distribution networks with small-current grounding systems. The method addresses weak single-phase-to-ground fault-current amplitudes, indistinct fault features, and limited adaptability of steady-state-quantity-based location criteria under arc-suppression-coil compensation. Steady-state and transient characteristics of single-phase-to-ground faults were analyzed in neutral-point ungrounded systems and arc-suppression-coil-grounded systems. Transient zero-sequence currents exhibited more pronounced amplitude, polarity, and waveform differences than steady-state zero-sequence currents, with lower sensitivity to compensation effects. These characteristics provide effective features for fault-section discrimination. The discrete Fréchet distance was used to quantify the overall waveform difference between transient zero-sequence currents at both ends of each line section. The coefficient of variation was introduced to characterize the dispersion of waveform differences among different sections. A fault-section location criterion was then constructed. The location performance was verified through PSCAD/EMTDC–MATLAB co-simulation under different neutral-point grounding modes, transition resistances, and fault inception angles. The results show that the proposed method accurately identifies fault sections and maintains good adaptability under both distinct and weak fault-feature conditions. Compared with the correlation-coefficient method, grey relational analysis, and empirical mode decomposition, the proposed method more comprehensively characterizes waveform differences in transient zero-sequence currents at both ends of a line section. It shows good section-discrimination capability under the representative fault conditions considered in this study. Full article

Background/Objectives: Symmetrical dual-site acetylcholinesterase (AChE) inhibitors offer a compelling strategy to mitigate mutational drug resistance, yet static modeling fails to capture induced-fit dynamics under mutational stress. Methods: Here, a 100,000-compound virtual library was filtered using a machine learning-based QSAR classification pipeline. A strict, empirically calibrated Jaccard applicability domain filter (AD = 0.823) eliminated topological anomalies, yielding a robust cross-validation accuracy (ROC-AUC: 0.80 ± 0.05; independent test MCC: 0.61). Multi-parameter ADMET and shape screening prioritized unique chemotypes to probe the 20 Å enzyme gorge. All-atom explicit-solvent molecular dynamics simulations were coupled with 150 ns enhanced-sampling Metadynamics along two orthogonal collective variables (gorge depth and ligand orientation) to map out the free energy surfaces under mutational stress. Results: Symmetrical probes suffered catastrophic unbinding upon anchor loss. Conversely, the symmetrical core of Lead Compound 1631 demonstrated extraordinary structural resilience. In silico site-directed mutagenesis (W86A and W286A) triggered a thermodynamic locking effect; the W86A mutant forced the complex into a deeper energetic well (ΔG_min = 9.23 ± 1.98 kJ/mol) than the wild-type state (5.26 ± 1.69 kJ/mol). MM/GBSA decomposition confirmed an active electrostatic-solvation compensation mechanism along a “solvation see-saw” diagonal (ΔΔG_total = +1.59 kcal/mol). Finally, Dynamic Cross-Correlation Matrix analysis quantified a mechanical inversion of the CAS-PAS axis into an anti-correlated clamping mode (−0.04) that locked the ligand bridge in place. Conclusions: These results demonstrate that symmetrical dual-site targeting, combined with dynamic thermodynamic locking, provides a resilient framework to overcome mutational resistance in AChE inhibitors. Full article

The achievement of continuous improvement in maintenance, and consequently in its overall efficiency, becomes a cornerstone of railway transport systems, particularly when one of the objectives is to increase operational speeds. Therefore, methodologies that enable the early detection of defects in the most critical components of the system—thus ensuring maximum availability of railway rolling stock while reducing maintenance and operational costs—are of paramount importance. Recently, the feasibility of using decision trees to classify the condition of a bogie wheelset has been analysed through the study of vibration signals obtained from the wheelset on a test bench. These signals were, in turn, decomposed into their intrinsic mode functions, to which classical signal processing techniques were applied, yielding excellent results. The present work therefore constitutes both a continuation and a complement to the aforementioned study, with the ultimate aim of establishing a comparison as well as enhancing the methodology, which in itself represents the principal contribution of this manuscript. Full article

Faults in wind farm multi-branch collector lines produce weak transient zero-mode current characteristics, rendering conventional amplitude- and phase-based selection methods ineffective. To address this, the paper proposes a faulty feeder selection method that integrates transient zero-mode differential current with the S-transform. A π-type zero-mode network is first established to reveal the composition and variation in transient zero-mode current under different operating conditions. Theoretical analysis demonstrates a distinguishable feature—the sending-end current of the faulted branch exhibits significantly larger amplitude and opposite polarity compared with non-faulted branches—while the end-side currents near wind turbines remain highly similar and thus hinder accurate selection. Motivated by this disparity, the adaptive time–frequency focusing property of the S-transform is exploited to extract high-frequency energy and differential signatures within the optimal band, from which a dual criterion consisting of the differential feature quantity $D_m$ and the time–frequency energy $E_m$ is constructed. The method is validated on a 35 kV DFIG-based wind farm collector line model built in PSCAD/EMTDC and benchmarked against the wavelet transform and empirical mode decomposition under various operating conditions. The results confirm that the proposed method accurately distinguishes bus faults from branch faults, reliably identifies the faulty feeder, and remains unaffected by fault distance and operating conditions, demonstrating strong robustness and anti-interference capability. Full article

Structural health monitoring (SHM) relies heavily on the real-time extraction of valid information from raw streaming vibration data. However, the widely used traditional variational mode decomposition (VMD) method has two critical technical bottlenecks, which include key decomposition parameters that rely on manual empirical setting with unavoidable subjectivity and batch processing mechanisms that cannot support online real-time denoising of continuous monitoring data. To overcome these challenges, this paper develops a novel Recursive Sliding Bandwidth-Aware Variational Mode Decomposition (RSBAVMD) method and carries out a full set of scientific research. Firstly, we introduce the weighted spectrum trend (WST) method to adaptively divide the signal spectrum into sub-bands and automatically solves the optimal number of decomposition modes and the matched penalty factor for each mode, eliminating the dependence on manual parameter tuning. Then, to enable online processing of streaming SHM data, we introduce an online iterative update method based on recursive sliding Fourier transform, thus breaking the offline application limitation of conventional VMD. We conduct validation experiments using numerical simulated signals and measured structural vibration data. The results indicate that the proposed RSBAVMD method achieves adaptive determination of components and penalty factors. Compared with traditional VMD and mainstream online denoising methods, this method has the best denoising effect. Compared with the VMD method, the proposed method reduces analysis time by about 80% and is less affected by the amount of analyzed data. The proposed RSBAVMD method can satisfy the real-time denoising requirements of streaming vibration data while ensuring accuracy, providing reliable support for online SHM and structural state assessment. Full article

Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work. Full article