Search Results (18,216)

The spread of unmanned aerial vehicles in daily operations makes the early and reliable diagnosis of propeller faults necessary. However, the performance values reported for such systems are usually obtained with sample level splits, and it is not known which feature representation should be matched with which learner. In this study, a leakage-free feature–model matching framework is presented for propeller fault classification. Microphone and six-axis inertial measurement unit data have been collected on a test bench with 980 kV and 1400 kV motors for one healthy and eight faulty propeller conditions at 16 throttle levels, and 8490 windows of 1 s have been extracted from 1735 measurement files. Four scalar feature sets and three time-frequency representations have been matched with seven ensemble learners and three compact convolutional networks under a file atomic split, and the permutation ranking of the best model has been returned to the feature selection stage. The highest macro-F1 value of 0.8027 and an accuracy of 0.8816 have been obtained with the stacked ensemble trained on the 52 input subset ranked by explainability. It is seen that the time domain statistics and the accelerometer axes are dominant, that three inertial axes reach a macro-F1 of 0.7661, and that cepstral and envelope features stay below the Welch-based features at the sampling rate of 90.9 Hz. The cross-motor experiments have shown that the models depend strongly on the motor class, and the McNemar test has confirmed that the difference between the ensemble branch and the compact convolutional branch is not accidental. In this way, the framework can be used as an evaluation protocol for low-cost multisensor setups on low-level devices. Full article

Reliable discrimination between power swings and short-circuit faults is essential for secure transmission line protection, since misclassification may lead to unnecessary tripping or delayed fault clearing. Conventional power swing blocking techniques, particularly those based on impedance trajectory analysis, often require system-dependent settings and may exhibit reduced reliability under dynamic operating conditions with increasing renewable generation penetration. This paper proposes a setting-independent method for power swing and fault discrimination based on the Second Central Moment (SCM) of normalized instantaneous voltage and current signals. In this context, setting-independent means that the method does not require line-specific protection settings or case-by-case threshold tuning, although nominal voltage, nominal current, system frequency, and sampling frequency are required for signal normalization and sliding-window implementation. The SCM provides a statistical measure of signal dispersion that enables classification into three operating states: steady-state operation, power swing conditions, and fault events. Common SCM decision boundaries are applied without adjustment across the evaluated transmission lines, operating conditions, fault characteristics, power-swing frequencies, and levels of inverter-based resource penetration. The proposed method is validated through time-domain simulations using the Kundur two-area benchmark system and the IEEE 14-bus network under a wide range of disturbance scenarios, including oscillatory conditions, symmetrical and asymmetrical faults, renewable integration, and swing–fault sequences. For benchmarking purposes, the SCM-based algorithm is compared with a commercial Swing Center Voltage (SCV)-based blocking scheme widely implemented in digital relays. The results show that the proposed method achieves reliable swing–fault discrimination with low computational complexity while providing earlier blocking activation under slow oscillatory conditions and inherent fault discrimination capability. These characteristics support its practical application in real-time transmission line protection. Full article

Shale gas development in China is dominated by high-pressure gas reservoirs occurring in deep horizons in Sichuan Basin and its peripheral areas. With the progress of exploration and development technologies, medium-shallow normal-pressure shale gas represented by Anchang Syncline in Guizhou has also realized commercial operation. To address the problems of overall low production and high gas breakthrough flowback ratio of medium-shallow normal-pressure shale gas, this paper systematically analyzes its development performance laws based on the complex tectonic setting and the reservoir characteristics of “four lows and one high” of the Anchang Syncline. A series of key technologies were developed, including the static–dynamic iterative identification technology for faults and micro-amplitude structures in complex tectonic areas, the coupled iterative fine modeling technology of geology-development dual chain, the iterative optimization technology of fractures and simulation parameters, as well as low-cost drilling-completion and drainage-production process technology. The application of the above technical suite achieved commercial development of normal-pressure shale gas. By the end of 2024, 66 wells had produced 6.5 × 10⁸ m³ cumulatively, and the well Estimated Ultimate Recovery (EUR) is 2000–4000 × 10⁴ m³. It was clarified that geological conditions constitute the intrinsic basis of gas well production capacity, and engineering factors such as horizontal section length, well type selection, fracturing matching degree, and drainage-production timing are the key to production enhancement. The established development technical system and practical experience for normal-pressure shale gas provide an important reference for the efficient development of analogous gas reservoirs. Full article

Based on a fixed-time extended state observer (FESO), this paper proposes a novel predefined performance control (PPC) method for robotic manipulators with false data injection (FDI) attacks and actuator faults. First, a mathematical model of robotic manipulators with parameter uncertainties and external disturbances is constructed. Then, to compensate for the FDI attacks and actuator faults, an extended state is introduced such that the FESO is designed. Moreover, to avoid the transformation from nonlinear constraints to unconstrained variables in PPC, the barrier Lyapunov function (BLF) is introduced. By adopting the novel PPC, tracking errors of the robotic manipulators are driven into a predefined region. Finally, simulations on a two-degree-of-freedom manipulator demonstrate that, compared with FTESO-based sliding mode control, the proposed method has shorter settling times and better tracking accuracy. Full article

Recent advances have been achieved in intelligent fault diagnosis of rolling bearings. However, noise interference and data imbalance remain critical challenges for achieving accurate and reliable fault diagnosis in practical industrial applications. To address the limited robustness of conventional deep learning models under noisy conditions and their bias toward majority classes in imbalanced scenarios, this study proposes a robust bearing fault diagnosis method based on an adaptive weighted heterogeneous ensemble learning framework. The proposed method begins with continuous wavelet transform (CWT), which is employed to preprocess raw vibration signals and convert them into time–frequency images. Subsequently, a residual convolutional denoising autoencoder augmented by the convolutional block attention module is developed, namely CBAM-RCDAE. CBAM-RCDAE is capable of effectively reducing and eliminating noise interference in two-dimensional image data, thus enhancing fault diagnosis accuracy. Furthermore, a heterogeneous ensemble learning framework consisting of three base learners, including Swin Transformer, a multi-scale convolutional neural network, and BiLSTM, is developed to enhance generalization capability. An adaptive weight selection (AWS) strategy is introduced to adjust the weights and aggregate the outputs of the three base learners for final fault classification. The proposed method is extensively evaluated on the PU and CWRU bearing datasets. Experimental results demonstrate that, under the most challenging imbalanced conditions, the proposed method improves the G-mean metric by 5.89% and 4.95% compared with the state-of-the-art methods on the PU and CWRU datasets, respectively. In addition, the proposed method exhibits superior noise robustness, enabling reliable fault diagnosis performance across various noise levels. Full article

Induction motors play a vital role in industrial operations; however, stator inter-turn short-circuits faults remain a common and critical source of failure. This paper presents a machine learning-based diagnostic approach for detecting stator inter-turn short-circuit faults in three-phase induction motors operating at 50 Hz. Secondary data from a controlled test bench were processed using Power Spectral Density to determine energy distribution and guide the design of a Butterworth bandpass filter (20–350 Hz). The filtered signals were then analyzed using the Hilbert Transform to extract statistical features, which were ranked using the Minimum Redundancy Maximum Relevance algorithm to identify the most discriminative parameters. Two supervised classifiers, Support Vector Machine and Random Forest, were developed and validated using MATLAB’s Classification learner app with 5-fold cross-validation. The Support Vector Machine achieved an accuracy of 94.19%, while the Random Forest model achieved 99.51% with macro and F1-scores of 0.9951 and near-perfect area under the curve values. The results confirm that the Random Forest classifier provides superior generalization, sensitivity, and robustness in fault detection compared to Support Vector Machine. This study successfully demonstrates that combining Power Spectral Density, Hilbert Transform, Minimum Redundancy Maximum Relevance, and ensemble learning yields a highly effective framework for predictive maintenance and reliable fault diagnosis in industrial motor applications. Full article

The hybrid operation of grid-following (GFL) and grid-forming (GFM) renewable energy units can lead to phase-reference inconsistencies and distorted transient impedance trajectories, which in turn cause maloperation or failure-to-operate of conventional distance protection. To address this issue, this paper proposes a novel distance protection method based on transient impedance fitting. First, a dynamic phase transformation is applied to map the currents of GFL units into a unified reference frame, enabling consistent representation of heterogeneous currents from the hybrid renewable energy station. Second, a transient equivalent impedance model is established based on the transient voltage-current relationship of the transmission line, revealing the influence mechanisms of the rates of change in current amplitude and phase angle on the transient additional impedance. Finally, the magnitude of the transient impedance within a short post-fault data window is selected as the fitting object. The least-squares method is employed to extract the linear fitting slope and intercept, which characterize the evolution trend and initial position of the transient impedance trajectory, respectively, thereby forming the criteria for distinguishing internal and external faults. Simulation results demonstrate that the proposed method correctly identifies fault sections under various fault locations, transition resistances, and renewable power output conditions, with the protection decision completed within 15 ms. The proposed method effectively overcomes the susceptibility of conventional distance protection to maloperation and failure-to-operate in scenarios with high renewable energy penetration. Full article

Underground water hazards are among the most serious concealed threats to safe coal mine production, yet their accurate spatial localization remains challenging when relying on a single geophysical method. This study proposes a joint detection framework integrating the magnetotelluric (MT) method and the microtremor survey method (MSM), and applies it to the Shaping Coal Mine in Lianyuan City, Hunan Province, China. The MT method was used to image the resistivity structure of water-bearing bodies at depths of 50–500 m, while the MSM delineated the shear-wave velocity structure of shallow-to-middle strata (0–300 m). A comprehensive identification criterion based on the spatial superposition of “low resistivity + low velocity” anomalies was established. The MT results identified low-resistivity water-bearing structures within the coal-bearing Ceshui Formation, while the MSM revealed reduced-velocity anomalies, some 30% below the background value of the host unit, associated with the Coal Seam No. 5 goaf and fault fracture zones. The two methods formed an effective overlapping detection zone at depths of 100–300 m, with anomalies distributed above the mined-out area and near the Sifangqiao reverse fault, showing strong correspondence with documented water seepage. Within this overlapping window the two independently inverted anomaly sets show a spatial coincidence, expressed as the intersection-over-union of their footprints, of more than 0.85, and the principal seepage points recorded in the roadways fall inside the jointly delineated anomalies. The framework is an integrated interpretation of two independent inversions rather than a mathematically coupled joint inversion, and it provides supporting geophysical evidence for water hazard prevention and control in geologically complex coalfields. Full article

The Lianggaoshan Formation in the Gongshanmiao block of the Sichuan Basin is characterized by narrow channel sand bodies and thin layers, resulting in weak seismic responses, which leads to poor identification of small-scale fault–fracture systems. Initial high-density 3D seismic data exhibit strong shallow surface waves and significant shot-to-shot variations in energy and frequency, necessitating amplitude-preserving noise attenuation and broadband wavelet consistency processing. First, pre-stack multi-domain amplitude-preserving noise attenuation is applied, integrating surface-wave forward modeling, stationary wavelet transform, and matrix singular value decomposition to suppress complex noise. Next, robust deconvolution constrained by a target wavelet improves broadband consistency. Subsequently, anisotropic depth-domain velocity modeling and imaging under rugged topography are conducted using a well-constrained TTI initial velocity model and full-azimuth angle-domain grid tomography. Compared with conventional data, the processed high-density data significantly enhance bandwidth, structural imaging, and thin-layer resolution. Imaging continuity of small faults (6–10 m throw) is markedly improved, and the channel characterization accuracy of the Liang-2 Member increases from 180 m to 60 m. This workflow delivers high-SNR, high-resolution, and high-fidelity results, providing a reliable basis for thin-sandbody prediction and reservoir evaluation. Full article

This systematic narrative review examines recent advances in the application of swarm robotics and swarm intelligence to smart grid systems in the context of renewable energy sources. Smart grids represent a transformative paradigm in power systems, integrating advanced communication, monitoring, and control technologies to enhance the reliability, performance, and sustainability of power distribution. Swarm robotics, drawing inspiration from the collective behaviour of social insects, enables the coordination of numerous autonomous agents to carry out complex tasks in a decentralised, scalable, and fault-tolerant manner. Over the past decade, swarm intelligence approaches—including Particle Swarm Optimisation, Ant Colony Optimisation, and consensus-based distributed control—have emerged as promising methods for addressing smart grid challenges such as decentralised energy management, fault detection, infrastructure monitoring, and maintenance. This review was conducted through a two-stage systematic search of three databases (Scopus, IEEE Xplore, and ACM Digital Library; Web of Science was not accessible during the search period and was therefore excluded), which identified 54 primary studies meeting the full-text inclusion criteria, supplemented by 11 additional records located through hand-search, for a final corpus of 65 references. For each application domain (monitoring and inspection, energy distribution optimisation, fault detection and resilience, and cybersecurity and communication), we summarise the methodologies employed, the reported performance outcomes, and the evidence level of the available studies. We also analyse the scalability limits of current swarm approaches, communication constraints relevant to grid deployment, and the integration of swarm systems with existing SCADA and EMS infrastructure. The review identifies a significant gap between laboratory demonstrations and utility-scale deployment, and outlines priority directions for future research. Full article

To address spatial confusion and semantic collapse in maintenance knowledge graph construction for long-distance belt conveyor systems, this study proposes a linear topology-aware alignment framework driven by large language models. The framework constructs a Physical–Space–Fault–Rule ontology and introduces a normalized linear topology mapping mechanism to assign unique spatial anchors to highly homogeneous components. For noisy and colloquial maintenance logs, a Condition–Constraint–Action–Space (CCAS) extraction mechanism is developed to reconstruct implicit fault causal chains and spatial information from unstructured text. To prevent erroneous merging of components with similar semantic descriptions but different physical locations, the LTA-Alignment algorithm integrates Gaussian topological penalties with LLM-based grey-zone arbitration. A structured gold-standard annotation protocol is further established using maintenance logs, BOM records, spatial anchors, and closed-loop repair evidence. Experiments over three predefined runs show that CCAS achieves a global F1-score of 94.10 ± 0.56%, exceeding T5 by 6.20 percentage points. LTA-Alignment achieves a Merge F1-score of 95.75 ± 0.53% and an entity cluster purity of 96.70 ± 0.53%, exceeding SCSA by 3.36 and 3.90 percentage points, respectively. In downstream fault traceability, the dynamic spatiotemporal knowledge graph achieves an Accuracy@1 of 92.78 ± 0.36%, 10.16 percentage points higher than R-GCN, together with an estimated 68.50 ± 0.73% reduction in rule-based simulated troubleshooting time relative to Keyword Retrieval. The results demonstrate that combining normalized spatial topology with LLM reasoning improves entity disambiguation, fault-causal reconstruction, and traceability performance under the evaluated non-branching belt conveyor maintenance setting. Full article

The effects of Ru on Re segregation and low-cycle fatigue (LCF) behavior were investigated in two model single-crystal superalloys, Ni-8.5Al-4Re and Ni-8.5Al-4Re-2Ru (wt.%). Microstructural morphology, elemental distribution, fatigue deformation behavior, and dislocation structures were systematically compared, and density functional theory (DFT) calculations were performed to examine the associated atomic and electronic effects. The results show that Re segregates at the γ′/γ interface in both alloys, whereas the addition of 2 wt.% Ru reduces the extent of interfacial Re segregation. The Gibbsian interfacial excess of Re decreases from 18.19 ± 1.75 to 11.42 ± 0.01 atom nm⁻², while the maximum interfacial Re concentration ratio decreases from 1.36 ± 0.01 to 1.17 ± 0.02 after Ru addition. DFT calculations further show that Re increases the unstable stacking-fault energy of the γ′/γ interface by 10.34% relative to the unalloyed interface, whereas the corresponding increase is 5.05% in the Re–Ru co-doped system. Fatigue-deformed NiAlRe exhibits interfacial dislocation networks along the γ′/γ interfaces, whereas more extensive dislocation penetration into the γ′ precipitates is observed in NiAlReRu. At a strain amplitude of 0.6%, the Ru-containing alloy exhibits a lower cyclic stress response and more pronounced crack initiation and coalescence. These results show that Ru modifies the interfacial segregation behavior of Re and is associated with changes in the post-fatigue dislocation structures and high-temperature LCF response of the model superalloys. Full article

On 23 January 2024, the Mw 7.0 Wushi earthquake occurred along the Maidan Fault at the boundary between the southwestern Tianshan Mountains and the Tarim Basin. This event, together with its subsequent aftershocks, provides a valuable opportunity to investigate fault rupture, stress perturbation, and aftershock triggering within a continental fold-and-thrust belt. In this study, we inverted the coseismic slip distribution by jointly integrating Interferometric Synthetic Aperture Radar and Pixel Offset Tracking observations. The preferred fault geometry indicates a northwest-dipping fault with a dip of approximately 60°. The mean rake angle is about 46°, suggesting an oblique slip with both thrust and left-lateral components. The 2024 Wushi event, together with the 1902 Atushi M 8.2 earthquake, produced pronounced Coulomb stress changes on the surrounding faults, with positive stress increases exceeding 10 kPa. Combined with the GNSS-derived regional strain-rate field and the interseismic fault-locking model, these results indicate elevated seismic potential on several surrounding fault segments, including portions of the Maidan Fault, Nalati Fault, North Wensu Fault, and Kalpintag Fault. We further examine the 2024 shallow Mw 5.7 aftershock, whose southeast-dipping surface rupture, together with the northwest-dipping mainshock fault, defines a typical pop-up structure. We also discuss the possible triggering of the 2025 Mw 5.8 Aheqi event by the stress perturbation associated with the Wushi mainshock. Our results highlight that the 2024 Wushi event substantially modified the regional stress field and influenced subsequent seismic activity, providing new insights into rupture segmentation, fault interaction, and seismic hazard in southwestern Tianshan. Full article

The multi-infeed short-circuit ratio (MSCR) is widely used to characterize the steady-state strength of AC receiving systems with multiple line-commutated converter high-voltage direct-current (LCC-HVDC) infeeds. Its rated-power denominator, however, does not describe the disturbance actually imposed during converter blocking, commutation failure, AC-fault clearing, or concurrent disturbances at neighboring infeeds. This paper introduces a unified disturbance-driven transient multi-infeed short-circuit ratio (UDTMSCR). Six quantities obtained from the fault trajectory—reactive-power deviation, active-power reduction, reactive-power ramp, disturbance energy, recovery duration, and a fault-class correction—are normalized and combined into an infeed disturbance term. Substitution of this term for rated power retains the original impedance-coupling structure of MSCR, weights each neighboring disturbance by a bounded participation coefficient, and thereby accounts for the severity and timing of local and neighboring disturbances. The denominator is further separated into local and mutual contributions, and percentile thresholds may be used for severity classification. Numerical evaluation comprises eight representative two-infeed cases and a 48-case parametric study of mutual-path impedance, neighboring-event delay, and neighboring-disturbance amplitude. For the eight cases, the Pearson and Spearman correlations between inverse UDTMSCR and the fault-side voltage peak are 0.979 and 0.976, compared with 0.193 and 0.246 for inverse MSCR. The corresponding values in the parametric study are 0.973 and 0.983 for inverse UDTMSCR and −0.294 and −0.310 for inverse MSCR. A peak-only transient index and a transient voltage severity index are included as additional references, and the sensitivity of the results to the disturbance weights, coupling weights, grading thresholds, event window, and record imperfections is quantified. These results show that the proposed index retains the engineering interpretation of MSCR while better reflecting fault severity, inter-infeed coupling, and recovery. Because the trajectories are generated by a reduced-order model, verification with detailed electromagnetic-transient models and field records remains necessary. Full article

In comparison with stratovolcanoes, shield volcanoes tend to have more frequent dike-fed eruptions and large lateral and vertical collapses, as well as more gently dipping flanks. In many stratovolcanoes, dike-fed eruptions occur once every several hundred or thousand years but once every few years in many shield volcanoes. Using Hamilton’s principle of least action as a basis for determining potential dike/sheet propagation paths, it is shown that the probability of arrest of an injected dike/sheet is normally much greater in a stratovolcano than in a shield volcano. This is primarily because in stratovolcanoes rock layers and units are of contrasting mechanical properties, so that many dikes become arrested and thus do not feed eruptions. Similarly, many faults in stratovolcanoes become confined to one or several layers/units and do not reach the surface to generate landslides or ring faults. Consequently, the formation of large landslides is generally more difficult—requires more energy—in composite volcanoes than in shield volcanoes. For the same reason, formation of calderas in stratovolcanoes is normally more difficult than in shield volcanoes. More energy is needed to propagate fractures through many layers/units in stratovolcanoes than in shield volcanoes. It follows that stratovolcanoes tend to be tougher, more resistant to tectonic fracture propagation, and thus mechanically stronger than shield volcanoes. This may partly explain differences in the frequencies of dike-fed eruptions, large landslides, and caldera collapses between shield volcanoes and stratovolcanoes. Full article