Search Results (1,793)

Seismic-while-tunneling signals acquired during coal-mine excavation are typically characterized by strong nonstationarity, intense mechanical noise, weak reflection responses, unstable inter-trace phases, and complex waveform fluctuations. These characteristics make conventional energy- or amplitude-based picking methods susceptible to false triggers and missed detections. To reveal the local complexity mutation of mine seismic signals under strong-noise backgrounds, this study proposes a multiscale fractal-dimension-constrained coherent phase processing method for signal enhancement, first-arrival picking, and fault prediction. First, the raw seismic-while-tunneling records are reorganized into shot gathers, windowed, and downsampled to preserve the effective early-arrival information. A damped multichannel singular spectrum analysis method is then used to extract coherent low-rank components and suppress incoherent random noise. Second, short-window and long-window box-counting fractal dimensions are calculated to characterize local and background waveform complexity, and a fractal-dimension mutation index is constructed to identify abrupt complexity transitions associated with effective seismic arrivals. On this basis, the fractal mutation index is incorporated into a coherent phase picking function that combines multichannel phase consistency and stacked amplitude, forming a fractal-dimension-constrained CCPP detection criterion. This criterion enhances true coherent arrivals while suppressing isolated noise spikes and unstable local amplitude disturbances. Finally, phase-weighted stacking is applied to further strengthen phase-consistent reflection responses and improve the interpretability of seismic-while-tunneling imaging profiles. Field application at the WII02040503 working face of Tunbao Coal Mine demonstrates that the proposed method can effectively improve the continuity of coherent events, stabilize automatic picking results, and enhance anomalous reflection bands under complex underground noise conditions. During the engineering trial, a total of 2558 m of ahead prospecting was completed, and 29 faults were predicted. The field-confirmation rates of the predicted faults with throws greater than 3 m, between 1 and 3 m, and less than 1 m were 100%, 87.50%, and 81.25%, respectively. Overall, 25 of the 29 predicted faults were confirmed by field exposure, corresponding to an overall field-confirmation rate of 86.21%. After velocity-synchronization time-difference correction, the average planar positioning deviation of the confirmed fault predictions decreased from 7.86 m to 5.08 m, corresponding to a 35.37% reduction in positioning error. These results indicate that the proposed fractal-dimension-constrained coherent processing framework provides an effective approach for complexity-aware signal enhancement and robust fault prediction in seismic-while-tunneling monitoring. Full article

Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22.1% reduction in average vehicle delay ($p<0.001$), while microscopic simulations confirmed up to 28.0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53.5–72 kg of CO₂ avoided per day, alongside annual fuel savings of 8430 L. Assembled within a $1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies. Full article

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases. Full article

China’s offshore wind power is rapidly developing towards the direction of “deep-water and far-shore, large-scale, and clustered”. Existing offshore wind power transmission schemes based on the MMC are technologically mature but highly expensive. Although transmission schemes based on the DRU possess economic advantages, they lack AC voltage support and reverse power flow capability. To combine the control performance of the MMC and the economic advantages of the DRU, this paper proposes a heterogeneous DC transmission system for offshore wind power based on the parallel operation of MMC-HVDC and DRU-HVDC, which can realize the clustered transmission of deep-water and far-shore wind power. First, the configuration scheme of the system is introduced, and the basic control strategy is proposed. Secondly, the small-signal model of the system is established, and the small-signal stability analysis is conducted. Then, the control strategies for the system under near-zero power conditions and AC/DC faults are proposed, respectively. Finally, the effectiveness of the proposed topology and control strategies is verified through PSCAD electromagnetic transient simulations. Full article

Attitude tracking constitutes one of the most essential functions for rigid spacecraft operating in orbit. Nevertheless, the performance of spacecraft attitude control systems strongly depends on the operational integrity of onboard actuators, and actuator malfunctions can severely deteriorate tracking accuracy or even destabilize the entire system. Such adverse effects become particularly critical during transient maneuvers, where the spacecraft undergoes rapid rotational acceleration to achieve a prescribed attitude command. To cope with these demanding operating conditions, this work develops a fault-tolerant attitude tracking framework that combines a non-singular fixed-time controller with an adaptive fixed-time fault estimation mechanism. By treating actuator faults and external disturbances as a unified lumped uncertainty term, the proposed approach enhances system robustness under both nominal and faulty operating conditions. The adaptive estimation mechanism serves a crucial role in identifying the upper bound of the lumped uncertainty, thereby enabling reliable fault compensation and improving the overall fault-tolerant capability of the closed-loop system. In addition, rigorous stability proofs based on Lyapunov theory are provided to demonstrate that all closed-loop signals remain bounded and that the tracking errors converge within a fixed settling time independent of initial conditions. Furthermore, theoretical investigations reveal that the proposed non-singular fixed-time formulation achieves faster convergence characteristics compared with a class of existing fixed-time control strategies while simultaneously eliminating singularity-related issues. Finally, comprehensive experimental studies are carried out on a practical spacecraft test platform to verify the feasibility, robustness, and effectiveness of the proposed control methodology. Full article

When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions. Full article

To address the degradation of speed regulation performance in the permanent magnet synchronous motor (PMSM) transmission system caused by the gearbox broken tooth fault and disturbances, a fault model-assisted active disturbance rejection control (FMA-ADRC) algorithm is proposed in this paper. First, an electromechanical coupling model of the motor–gearbox transmission system is established based on the dynamic model of the tooth fault. Secondly, a fault model-assisted extended state observer (ESO) in the active disturbance rejection controller is designed, where the periodic torque disturbances caused by the fault are compensated to reduce the estimation burden on the observer. In addition, the observer is nonlinearized to improve the accuracy of tracking disturbances. The observer error of the nonlinear ESO (NESO) is proven to converge to a bounded region within finite time by using the Lyapunov stability proof theory. Finally, the speed regulation performance of the proposed FMA-ADRC controller is verified under different degrees of fault using an experimental platform based on DSP28335 and MATLAB/SIMULINK R2023b. The reliability and superiority of the proposed controller are verified by the experiment results. Full article

Fluid injection in deep reservoirs can induce fault reactivation and seismicity, posing challenges for geothermal and subsurface energy development. This study investigates the mechanical interaction between two adjacent non-intersecting faults under fluid injection using a pseudo-three-dimensional thermo-hydro-mechanical (THM)-coupled numerical model. The results show that injection first triggers slip on F2, which then redistributes stress onto F1. The response of F1 is strongly heterogeneous: some segments are stabilized due to a decrease in Coulomb failure stress, whereas other segments are destabilized due to an increase in Coulomb failure stress. Stress-path analysis indicates that the immediate response of F1 to F2 slip is mainly governed by changes in effective normal stress and shear stress, rather than abrupt pore pressure changes on F1. These findings demonstrate that fault slip can act as a mechanical stress source that either promotes or inhibits adjacent fault reactivation. Therefore, slip-induced stress transfer should be explicitly considered when assessing fault stability in reservoirs containing multiple closely spaced faults. Full article

This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. Full article

Industrial process fault diagnosis remains challenging because one-dimensional time-series data often involve complex dynamics, noise disturbances, and temporal dependencies, which hinder reliable fault representation and robust diagnostic decisions under complex operating conditions. To address these challenges, a fault diagnosis method for industrial processes based on the Multiscale Convolution-Attention-GRU Residual Network (MCAG-ResNet) is proposed. MCAG-ResNet integrates multiscale feature learning, attention-based feature recalibration, temporal dependency modeling, and residual learning in a unified architecture to enhance discriminative fault representation and diagnostic robustness. In addition, normalization and lightweight data augmentation are incorporated to improve training stability and generalization performance. Validation on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets demonstrates the effectiveness, generalization capability, and diagnostic stability of the MCAG-ResNet in complex industrial process fault diagnosis. Further analyses, including variable contribution, feature importance, noise robustness, hyperparameter sensitivity, performance–complexity, and statistical stability analyses, verify its interpretability, robustness, parameter rationality, practical applicability, and stability. Full article

Thin, steeply dipping orebodies hosted in kaolinized altered fault zones are difficult to mine safely because of weak rock mass integrity, water sensitivity, and limited self-supporting capacity. This study investigates the F20 ore-bearing altered structural zone at Changtai Mining and develops an artificial floor design for downward drift-and-fill mining. Engineering geological characterization, rock mass quality evaluation, mechanical analysis, and three-dimensional numerical simulation were combined to assess floor-bearing requirements and regional recovery stability. The results show that the wall rocks are grade III, whereas the ore-bearing altered zone is grade IV and represents the controlling weak component. For the preferred 3.5 m × 3.5 m drift, an equivalent artificial floor bearing thickness of about 1.0 m is required. Numerical evaluation indicates that supported drifts remain stable, but crosscut–drift intersections are the main deformation and damage concentration zones. A representative 0.5 m drift offset significantly weakens the load-transfer path of the floor–rock system. The proposed vertically aligned, short drift, rapid backfill scheme with a reinforced composite artificial floor provides a practical basis for safe recovery of weak kaolinized altered orebodies. Full article

Chillers serve as the core high-energy-consuming equipment in heating, ventilation, and air conditioning (HVAC) systems. During long-term continuous operation, they tend to suffer gradual subtle degradation, with a performance deviation less than 5%. Conventional fault diagnosis methods rely on manual threshold judgment or labeled fault data, which fail to realize accurate early warning signals. In addition, existing algorithms lack multi-dimensional baseline comparisons to verify their practical engineering performance. To address these limitations, this paper proposes an unsupervised health assessment method combining an LSTM autoencoder and Euclidean distance (LSTM-AE-ED). A multi-gradient fault time-series dataset is generated via a MATLAB R2022b/Simscape mechanism model verified by both summer field measurements and refrigeration pressure-enthalpy cycles, which resolves the practical engineering challenges of scarce on-site fault samples and potential equipment damage caused by actual fault tests. The proposed model is trained solely on healthy time-series data. It extracts dynamic coupling characteristics of chillers through LSTM, constructs a dimensionless health index based on Euclidean distance in feature space, and introduces the standard deviation of health index to improve evaluation stability. Baseline comparisons with vanilla AE and single-layer LSTM are carried out. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.3% and exhibits high sensitivity to mild degradation of four typical faults, adapting to dynamic multi-working-condition scenarios. This approach requires no additional acquisition devices for derived parameters such as power consumption and COP; online assessment can be realized merely with standard temperature, pressure, and flow sensors equipped on chillers. With lightweight inference performance, it is suitable for edge monitoring terminals of chillers in data centers, providing a low-cost and practical quantitative technical scheme for predictive maintenance and hierarchical early warning signals of refrigeration equipment. Full article

This paper proposes a novel data-driven K-Gap metric method based on the Koopman operator for the detection and isolation of multiplicative faults in high-speed train suspension systems. A systematic comparison is conducted with a data-driven K-Gap approach implemented through the fuzzy modeling framework. First, Takagi–Sugeno (T–S) theory is employed to extend the K-Gap metric for nonlinear dynamic modeling of the suspension system. Subsequently, the Koopman operator framework is introduced to lift the system states into a high-dimensional observable space, enabling a globally linear representation of the system. Building upon Koopman-based stable kernel representation (SKR), a more accurate K-Gap residual metric is constructed. Finally, a unified fault diagnosis scheme is developed with the K-Gap metric as the core indicator, and the two approaches are experimentally compared in terms of their performance in detecting and isolating multiplicative faults. The experimental results demonstrate that the Koopman-based method significantly outperforms the T–S fuzzy model in terms of residual separability, fault classification accuracy, and diagnostic stability, confirming its effectiveness and superiority for fault diagnosis in complex nonlinear systems. Full article

Superconducting transmon processors represent a leading platform for large-scale quantum computing due to their high gate fidelities and scalability. However, conventional qubit–coupler–qubit (QCQ) architectures face critical physical and structural bottlenecks, notably frequency crowding [spectator qubit collisions] during system scaling and inefficient mapping onto the standard surface code. To overcome these limitations, we propose a novel lattice-patch architecture that couples four fixed-frequency transmons to a single fixed-frequency coupler. This design enhances qubit connectivity and maps directly onto the surface-code lattice unit [plaquette], thereby minimizing the compilation overhead associated with logical qubit implementation. Furthermore, utilizing an entirely fixed-frequency design intrinsically eliminates susceptibility to external flux noise, ensuring robust operational stability. Multi-level numerical simulations demonstrate CNOT gate fidelities exceeding 0.98 across all six connectivity directions within the patch. Nevertheless, the complex interaction network of the four-qubit architecture induces unintended residual phase accumulation during cross-resonance driving. This parasitic effect necessitates precise calibration, achievable via virtual $R_z$ gates [software phase updates]. Ultimately, our results establish the lattice-patch architecture as an efficient, robust building block for future fault-tolerant quantum computers. Full article

In this paper, the issue of robust sensor fault estimation for Takagi–Sugeno (T–S) fuzzy systems with Caputo–Hadamard fractional-order dynamics subject to monotone nonlinearities is addressed. An adaptive observer is designed for the joint estimation of the system state and a globally constant sensor bias fault. The Caputo–Hadamard operator is used to handle logarithmic memory effects, and the T–S fuzzy representation is used for multi-regime nonlinear dynamics through a convex interpolation structure. Sufficient linear matrix inequality (LMI) conditions are obtained to ensure generalized Mittag–Leffler stability of the augmented estimation error system under a constant-fault assumption, by combining a sector inequality for strongly monotone nonlinearities with a fractional Lyapunov approach. The stability conditions are directly posed in the decision variables and the observer gains are recovered through a standard change of variables. To broaden the engineering applicability of the result, a finite-horizon practical Mittag–Leffler stability theorem is also derived for absolutely-continuous time-varying sensor faults whose Caputo–Hadamard derivative is bounded on the operating horizon $[t_0,T]$, in which the augmented estimation error remains in a residual ball whose radius is proportional to that bound. An alternative design, called a polynomial gain-scheduled observer, is also developed to reduce the conservatism of the constant-gain design, with observer gains given as polynomials of a measurable, fault-free scheduling vector. Quantitative root-mean-square performance metrics, LMI feasibility margins and an adaptation-gain sensitivity study are reported, and the polynomial matrix inequality is certified both by a dense grid check and by a sum-of-squares (SOS) feasibility argument so that the polynomial design is supported by a constructive certificate over the admissible scheduling set. Three numerical scenarios with fractional order $0.8$ are provided: a strict constant-bias scenario that exactly validates the LMI theorem, a bounded-derivative ramp scenario that validates the practical Mittag–Leffler theorem, and a polynomial gain-scheduled scenario that validates the polynomial observer. Full article