Search Results (577)

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. Full article

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential. Full article

Continuous Integration pipelines rely on large-scale automated testing to support rapid releases. However, flaky tests exhibit non-deterministic outcomes under an identical code and configuration, substantially increasing rerun costs and hindering fault localization. Existing approaches struggle to uniformly model heterogeneous runtime evidence and its multi-relational structure in CI environments, which limits cross-project generalization and interpretability. To address this gap, this paper presents HgtFlaky, a runtime-evidence-centered multi-view heterogeneous graph learning framework. A Unified Event Model is introduced to normalize heterogeneous CI artifacts into semantically consistent event quadruples, and a heterogeneous execution graph is then constructed to capture testing entities and multiple relation types. Based on the HEG, three complementary views are derived to characterize run-level, test-level, and thread-level flaky behaviors. A heterogeneous graph Transformer is further adopted to jointly encode the multi-view graph instances and learn transferable test-level representations for flaky/non-flaky prediction. Experiments on two benchmark datasets, FlakeFlagger and IDoFT, show that HgtFlaky achieves strong and stable performance. Under 10-fold cross-validation, it obtains an F1-score of 83% on FlakeFlagger and 98% on IDoFT. Under per-project validation on FlakeFlagger, HgtFlaky achieves 78% Precision, 89% Recall, and 81% F1-score, outperforming Flakify by 8 percentage points and FlakeFlagger by 74 percentage points in F1-score. Full article

Long horizontal wells in high-permeability fault-block reservoirs may intersect multiple faults, leading to complex pressure-transient responses, strong parameter coupling in conventional well-test interpretation, inefficient manual history matching, and pronounced non-uniqueness in fault-property identification. To address these challenges, this study proposes a physics-regularized neural inversion framework based on a PINN parameterization and low-weight physics regularization for well-test parameter inversion in long horizontal wells intersecting multiple faults. The proposed method takes the multiple-fault pressure response of a long horizontal well as the target problem. Both the pressure–drawdown curve and the pressure–drawdown derivative curve are used as data constraints. At the same time, parameter scaling and stage-wise training are introduced to jointly invert the reservoir permeability, fault transmissibility coefficient, skin factor, and effective producing length of the horizontal well. Considering that the simplified line-source forward model is not fully consistent with the two-dimensional pressure-diffusion equation and the fault-interface residuals, a physics-loss consistency test is performed to determine safe weighting ranges for the PDE residual and the fault-interface residual. These residuals are then incorporated into the training process as low-weight physics regularization terms to improve the physical plausibility of the inversion results. Results from the base case, different fault types, multiple-fault combinations, noise-robustness tests, ablation experiments, and method comparisons show that the proposed method can stably fit pressure–drawdown and pressure–drawdown derivative curves and effectively identify key well-test parameters in single-fault cases and some multiple-fault cases. In single-fault cases, the order of magnitude of the fault transmissibility coefficient can be identified stably. Reliable inversion performance is obtained for medium- to high-transmissibility faults and some multiple-fault combinations. In contrast, ambiguity remains between sealing faults and strong-baffle faults in multiple low-transmissibility fault combinations. The results further indicate that, under multiple random initializations, the physics-regularized neural inversion framework provides improved inversion stability in the tested synthetic low-transmissibility multiple-fault cases compared with the traditional least-squares method. Therefore, the proposed framework can serve as an intelligent auxiliary tool for well-test parameter inversion and fault-connectivity evaluation in complex fault-block reservoirs. Nevertheless, fine discrimination of low-transmissibility faults and interpretation of highly noisy field data still require joint constraints from geological, seismic, and production-dynamic information. A preliminary reduced field PINN fitting test using the well X falloff event further provides an engineering-scale applicability check for real pressure-transient data, with a pressure NRMSE of 2.457% for the extracted shut-in response. Full article

The operational health state of motor bearings is critical to the operational safety of harbor portal slewing cranes. However, in harsh industrial environments with strong noise and time-varying rotational speeds, existing bearing fault diagnosis methods still suffer from the problems of incomplete fault feature extraction from single-sensor signals and the excessively large size of multi-source fusion models, which makes them unable to adapt to edge deployment. To address these issues, this paper proposes a Multi-source Feature Fusion Lightweight Network (MTFL-Net) integrated with targeted structured channel pruning. First, vibration and current signals are preprocessed via differentiated time-frequency transformation and converted into 2D time-frequency images, to fully preserve transient impact and spectral fault features. Second, a multi-branch feature extraction architecture embedded with residual connections, multi-scale convolution and channel attention gating is designed, to alleviate feature degradation and adaptively enhance fault-sensitive features. Third, targeted structured channel pruning is performed on the feature extraction branches, to remove redundant channels while retaining the multi-source fusion logic and core feature extraction structure. Experiments on two public bearing datasets show that the original model achieves 99% diagnostic accuracy, and the pruned model still maintains an accuracy of 95%. The results demonstrate that MTFL-Net can significantly reduce model size and computational cost while retaining high diagnostic precision. Full article

Continuous industrial material-handling systems are operationally and energy-intensive technological structures in which downtime affecting one equipment group can reduce the availability of the entire production chain. This study develops a probabilistic framework for assessing downtime impacts when detailed historical event-level downtime records are available, but complete technical and economic equipment parameters are missing. The analysis is based on 6605 downtime records for conveyors, excavators and stackers observed between 2017 and 2025. Historical downtime records were combined with interval-based assumptions for power demand, load factor, handling capacity, electricity price and commodity value, and were propagated through a Monte Carlo simulation with 10,000 iterations. The results revealed a strong concentration of downtime burden. The combination of P–Conveyor–Material Collapse accounted for 32.58% of total downtime, while the top five equipment–fault combinations explained 67.86% of cumulative downtime. At the system level, the median modelled energy-service unavailability reached approximately 4339 MWh, the median production-loss equivalent reached approximately 9279 kt, and the median total economic loss was approximately EUR 209.5 million. The proposed Energy–Economic Impact Index integrated event frequency, downtime severity, energy-service unavailability and economic loss into a single maintenance-prioritisation indicator. The highest-ranked maintenance target was P–Conveyor–Material Collapse, confirming that maintenance priorities should be determined by combined operational, energy-related and economic consequences rather than by event frequency alone. The study demonstrates that historical downtime records can be transformed into a probabilistic decision-support tool for risk-based maintenance planning in industrial systems with incomplete technical and economic data. Full article

In-wheel motor bearings in electric vehicles operate in harsh environments where strong background noise often masks early fault features, limiting the accuracy of traditional diagnostic methods. This study proposes an intelligent fault diagnosis framework integrating improved Successive Variational Mode Decomposition (SVMD) with a ResNet–Kolmogorov–Arnold Network (ResNet-KAN). To enhance feature extraction, a multi-strategy Crested Porcupine Optimizer (CPO) is employed to adaptively optimise SVMD parameters. Subsequently, a Gramian angular difference field (GADF) reconstruction strategy transforms one-dimensional vibration signals into two-dimensional images to improve spatial distinguishability. Finally, a ResNet-KAN model, featuring a ReLU-based non-linear classification head, is developed to capture complex fault boundaries more effectively than traditional linear layers. Experimental results demonstrate that the CPO-SVMD method increases the kurtosis of extracted components by at least 25.6% compared to traditional optimisation methods. Furthermore, the ResNet-KAN model achieves an identification accuracy exceeding 98% on the in-wheel motor bearing dataset, outperforming 2DCNN, ResNet, and ViT models by at least 2%. This integrated approach provides a robust, high-precision solution for the intelligent condition monitoring and early warning of in-wheel motor drive systems under complex, high-noise operating conditions. Full article

To address the challenges of high feature similarity and severe noise interference in the open-circuit fault diagnosis of IGBT inverter modules under harsh marine conditions, this paper proposes an improved artificial bee colony-optimized one-dimensional convolutional neural network (IABC-1D-CNN) for robust fault diagnosis in marine ranching power systems. This study provides a MATLAB R2024a/Simulink-based feasibility validation rather than hardware or field verification. First, a photovoltaic grid-connected inverter simulation model is established to generate three-phase current signals under different operating conditions and fault states, and a sliding-window segmentation method combined with data augmentation is employed to improve sample diversity. Then, the improved artificial bee colony algorithm, incorporating differential evolution and genetic strategies, is used to globally optimize the key hyperparameters of the 1D-CNN, thereby improving convergence efficiency and model stability. Based on the optimized architecture, the proposed model enables automatic feature extraction and accurate identification of IGBT open-circuit faults under complex marine environments. Experimental results show that the proposed method achieves high diagnostic accuracy under both noise-free and noisy conditions. Under signal-to-noise ratios (SNRs) of 20 dB, 15 dB, 10 dB, and 0 dB, the diagnostic accuracies reach 99.55%, 98.86%, 97.27%, and 89.25%, respectively, consistently outperforming Baseline 1D-CNN, CNN-LSTM, and ELM. These results demonstrate that the proposed method provides a simulation-validated diagnostic framework with strong classification accuracy and noise robustness, while practical deployment requires further HIL and field-data validation. Full article

Rolling bearing fault diagnosis is critical for ensuring the safe operation of rotating machinery, yet it faces significant challenges in noisy environments. This paper proposes BEAM-Net (Bearing-spectrum Enhanced by EMA and Weighted Spectral Convolution Network), a lightweight neural network designed specifically for rolling bearing fault diagnosis under strong noise conditions. Classifying bearing faults from vibration signals remains a challenging task when fault-related features are subtle and easily submerged in background noise—especially when the signal-to-noise ratio (SNR) is low. To address this challenge, BEAM-Net adopts a “decompose–enhance–extract” pipeline: first, an Exponential-Moving-Average Trend Decomposer (ETD) splits the frequency spectrum into a smooth trend component and a fault-sensitive residual component; second, a Spectral Residual Gate (SRG) reinjects detailed residual information through a learnable gating mechanism; finally, a Weighted Spectrum Convolution block (WSC) incorporates a symmetric center-emphasizing prior into the convolution kernel, ensuring that local spectral patterns receive greater attention. Experimental results on the Case Western Reserve University (CWRU) bearing dataset at SNR = −6 dB show that BEAM-Net achieves an F1 score of 99.15% with only 2835 parameters. Compared to the single-convolution baseline, this represents a $+0.78\%$ improvement in F1 score and a 50% reduction in the false positive rate (from $0.18\%$ to $0.09\%$). Cross-dataset validation on the Paderborn University (PU) and Machinery Failure Prevention Technology (MFPT) datasets further confirms the generalizability of the proposed approach, achieving F1 scores of 97.83% and 98.46%, respectively, under comparable noise conditions. These findings demonstrate that combining explicit spectral trend modeling with weighted convolution is not only effective but also parameter-efficient, making it well-suited for noise-robust rolling bearing fault diagnosis. It should be noted that the current method is primarily validated on spectral-analysis-based diagnostics of rolling bearings; its applicability to other vibroacoustic diagnostic modalities (e.g., tapping or nonlinear vibration excitation) and to quantitative defect severity grading remains to be investigated in future work. Full article

Studying jet engine vibration (JEV) enhances flight safety and operational reliability through advanced detection, precision modeling, and data-driven techniques. This approach involves complex nonlinear vibration behaviors that often exceed the capabilities of conventional techniques. It facilitates early fault detection, predictive maintenance, and improved engine design. This study employs the non-perturbative approach (NPA) to examine the dynamics of a parametric nonlinear oscillatory system. The formulation is based on He’s frequency formula (HFF), which transforms a nonlinear ordinary differential equation (ODE) into an equivalent linear one. The analytical results are validated using Mathematica software (MS) (v13), showing strong agreement between the original nonlinear ODE and the corresponding linearized equation. To further explore the system behavior, bifurcation diagrams (BDs) are constructed, and the largest Lyapunov exponent (LLE) is utilized to identify stability regions and detect chaotic oscillations. The averaging method is applied to determine the critical resonance conditions and derive the frequency–response relationships; meanwhile, stability near simultaneous primary resonance is examined using the Routh–Hurwitz criterion. Finally, numerical simulations (NSs) based on the fourth-order Runge–Kutta method (RK-4) confirm the effectiveness of the positive position feedback (PPF) control strategy. Full article

Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work. Full article

To address the frequent occurrence of multiple fault types, the difficulty of feature extraction, and the susceptibility to noise interference in rolling bearings under complex operating conditions, this paper proposes a fault diagnosis method based on an improved one-dimensional convolutional neural network (1DCNN) integrated with a Transformer architecture. This approach leverages the 1DCNN to efficiently extract local impact and energy features from vibration signals, while the improved Transformer enables global modeling of long-range temporal dependencies, thereby significantly enhancing the recognition accuracy for multi-class fault signals and the generalization capability of the model. Experimental data are sourced from the Case Western Reserve University bearing fault dataset, with multi-channel vibration signals subjected to preprocessing and balanced sampling, and various types of simulated noise systematically introduced to comprehensively verify the noise robustness of the proposed model. Experimental results on the public dataset demonstrate that the improved 1DCNN-Transformer model achieves a classification accuracy of 99.43%, markedly outperforming traditional methods such as ANN, CNN, LeNet, and SVM. Further t-SNE visualizations and confusion matrix analyses reveal the method’s superior feature discrimination and high-precision performance across multiple fault categories. Tests under strong noise conditions further indicate that the model exhibits high robustness and excellent potential for engineering applications. In summary, the proposed method provides an efficient and reliable solution for intelligent fault diagnosis of rolling bearings in complex environments and lays a solid foundation for future model development and industrial deployment. Full article

To ensure the reliability and sustained performance of industrial photovoltaic (PV) systems, fault detection frameworks must achieve both high detection accuracy and computational efficiency, particularly for deployment on resource-constrained edge platforms. This work proposes a lightweight and low-latency photovoltaic defect detection framework that integrates DCGAN-based generative augmentation with the proposed GhostViT-YOLOv10n architecture. The augmentation strategy helps address class imbalance, improve representation of rare defects, and enhance generalization capability in electroluminescence (EL) imagery through structured geometric and photometric transformations. The proposed framework integrates lightweight Ghost-based optimization, Cross-Stage Partial Fusion (C2f), Spatial Pyramid Pooling—Fast (SPPF), MobileViT contextual learning, and SimAM-based attention refinement to improve multi-scale feature extraction while maintaining low computational complexity. Experimental evaluation on the PVEL-AD and PV Multi Defect benchmark datasets demonstrates strong detection performance. On the PVEL-AD dataset, the BaseLine achieves a mAP@0.5 of 71.6% with only 2.7 M parameters and 8.4 GFLOPs, while our proposed GhostViT-YOLOv10n framework with DCGAN-enhanced version further improves detection performance to 93.6% mAP@0.5 with only 2.19 M parameters and 6.6 GFLOPs. On the PV Multi Defect dataset, the BaseLine achieves a mAP@0.5 of 74.0% with 2.71 M parameters and 8.4 GFLOPs, and the optimized framework with DCGAN-augmented configuration further improves performance to 95.4% mAP@0.5 with 2.58 M parameters and 7.7 GFLOPs. These results demonstrate the effectiveness of combining lightweight architectural optimization with generative augmentation for improving rare defect representation and multi-scale photovoltaic defect detection. To validate practical deployment feasibility, the optimized framework was deployed on a Raspberry Pi 5 using ONNX Runtime under CPU-only conditions. The deployed model achieved an average inference time of 43.05 ms and a real-time processing speed of 23.23 FPS while maintaining moderate CPU utilization and stable thermal behavior. These deployment results demonstrate the suitability of the proposed framework for lightweight edge-oriented photovoltaic inspection applications without requiring GPU acceleration. All evaluations were conducted exclusively on real test datasets, while synthetic samples were used only during training to improve data diversity and rare defect representation. Overall, the proposed framework provides a balanced solution that combines detection accuracy, computational efficiency, lightweight edge deployment capability, and generative augmentation for practical photovoltaic defect inspection applications with potential suitability for future drone-assisted inspection scenarios. Full article

Effective fault diagnosis of wind turbine blades and rotating machinery is critical for ensuring operational reliability and reducing maintenance costs. This study introduces a healthy-reference modeling framework that combines physics-informed neural network (PINN) with deviation-based dynamic regime features for systematic fault detection. At first, healthy and faulty data are normalized, then PINN is trained solely on healthy data, creating a reference model that predicts normal behavior. Deviations between measured signals and the healthy-reference predictions are then analyzed to extract key dynamic regime features, including energy, stability, drift, intermittency, and persistence, capturing subtle variations caused by faults. An interpretable Support Vector Machine (SVM) classifier uses these features to identify fault types such as ball, inner race, outer race, crack, erosion, and unbalance. Classification is performed using dynamic feature combinations while energy is often used as the base feature. The result shows energy with persistence combination performance is better than other feature combinations, and fused features achieved higher accuracy for both datasets. The approach is validated on both bearing data and an experimental blade dataset, demonstrating strong performance across different mechanical systems. Comparative evaluation with three different approaches, including Cross-load Scalogram-based CNN, Spectrogram-based CNN, and Hybrid SVM, highlights that the proposed healthy reference framework offers a data-efficient, interpretable, and robust solution for fault detection. This work highlights the importance of modeling healthy dynamics before classification, capturing both how strong a fault is and how it behaves over time, which offers a practical approach for wind turbine condition monitoring with limited data. Full article

This study investigates the coordinated impact of a synchronous condenser (SC), battery energy storage system (BESS), and static synchronous compensator (STATCOM) on enhancing voltage and frequency stability in a modified IEEE 9-bus power system under severe disturbances. The aim is to quantify the individual and combined contributions of these technologies during both fault ride-through (FRT) and load-increment events. The methodology includes dynamic modelling of all three devices in DIgSILENT PowerFactory. The SC is represented as a synchronous machine with inertia and AVR-based voltage control; the BESS employs converter-based active power and frequency-droop control; and the STATCOM provides fast reactive power injection through a dual-loop voltage regulator. Key indicators include nadir (minimum frequency), Rate of Change of Frequency (RoCoF), steady-state deviation, voltage sag depth, and recovery characteristics. Results indicate distinct roles for each device. The SC increases inertia and improves damping, but it also introduces small, well-damped oscillations. The BESS significantly enhances frequency stability by mitigating nadir, reducing RoCoF, and accelerating recovery, with negligible effect on voltage regulation. The STATCOM substantially reduces voltage sag and speeds up voltage recovery, but it does not influence frequency behaviour. When combined, the hybrid SC–BESS–STATCOM system demonstrates strong complementarity: the SC supports inertia, the BESS stabilizes active-power imbalance, and the STATCOM ensures fast reactive-power compensation. Full article