Search Results (8,304)

This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable. Full article

While predictive maintenance increasingly relies on artificial intelligence, strict dependence on cloud computing introduces network latency and demands continuous connectivity, creating critical bottlenecks for time-sensitive industrial applications. To overcome this, we introduce a novel hybrid edge-cloud architecture, which allows deploying an ultra-low-power microcontroller (ESP32-S3) without dedicated AI acceleration hardware to perform complete, operational, predictive maintenance on ultra-constrained embedded hardware. The edge model is optimized to be very small to ensure that increasing model complexity does not cause inference latency to exceed 100 ms or make real-time operation infeasible. We created a very compact INT8-quantized neural network to perform the simultaneous classification of faults and estimation of Time-to-Failure (TTF) with a deterministic mean inference time of 42.3 ms. It dynamically estimates prediction confidence, processes high-confidence predictions locally, and offloads uncertain predictions to a higher-capacity cloud model, and recovers 97.3% of the cloud accuracy gain at 92% of the cloud latency budget. An asymmetric loss function penalizes over-prediction of the remaining useful life, and thus it provides conservative and safe warnings of fault. Operators’ interpretability is improved with Shapley Additive exPlanations (SHAP) and natural-language recommendations. Network outages of up to 50% have not influenced the safety-critical fault recall (above 0.924), so graceful degradation is reached when the network is used in real time in industrial applications. The edge-first with adaptive cloud fallback approach is demonstrated to be technically feasible for a full predictive maintenance workflow—including inference, confidence fusion, and explainability on a low-cost commercial microcontroller. Full article

To address the increasingly prominent challenges of “low inertia” and “weak damping” in modern power systems, grid-forming (GFM) control technologies with inertia and damping support capabilities are being extensively adopted. However, distributed generation units interfaced with GFM inverters are highly susceptible to overcurrent phenomena during grid short-circuit faults. Existing research primarily focuses on current-limiting control strategies for virtual synchronous generators (VSGs), while investigations into their fault current characteristics remain insufficient. Given this, this paper proposes a short-circuit current calculation methodology for VSG-based PV-storage grid-connected systems. First, a model of a grid-forming PV-storage grid-connected system based on virtual synchronous control is established. Subsequently, the virtual impedance is solved within the timescale of current inner-loop stabilization, and the virtual internal electromotive force (EMF) equation for the VSG is formulated. This leads to the derivation of an analytical expression for the VSG short-circuit current, accounting for variations in the virtual internal potential. Furthermore, the impacts of diverse control parameters and fault severities on the short-circuit current are investigated based on this expression. Finally, simulations are conducted on the MATLAB/Simulink(R2024b) platform to validate the accuracy of the proposed short-circuit current calculation method and the correctness of the analysis regarding the influencing factors. Full article

Rolling bearing fault diagnosis is essential for ensuring the safety and reliability of rotating machinery. Although deep learning-based methods have achieved promising performance, they usually require sufficient labeled data, which is difficult to obtain in practical industrial scenarios where fault samples are scarce and data sharing across sites is restricted by privacy and confidentiality constraints. Federated learning enables collaborative model training without transmitting raw data, but existing federated fault diagnosis methods often degrade under few-shot conditions. Moreover, current federated meta-learning approaches mainly focus on model-level adaptation and lack explicit class-level representation alignment, leading to prototype drift across heterogeneous operating conditions. To address these challenges, this paper proposes ProtoFed, a prototype-enhanced federated meta-learning framework for few-shot rolling bearing fault diagnosis. ProtoFed converts raw vibration signals into time–frequency representations using continuous wavelet transform and performs local episodic learning with prototypical networks. A Global Prototype Calibration mechanism aggregates local class prototypes into stable global prototypes with exponential moving average smoothing, while a Prototype-Distance Aware Aggregation strategy adaptively adjusts client aggregation weights according to local–global prototype divergence. Experiments on the CWRU and Paderborn University bearing datasets under non-IID 5-shot and 10-shot settings show that ProtoFed consistently outperforms standard federated learning, prototype-based federated learning, and federated meta-learning baselines. Under the 5-shot setting, ProtoFed achieves 95.63% and 91.35% accuracy on CWRU and PU, respectively, approaching centralized few-shot upper-bound performance while preserving the federated training paradigm. Full article

Fault tolerance in distributed real-time systems has, up till now, relied on static redundancy, replication, or predictive mechanisms, which introduce latency, resource overhead, and inadaptability under dynamic failure conditions. This paper presents Chrono Weave (CW) as a revolutionary new idea that describes how a system is working as a flow of a time-ordered field of states, so that even if the system is broken, it can recover without explicit redundancy or replication. CW does not replicate computation but rather encodes system evolution into temporally entangled microstates; therefore, recovery is made possible through deterministic temporal interpolation. The Temporal Consistency Field (TCF), a new concept, is presented to measure system integrity over time, enabling fault localization and instant reconstruction. The new system does not require standby replicas, and recovery is achieved just by way of using temporal coherence that is inherent. From a theoretical viewpoint, it is shown that CW can reduce recovery latency asymptotically towards zero as long as the drift is bounded. From the perspective of distributed control, simulation experiments have still managed to show great recovery speed and system reliability improvements over the traditional ones. This paper opens fault-tolerant computing to a new mode of operation where instead of being based on redundancies, time-structured, self-healing systems are used. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

Earthquakes may cause severe damage to engineering structures in the seismogenic fault zone. In near-fault regions, ground motions on the two sides of a fault exhibit significant asymmetry in terms of permanent displacement, velocity pulse, and dynamic displacement amplitude. Taking the Xianglu Mountain Tunnel in the southwest of China as the engineering object, this study designed scaled fault-crossing tunnel-surrounding rock test models and conducted a series of quasi-static and dynamic model tests using a dual-shaking table system with non-uniform ground motion input. The effects of three different earthquake action modes on the responses of tunnel engineering structures crossing seismogenic faults were investigated through five static and dynamic earthquake action modes. The test results indicate that considering only the dynamic effect of ground motion or only the static effect of permanent displacement due to fault dislocation will underestimate the seismic response and damage degree of the surrounding rock and tunnel structure. However, the contribution of dynamic effects of ground motion to tunnel failure is much smaller than that of static fault dislocation. The magnitude of permanent displacement from fault dislocation, the peak displacement of non-uniform ground motion time history, and the peak relative displacement are all important factors affecting the deformation of surrounding rock and the strain of tunnel structures. Traditional static analysis methods will lead to an underestimation of the damage risk of tunnel structures. Compared with the non-uniform earthquake action mode, the deformation within the fracture zone under the static action mode is underestimated by approximately 6.39%, and the peak tensile strain under the static action mode underestimates the damage risk by approximately 40%. Full article

Transmission error (TE) is a major excitation source in geared systems, but microgeometry deviations are usually evaluated through nominal amplitudes rather than their tooth-to-tooth spatial distribution. This study investigates how different tooth-level barreling deviation patterns influence TE modulation and spectral characteristics in a controlled single helical gear-pair model. The nominal barreling value was kept constant, while four deviation patterns were imposed on the 23-tooth pinion: harmonic, phase-shifted harmonic, clustered with an outlier, and random. The TE response was evaluated in the time domain and by Fast Fourier Transform (FFT)-based spectral analysis, with particular attention to the gear mesh frequency (GMF) and shaft-frequency-spaced sidebands. The results show that identical nominal barreling levels can produce different TE waveforms and spectral signatures. Harmonic distributions mainly preserve a regular response, whereas phase-shifted and clustered patterns increase waveform asymmetry and sideband activity. The clustered outlier case produced the most fault-like response. The findings indicate that tooth-level spatial distribution should be considered explicitly in simulation-based gear microgeometry and noise, vibration, and harshness (NVH) sensitivity studies. Full article

Given the complex operating conditions and latent faults exhibited by electric vehicle charging infrastructure amid massive order volumes, traditional monitoring methods based on thresholds or single statistical metrics struggle to detect dynamic, time-varying anomalies. This paper proposes a method for identifying and tracing the operational status of charging facilities based on the k-shape time-series clustering algorithm. This method directly uses charging current time series as the research object, eliminating the cumbersome manual feature extraction process. By utilizing a shape-based distance (SBD) metric strategy, it overcomes common time-series data issues such as phase shifts and amplitude scaling while preserving the integrity of the time dimension. Through iterative calculation of cluster centroids, the algorithm successfully and adaptively classifies massive amounts of data into typical clusters such as “standard charging,” “deep oscillation,” and “power-limited.” Based on the clustering results, this paper further constructs a “shape-operating condition” mapping mechanism. Combined with a Bayesian posterior probability model, this enables the localization of high-risk “vehicle-charger” combinations statistically associated with abnormal waveforms. Empirical studies demonstrate that this method can effectively identify equipment performance degradation at the micro-level of waveforms and provide prioritized inspection clues for the intelligent operation and maintenance of charging networks. Full article

AC microgrids with high penetration of inverter-based distributed energy resources (IBDERs) introduce major protection challenges due to reduced fault current levels, bidirectional power flows, and control-dependent fault behavior. Under these conditions, short-circuit current calculation and relay protection coordination become tightly coupled, since inaccurate fault modeling directly degrades relay sensitivity and selectivity. This review presents a protection-oriented assessment of state-of-the-art short-circuit calculation and relay protection strategies for AC microgrids. The analysis shows that conventional IEC-based fault models and static overcurrent protection schemes are insufficient for inverter-dominated networks. Generalized Δ-circuit–based modeling framework is identified as the most suitable foundation for microgrid fault analysis, as they enable inverter-aware phasor-domain representation and support both grid-connected and islanded operation. In addition, adaptive relay coordination approaches that incorporate time-varying IBDER participation and fault ride-through behavior demonstrate improved coordination robustness compared to conventional fixed settings, although their practical deployment remains constrained by network topology and communication requirements. Simulation results obtained on a representative microgrid case study confirm that the combined application of protection-oriented short-circuit modeling and adaptive relay coordination significantly improves fault detection reliability and coordination performance. The findings highlight the necessity of jointly addressing fault modeling and protection design to ensure reliable operation of inverter-dominated AC microgrids. Full article

This article settles a new multi-fault-tolerant control problem of an Euler–Bernoulli beam system (EBBS) in the existence of multiplicative faults, additive faults, and control-matched disturbances simultaneously, using the direct adaptive learning control technique. Such an EBBS can be employed to model the vibration of flexible vehicles and flexible manipulators in the actual engineering control. An original hierarchical adaptive boundary control strategy is developed to compensate for multiplicative and additive faults and to reject control-matched disturbances. The classical Lyapunov direct method, together with the variation of Wirtinger’s inequality is utilized to demonstrate the closed-loop system performance. The modified ${\mathcal{C}}_0$-semigroup frame is exploited to certify the well-posedness of its solution under the designed controller. Simulation study on a simply supported beam is demonstrated to verify the validity of the evolved multi-fault-tolerant boundary control algorithm. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article

In central Mexico, ground failure and subsidence have accelerated, as evidenced by the Villa de Reyes graben, particularly at the El Hundido Dam, with the primary cause attributed to groundwater overexploitation. This study integrates electromagnetic profiling (EMP), electrical resistivity tomography (ERT), and transient electromagnetic (TEM) surveys to determine the origin of the fractures at the El Hundido Dam. Based on the TEM survey, a geoelectric section was obtained that models the depth and morphology of the igneous bedrock. At the El Hundido Dam, the igneous basement exhibits convex deformation due to transpressional stresses, which favours the formation of a positive flower-type fault structure. Deformations caused by the basement topography and the fault system are evident in the 100 m-thick Quaternary sequence, as revealed by ERT studies. ERT and EMP surveys showed the presence of a clayey layer that acted as a barrier to surface water infiltration, allowing it to be stored in the past, and which is now destroyed by fractures. Although the drop in the water table has contributed to polygonal cracking, hydro-compaction, and ground subsidence, local tectonics is the primary factor controlling subsoil faulting at the El Hundido Dam. Full article

Background: Solar energy provides a sustainable and environmentally friendly alternative to fossil fuels, and photovoltaic (PV) systems are increasingly deployed worldwide. However, their operational reliability is often compromised by various fault conditions, which reduce power output and shorten system lifespan. Although automated image-based deep learning methods have shown promise for PV fault classification, their performance is often limited by severe class imbalance and subtle, low-contrast defect patterns. This study aims to address these challenges by proposing an improved deep learning framework for robust PV fault classification. Method: An attention-enhanced convolutional neural network framework, termed AEConvNeXt, is proposed for PV fault classification. The model is built on a ConvNeXt-Tiny backbone and incorporates a dropout-regularized Convolutional Block Attention Module (CBAM) to enhance localized feature refinement. To further improve learning under imbalanced data conditions, a hybrid loss function combining Cross-Entropy Loss and Focal Loss is employed. Results: Experimental evaluations demonstrate that AEConvNeXt achieves an overall accuracy of 94.37% and a macro F1-score of 94.43%, outperforming the strongest baseline model, ResNet-50, by more than 3%. Grad-CAM visualizations further confirm that the model effectively focuses on fault-relevant regions, improving interpretability. The proposed framework also shows consistent and robust performance across all six PV fault categories under varying conditions. Conclusions: The proposed AEConvNeXt framework provides an accurate and explainable solution for real-time PV fault detection, effectively addressing class imbalance and improving minority fault recognition. Full article

Cross-noise fault diagnosis remains challenging due to the mismatch between training and testing noise conditions, which degrades feature reliability and model generalization. To address this issue, this paper proposes a reliability-guided adaptive feature fusion framework (RGAF-Net). The method focuses on sample-wise adaptive feature fusion, where the enhanced wide first-layer convolutional neural network(WDCNN) backbone is employed to improve multi-scale feature extraction under noisy environments. In addition, a dual-path architecture is introduced to provide complementary representations, including globally robust structural representations and locally detail-sensitive structural responses. Furthermore, a lightweight reliability estimation module is designed to characterize the signal degradation tendency under noisy conditions of each input sample, based on which a sample-wise routing mechanism dynamically adjusts feature contributions during feature fusion. Experiments on two public bearing datasets (PU and JNU) under cross-noise settings demonstrate that the proposed method achieves improved performance compared with representative approaches, particularly under severe noise conditions. For example, on the JNU dataset at −10 dB, the proposed method improves the Macro-F1 score by over 19 percentage points compared with the baseline WDCNN. Ablation studies and visualization analyses further demonstrate the effectiveness and adaptive fusion behavior of the proposed framework. The results indicate that the proposed method provides an effective solution for robust fault diagnosis under noise mismatch scenarios. Full article