Search Results (122)

Reliable state-of-charge (SOC) estimation is essential for lithium-ion battery management, yet parameter drift, operating-profile variation, and sensor faults can compromise observer consistency. This study presents a reproducible constrained FFRLS-EKF framework in which online second-order RC parameter updates are subjected to resistance, capacitance, and time-constant feasibility constraints before being scheduled in the EKF. Estimator residuals and parameter variations are then reused for exploratory concurrent fault analysis. Because the dynamic driving-cycle datasets do not provide independently measured continuous reference SOC, SOC RMSE/MAE is not reported for DST, FUDS, UDDS, US06, or BJDST; Coulomb counting is treated only as a non-independent trajectory reference because it also contributes to the FFRLS regression target. A separate 21-checkpoint HPPC validation, with reference labels withheld from the estimator, yields SOC RMSE/MAE values of 2.24/1.76 percentage points for the constrained adaptive method, compared with 2.50/2.04 percentage points for the fixed EKF. A 270-run robustness study varies fault magnitude, onset time, voltage-noise level, and initial SOC. The results identify physical projection as the dominant stabilizing mechanism, with adaptive forgetting providing secondary transient-memory adjustment. An additional 243-run two-fault stress test shows that residual-sensitivity decoupling is not universally identifiable: exact-pair recovery degrades as noise increases and remains strongly dependent on the operating profile and fault pair. Accordingly, the concurrent fault module is presented as a transparent diagnostic baseline rather than a universally validated fault-isolation method. Full article

The output quality of random-number sources directly affects the security of cryptographic systems. Physical-noise degradation, environmental disturbance, device aging, and fault injection may increase output bias, correlation, and predictability. This paper presents a resource-conscious on-chip entropy-quality supervisor for random-number source output streams. The design uses non-overlapping 1024-bit measurement windows and a shared feature engine for bit counts, directional transition counts, and run information. These features support repetition-count, adaptive-proportion, and low-toggle checks, together with two-bit pattern-concentration and first-order conditional-transition indicators, exponentially weighted moving-average trend monitoring, comprehensive scoring, and a seven-bit alarm bitmap. The RTL accepts a 32-bit valid-data interface, makes one decision every 32 valid words, and is integrated into an Artix-7 XC7A35T project configured with a 50 MHz system-clock constraint. The complete project includes a ring-oscillator TRNG, and controlled deterministic fault patterns are inserted into selected windows of the TRNG stream for fault-response verification. Deterministic RTL simulations show complete alarm mappings of 0111111 for fixed-value patterns, 0011000 for an isolated alternating window, and 1011000 for the fourth consecutive alternating window. A parameterized capture simulation also verifies pre-event, injection, and recovery sequencing. FPGA implementation results show that the entropy-supervisor core uses 1009 LUTs and 276 flip-flops without BRAM or DSP resources, while the complete project uses 1313 LUTs, 614 flip-flops, and one BRAM tile. The design meets the 50 MHz clock constraint with a WNS of 1.079 ns and no setup or hold violations. The total on-chip power reported by Vivado is 0.076 W; without simulation-derived switching activity, this value is approximate and is not a board measurement. The results establish the functional behavior of the monitoring and decision paths. The two-bit pattern-concentration and first-order conditional-transition indicators are empirical tools for online anomaly diagnosis; neither is a min-entropy estimator, and they do not replace source-specific entropy assessment under NIST SP 800-90B. Full article

Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes $n_m$, the filter length L, the CK shift order M, and the characteristic-period scaling $T_{\mathrm{scale}}$—whose manual tuning is impractical for multi-channel floating offshore wind turbine monitoring deployments. We propose GLO-FMD, an adaptive denoising framework coupling the Gray Langurs Optimizer (GLO) with FMD. GLO autonomously optimizes the FMD parameters, thereby aligning the CK objective with structural modal periods rather than impulsive fault periods. Although the search space spans $(n_m,L,M,T_{\mathrm{scale}})$, the CK shift order M is fixed at 2 and $T_{\mathrm{scale}}$ is estimated automatically from the dominant autocorrelation peak; consequently, only $(n_m,L)$ are actively optimized. The optimized FMD decomposes multi-axis tower-base signals into band-limited modes through iterative CK-maximizing FIR filter optimization; each mode identifies a dominant periodic component, and the original signal is zero-phase band-pass filtered around the identified frequencies to preserve physical phase during reconstruction. Validation employs (i) semi-synthetic signals reproducing the measured tower-base structure (a smooth $0.15$ Hz structural mode plus an impulse-excited $3.77$ Hz resonance) with exactly known ground truth—a best-case benchmark by construction that isolates denoising capability from reference uncertainty—and (ii) real strapdown inertial sensor data acquired at 8 Hz from the tower-base interface of a floating offshore wind turbine at an operational site in Chinese coastal waters, over a six-day measurement campaign (18–23 April 2023). Six kinematic channels spanning triaxial acceleration (north, up, east) and triaxial velocity (north, up, east) are analyzed, with 200-s (1600-sample) continuous windows extracted for algorithmic evaluation. On the semi-synthetic data, GLO-FMD achieves a $9.6$–$10.2$ dB SNR improvement over default wavelet thresholding against the known ground truth, and the GLO optimization is essential for reliability—the default FMD configuration is unstable across noise realizations, whereas the optimized parameters recover the clean components consistently. GLO-FMD also achieves pseudo-reference-relative SNR gains of 5.3–7.8 dB over default wavelet thresholding across all six real-data channels. Bootstrap resampling over 12 independent segments confirms statistical significance ($p<0.001$, Cohen’s $d>8$), and a no-reference smoothness index provides complementary evaluation independent of the pseudo-reference assumption. Multi-day consistency analysis yields coefficients of variation below 5%, demonstrating short-term consistency across the environmental conditions represented in the six-day dataset. The online denoising stage requires approximately 1.5 s per channel, supporting potential deployment on edge-computing hardware at the turbine controller level. Full article

This paper investigates the fault-detection and isolation problems for T-S fuzzy systems with actuator faults and sensor faults. To begin with, the zonotopic set-memberships of both the state and output are set up. After this, by taking the intersection of these two zonotopic set-memberships as the state-estimation zonotopic set-membership, an interval state estimation method is developed. In order to obtain an optimization-interval estimation, a parameterized correction matrix is introduced into the zonotopic set-membership construction. Aiming at some optimization goal, the computation of the parameterized correction matrix is given by LMIs. Moreover, fault detections for both actuator and sensor faults are accomplished, and fault isolation between actuator and sensor faults is also fulfilled by constructing proper residuals using the optimization-state interval estimation. Finally, a simulation example is given to verify the effectiveness of the proposed method. Full article

Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses. This paper proposes an integrated solution combining high-precision insulation monitoring and intelligent fault line selection, which ensures the reliability of line selection criteria through improved measurement accuracy and achieves automatic fault isolation via optimized line selection strategies. The paper analyzes the mathematical essence of the ill-conditioned measurement equations of the traditional bridge method under severe single-pole grounding faults, establishes a dual-channel heteroscedastic noise model, and utilizes the inherent physical constraint that the sum of the positive and negative pole-to-ground voltages always equals the bus voltage to transform the ill-posed inverse problem into an equality-constrained optimal estimation problem, deriving an analytical solution in the sense of constrained least squares. A collaborative monitoring strategy of “balanced bridge monitoring first, unbalanced bridge precision measurement afterward” is proposed. An automatic fault line selection and isolation algorithm based on sequential branch switching is designed, which leverages the operational characteristic that PV systems allow short-term branch interruption, enabling automatic identification and isolation of faulty branches and automatic restoration of non-faulty branches without installing any leakage current sensors. Experimental results show that under severe fault conditions with a single-pole insulation resistance as low as 22 kΩ, the proposed method limits the error to within 5%; the proposed line selection strategy can complete identification and isolation of all faulty branches within at most two rounds of switching. Full article

This paper presents an interval-observer-based actuator fault detection and isolation (FDI) method for underactuated autonomous underwater vehicles (AUVs) under bounded hydrodynamic uncertainty and time-varying ocean currents. A locally frozen linear time-invariant (LTI) representation enables deterministic set-membership analysis, and the robust adaptive propagated interval observer (RAPIO) propagates admissible center–radius state bounds within a Lyapunov framework. Adaptivity is introduced through a reinforcement learning (RL)-augmented uncertainty-bound modulation mechanism, where an offline-trained agent scales a nonnegative channel-wise slack term without modifying the scheduled observer-gain rule or the nominal center predictor. Under the stated observer and disturbance-envelope conditions, positivity, stability, and diagnostic-channel inclusion hold for any bounded learning signal. Actuator loss-of-effectiveness (LoE) faults are represented through the actuator-effectiveness channel and detected through interval-consistency violations, enabling axis-wise isolation of surge, yaw-rate, and pitch-rate actuator faults. The same schedule-blind decision layer is additionally evaluated with structurally distinct additive-bias and stuck/jam actuator models. All stuck/jam events are detected, and bias-magnitude sweeps identify channel-wise 100%-detection boundaries with zero false alarms. A structured 72-case scenario sweep shows reliable detection, strong false-alarm rejection, and acceptable detection delays compared with benchmark observers. Full article

Persistent Scatterer Interferometry (PSI) provides dense and temporally continuous measurements of ground deformation, offering a robust framework for investigating, among other phenomena, earthquake-related surface deformation. However, most satellite-based investigations of large earthquakes remain focused on coseismic interferograms, source inversions, and short post-seismic observation windows. In this study, we propose a PSI-based parametric approach that, given a Persistent Scatterer (PS) time series, uses a piecewise linear regression with an imposed coseismic step at the earthquake origin time to estimate the pre-event line-of-sight (LOS) velocity, the coseismic displacement step, and the post-event LOS velocity using ascending and descending satellite observations. The methodology is applied to the 7 January 2025 M_w 7.1 Dingri earthquake (southern Tibet), a recent large normal-faulting event for which previous studies have documented complex rupture behavior and significant co- and post-seismic surface deformation. The results show that our PSI-based approach enables, within a single framework, the isolation of the coseismic jump, the quantification of post-event velocity patterns, and the systematic comparison of pre- and post- event deformation. In addition, the combination of ascending and descending datasets yields a first-order reconstruction of the vertical and east–west deformation components. The proposed approach complements physics-based source modeling by offering a scalable, observation-driven, and point-wise characterization of deformation evolution. Full article

Two-Phase TMR conserves energy by partitioning redundancy operations into two stages and making the execution of the third task copy optional, yet it remains susceptible to permanent faults. Reactive TMR (R-TMR) counters this by isolating faulty cores, handling both transient and permanent faults. However, the lightweight hardware required by R-TMR not only increases complexity but also becomes a single point of failure itself. To bypass isolated node constraints, this paper proposes a Fault Tolerance and Isolation TMR (FTI-TMR) algorithm for interconnected multicore systems. We construct a stability metric characterized by its prior and posterior estimates to identify the most reliable nodes in the system. These nodes then perform periodic diagnostics to isolate permanent faults. The experimental results show that FTI-TMR reduces task workload by approximately 30% compared with baseline TMR, while achieving higher permanent-fault coverage. Full article

Reliable fault detection and isolation (FDI) for actuators and sensors in underactuated autonomous underwater vehicles (AUVs) is challenging because nonlinear hydrodynamic coupling redistributes fault signatures, and persistent ocean currents can trigger false alarms. This paper presents a reinforcement learning (RL)-augmented dual-layer FDI framework for actuator and sensor faults in underactuated AUVs. The actuator layer uses a robust adaptive propagated interval observer (RAPIO) that evaluates thruster and control-surface residuals against a calibrated dynamics-consistency tube. The sensor layer forms estimator-consistency residuals for Doppler velocity log (DVL), depth, and inertial measurement unit (IMU) measurements against a reference-separated finite-time extended state observer (FTESO). An offline-trained soft actor–critic (SAC) policy schedules bounded actuator uncertainty margins and sensor alarm thresholds according to operating confidence. The scheduled actuator error dynamics remain Metzler and Hurwitz, preserving positive interval propagation and center-error input-to-state stability (ISS) independent of policy convergence. A Schmitt-trigger alarm and signal-space disambiguation rule classify healthy, actuator-only, sensor-only, and simultaneous-fault conditions under explicit residual-separation and persistence conditions. Across 72 simultaneous-fault episodes over a $4\times 6$ uncertainty–current grid, the proposed method achieved $100\%$ detection coverage for both actuator and sensor faults with only five false-alarm events, retaining full coverage in the severe-current, high-uncertainty subset where the selected actuator and sensor baselines achieved only 88.9% and 70.4% detection, respectively, with more false alarms. These results indicate that the proposed bounded RL scheduler can deliver reliable, certifiable actuator and sensor fault diagnosis under significant operational uncertainty. Full article

The integration of data centers (DCs) into distribution systems introduces new challenges for reliability assessment. It also provides operational flexibility through the spatial migration of computational workloads. However, existing reliability assessment methods have not sufficiently captured the joint effects of DC workload flexibility, post-fault network reconfiguration, and multiple heterogeneous faults. To address this gap, this paper proposes an optimization model-based reliability assessment framework for distribution systems with integrated DCs. A DC workload dispatch model is first developed by considering server operation, cooling demand, backup requirements, and inter-DC workload transfer limits. The post-fault operation process is then formulated as a two-stage optimization problem, including fault isolation and service restoration. The proposed framework co-optimizes network reconfiguration and DC workload migration under line faults, node faults, and remote-controlled switch failures. The resulting problem is reformulated as a mixed-integer linear programming model and solved using commercial solvers. Case studies on a modified IEEE 123-node test feeder show that DC workload migration can significantly reduce expected energy not supplied by exploiting the spatial flexibility of interconnected DCs. The results also demonstrate that neglecting node faults and switch failures may lead to overly optimistic reliability estimates. Additional tests on a modified 356-node regional distribution system further verify the applicability and scalability of the proposed framework. The proposed method provides a physically interpretable reliability assessment tool for active distribution systems with DC integration and heterogeneous fault mechanisms. Full article

Sensor-based environmental monitoring systems are often affected by missing, noisy, and unreliable measurements caused by sensor faults, sparse deployment, calibration drift, and communication interruptions. To address these challenges, this study proposes an intelligent virtual sensor generation framework that integrates physical-constraint-based preprocessing, statistical virtual sensor modeling, KL-divergence-based fusion, deep generative augmentation, and temporal prediction. The raw weather-station data are first refined using threshold-based filtering, physical validity constraints, and Isolation Forest-based outlier detection. To handle the circular nature of wind direction, the angle is encoded using sine and cosine components during modeling and reconstructed using the atan2 function for evaluation. Multiple statistical methods, including Inverse Distance Weighting, Kernel Density Estimation, Ridge Regression, and Copula-based modeling, are employed to generate complementary virtual sensor data. These outputs are adaptively fused using KL divergence according to their distributional similarity with real sensor data. The fused datasets are further augmented using Variational Autoencoders and Conditional Tabular Generative Adversarial Networks, and then evaluated using BiLSTM and BiGRU models with MAE, MSE, and RMSE metrics. The experimental results demonstrate that the proposed framework generates physically valid and distributionally consistent virtual sensor data. Fusion-based methods outperform standalone approaches, while VAE-based augmentation generally provides better statistical fidelity and lower prediction errors than CTGAN. Additional validation using a public NOAA weather-station dataset further supports the transferability of the proposed fusion-based virtual sensing workflow. Comparisons with TimeGAN and diffusion-based temporal generative baselines, supported by Wilcoxon signed-rank testing, confirm the statistical significance and competitive performance of the proposed framework. A quantitative computational analysis also demonstrates the practical feasibility of the framework in terms of training time, inference time, memory consumption, and scalability. Overall, the proposed framework offers a reliable and scalable solution for virtual sensing in sensor-sparse and fault-prone environmental monitoring systems. Full article

This paper presents a hybrid algorithmic framework for fault detection and isolation (FDI) in nonlinear quadrotor unmanned aerial vehicle (UAV) systems operating under closed-loop conditions. The proposed method integrates a Linear Quadratic Control (LQC) strategy, synthesized through Linear Matrix Inequalities (LMIs), with a Conditional Long Short-Term Memory Autoencoder (CLSTM-AE) and an adaptive residual-based decision mechanism. The LQC scheme provides robust trajectory tracking through regional pole-placement constraints, while the CLSTM-AE learns the nominal closed-loop input–output temporal behavior of the UAV using only fault-free data. In contrast to conventional symmetric autoencoder-based detectors, the proposed CLSTM-AE uses the control inputs together with the available attitude estimates, represented by the Euler angles yaw, pitch, and roll, as conditioning information, while reconstructing only the monitored attitude outputs. This asymmetric structure allows the residuals to capture inconsistencies between the commanded control effort and the observed attitude response, which is particularly relevant in closed-loop nonlinear systems where feedback compensation may attenuate fault signatures. Deviations from nominal behavior are detected through reconstruction residuals computed using a smoothed Mean Squared Error (MSE) criterion and evaluated against an adaptive $3\sigma$ threshold. The framework is validated in three-dimensional flight simulations considering abrupt, transient, and incipient actuator fault scenarios. The obtained results show that the proposed approach outperforms representative conventional machine-learning methods, achieving an average accuracy of 98.2%, an average recall of 97.8%, and an average false positive rate of 1.4%. These results suggest that the proposed hybrid algorithm provides an effective and interpretable solution for closed-loop fault diagnosis in nonlinear UAV systems under measurement noise and system variability. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article

The large-scale penetration of Distributed Energy Resources (DERs), the proliferation of Energy Communities, and the increasing provision of flexibility services are fundamentally transforming distribution network operation, rendering traditional Distribution Management Systems (DMSs) structurally inadequate. This paper addresses this structural gap by proposing and experimentally validating A-ISolE, a novel hybrid Artificial Intelligence (AI) architecture that natively integrates centralized and distributed intelligence within a unified DMS framework. The core scientific contribution of this work lies in the formulation and deployment of a coordinated, hierarchical AI paradigm in which cloud-level predictive and optimization modules dynamically interact with edge-level autonomous control agents. Specifically, the paper introduces: (1) an integrated forecasting state estimation pipeline with AI-enhanced grid observability; (2) intelligent fault location and optimal feeder reconfiguration algorithms embedded into operational control loops; and (3) distributed edge control strategies enabling autonomous yet coordinated microgrid stabilization. The architecture is validated on a real pilot microgrid in Sanremo (Italy). Experimental results demonstrate quantifiable gains in many parameters, substantiating the feasibility of hybrid centralized/distributed AI as a foundational paradigm for future resilient and decarbonized distribution networks. Full article