Search Results (740)

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure signatures by stochastic noise. To address these challenges, we propose SDGR-Net, a spatiotemporally decoupled learning framework designed to model the complex degradation dynamics of HDDs. SDGR-Net introduces three synergistic innovations: (1) a spatiotemporally decoupled dual-branch encoder that disentangles longitudinal temporal evolution from cross-variable correlations via parameter-isolated branches, thereby reducing representational interference; (2) a parsimonious dual-view temporal extraction mechanism that captures early-stage anomalies through forward–reverse sequence concatenation, enabling high-fidelity preservation of non-stationary pre-failure patterns; and (3) a cross-branch dynamic gated residual fusion module that functions as an adaptive information bottleneck to emphasize failure-critical features while suppressing redundant noise. Extensive experiments conducted on three heterogeneous HDD datasets, ST4000DM000, HUH721212ALN604, and MG07ACA14TA, demonstrate that SDGR-Net consistently outperforms six state-of-the-art baselines. In particular, SDGR-Net achieves a peak fault detection rate (FDR) of 0.9898 and a 69.6% relative reduction in false alarm rate (FAR) under high-reliability operating conditions. These results, corroborated by comprehensive ablation studies, indicate that SDGR-Net effectively balances detection sensitivity and operational robustness, offering a practical solution for intelligent HDD health monitoring. Full article

Due to their vast potential, Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various applications. To prevent in-flight failures and loss of control, implementing Internet of Things (IoT)-based Predictive Maintenance (PdM) systems is crucial. However, data collected from PdM systems often contains outliers, which can significantly degrade the accuracy and performance of predictive models. In this paper, we present a comparative performance analysis of several outlier detection methods, namely K-Nearest Neighbors (KNN), Autoencoder (AE), and Isolation Forest (IForest). The datasets used to evaluate these methods were acquired from a UAV predictive maintenance system designed to estimate the Remaining Useful Life (RUL) of Li-ion batteries and detect faults in Brushless DC (BLDC) motors. Ultimately, this study aims to determine the most effective outlier detection method for UAV predictive maintenance datasets. Full article

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances ($Z_a,Z_b,Z_c$) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

The stable and accurate output of the inertial measurement unit array (IMU) of a micro-electro-mechanical system (MEMS) is the key to ensuring the data fusion of the MEMS IMU array. However, due to the large number of MEMS IMUs contained in the MEMS IMU array, it is susceptible to interference and has difficulty avoiding failures. The output of the MEMS IMU contains noise, outliers, and other related errors, which can seriously lead to low fault detection and isolation accuracy in the MEMS IMU. In this study, a new method of fault detection and isolation based on multivariate variational mode decomposition (MVMD), a whale optimization algorithm (WOA), and a generalized likelihood test (GLT) is proposed, which is called WOA-MVMD-GLT. Firstly, a multi-index fitness function WOA is proposed to optimize the parameters of MVMD. Secondly, MVMD is used to extract the features of the MEMS IMU’s signals. Finally, a GLT is used to construct a fault detection function and a fault isolation function to detect and isolate the faults of gyroscopes and accelerometers. The experimental results show that the method proposed in this paper can significantly reduce the false alarm rate and false isolation rate. Full article

As a key metering device in the smart grid, the clock battery status of smart meters directly affects the operational efficiency and economy of the grid. In response to the limitations of current evaluation methods in feature correlation analysis and model interpretability, this study proposes a knowledge-and-data-driven low battery voltage status prediction method. We systematically dissected the physical mechanisms underlying battery undervoltage faults and constructed a status features knowledge graph comprising 17 state features across four dimensions. By employing Pearson correlation analysis and association rule mining techniques, we achieved a quantitative correlation analysis between multi-source heterogeneous features and battery status. Building on this foundation, we developed an interpretable model framework based on XGBoost-SHAP. Empirical studies utilized a dataset of 939,000 faulty meters recalled by a provincial power company in 2023, with 9.87% of outlier samples eliminated using the Isolation Forest algorithm during preprocessing. Results demonstrate that the proposed model achieved an R² of 0.851 and a Mean Squared Error (MSE) of 0.0088 on the test set. The prediction performance significantly surpassed that of Random Forest (R² = 0.692) and MLP+BP neural networks (R² = 0.583), thereby validating the effectiveness of the approach in combining predictive accuracy with decision transparency. Full article

Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered. Full article

In traditional wind turbine (WT) operation and maintenance, fault diagnosis and repair have long been relied on, yet the demand for continuous operation under faults persists. To address this, this study proposes a triggered fault-tolerant control framework for wind turbines with zonotope-based interval estimation. The method enhances safety from point to range estimation of FDI, reduces network traffic load via a WT load region-based adaptive event-triggered mechanism, and enables fast, robust fault diagnosis/isolation using interval residuals. A damage equivalent load (DEL)-sensitive cost term balances structural fatigue suppression while ensuring power tracking and safety constraints. Theoretically, Linear Matrix Inequality (LMI) conditions based on common quadratic Lyapunov ensure closed-loop stability and bounded observation errors, with proven interval residual fault sensitivity and triggering reliability. Numerically, on the standard NREL 5-MW WT model under multi-conditions (turbulence, faulty communication), it achieves an average power tracking accuracy of 95.56%, 28.68% fatigue suppression, and 67.40% bandwidth saving. Overall, it synergistically optimizes robust estimation, economical communication, and fatigue-aware control, providing a theoretically rigorous and experimentally validated technical framework for engineering-scale WT reliability improvement and lifespan extension. Full article

This paper highlights a communal fault detection and isolation framework integrating a convolutional neural network (CNN) with a finite-state machine (FSM). The proposed concepts ensure state-based controlled discriminate pattern recognition and enable the diagnosis of different anomalies in the mobile robot in a multi-robot environment. The framework processes the time-series sensor data through the convolution layer upon experiencing different types of fault and governs different states based on fault diagnosis and recovery. The proposed concept has been validated using a Python 3.11 and Webot environment featuring the shrimp robot in a multi-robot arena. The model obtained an accuracy of 97% in identifying and classifying faults, enabling automated recovery of faulty robots in the multi-robot environment. Experiments conducted on different simulators demonstrate that effective fault management can be achieved with low training loss. Full article

Transient modeling of short-circuit faults in the DC power system of hybrid electric aircraft (HEA) serves as a fundamental basis for effective fault identification, localization, and isolation. Before faults are detected and protective measures are taken, distributed sources and loads maintain their normal control strategies. However, previous studies frequently overlook the impact of these control dynamics on the transient behavior of DC power systems, leading to reduced accuracy in fault transient models. Therefore, this paper proposes a fault transient modeling method for the DC power system of HEA considering the control effects of distributed sources and loads. Firstly, the transient characteristics of all components in the system are analyzed, including generators and fan motors, batteries and DC load, and supercapacitors. Subsequently, a comprehensive fault transient model of the HEA DC power system is established. Finally, the validity of the proposed method is verified through comparison with results from a semi-physical test platform. The results demonstrate that the proposed modeling approach enhances the accuracy of transient analysis for the faulty HEA DC power systems. Full article

Data in multi-tenant cloud environments is increasingly shared across organizations, making strong in-memory isolation a critical requirement. Existing confidential computing mechanisms such as AMD SEV provide hardware-enforced protection, but they require specialized processors and incur non-trivial performance overhead, which limits their deployment in heterogeneous clouds. This paper presents DASPRI, a software-based approach that constructs an isolated execution environment for trusted virtual machines by combining dual address spaces with privilege restriction. DASPRI partitions physical memory into a normal region and an isolated region on NUMA systems, and steers all memory allocations of trusted VMs into the isolated region by monitoring page faults and kernel allocation paths. It further hardens the isolated region by mediating direct and dynamic kernel mappings and by maintaining separate page caches for trusted and normal VMs. Remote attestation is integrated to protect the integrity of metadata used to identify trusted VMs. We implement DASPRI on a HUAWEI Kunpeng AArch64 server running OpenEuler and evaluate it using microbenchmarks and UnixBench. Experimental results show that DASPRI enforces strong memory isolation with less than 5% overhead on basic system operations and only 1.3% degradation in overall host performance. Full article

The Mw 6.4 Petrinja earthquake on 29 December 2020 ruptured the Petrinja-Pokupsko fault system in central Croatia, producing widespread coseismic deformation and subsequent postseismic processes. This study examines ground displacements in the Petrinja area from 2019 to 2022 using Sentinel-1 SAR data processed with SBAS time series analysis. Interferometric phase residuals were filtered using temporal coherence masking and RMS cut-off criteria to ensure high-quality displacement estimates. Line-of-sight (LOS) velocity fields were derived separately for ascending and descending tracks, combined into horizontal and vertical components, and rotated into a fault-parallel direction. Fault-parallel velocities were also extracted with pixel-wise coseismic offsets removed to isolate postseismic transients. Pre-event displacements are generally small and often within measurement uncertainties. However, because the 2019–2022 observation window includes the mainshock and concentrated early postseismic motion, robust estimation of long-term interseismic rates (millimeters per year) is not possible from this dataset. Such rates from independent regional GNSS measurements are therefore included solely for tectonic context and visual illustration. A clear surface displacement jump exceeding 20 cm was detected, with opposite signs in ascending and descending geometries, reflecting predominant right-lateral strike-slip motion. Following the removal of the coseismic jump, weighted profile analysis identifies residual transients of up to ±1.5 cm/yr near the fault, consistent with dominant shallow afterslip. Possible contributions from viscoelastic relaxation are noted, as such processes produce broader, longer-timescale deformation patterns that cannot be excluded without extended observations or forward modeling. These geodetic observations quantify the immediate postseismic deformation and provide constraints on near-fault slip patterns following the mainshock. Full article

Intelligence, surveillance, and reconnaissance (ISR) platforms and electric vertical take-off and landing (eVTOL) aircraft demand onboard power conversion systems that simultaneously achieve high gravimetric power density, robustness, and fault-tolerance. In this context, modular battery architectures based on per-string power electronic interfaces emerge as a key enabler for voltage regulation, fault isolation, and in-flight reconfiguration. However, the stringent mass and volume constraints of electric aviation place magnetic components among the primary limiting factors of converter scalability. This paper presents a design methodology for interleaved converters with coupled inductors that explicitly decompose common-mode, differential-mode, and uncoupled inductance components. The proposed approach enables independent adjustment of current ripple and dynamic response, allowing zero-voltage switching (ZVS) operation while ensuring stable and controllable behavior under close-loop current regulation. The methodology is experimentally validated on a 4 kW two-phase interleaved GaN-based boost converter operating at 500 kHz. Experimental results demonstrate a peak efficiency of 97%, with less than 1% variation across the operating range, and stable dynamic behavior under load transients. These results confirm the effectiveness of the proposed design methodology as a scalable solution for high-power-density, high-reliability power converters in electric aviation battery systems. Full article

The continuous functioning of any industrial manufacturing facility, especially critical infrastructures, has become crucial in the current multi risk context. Monitoring and detection of anomalies carries multiple significant practical benefits that are direct Industry 4.0 goals, and some of them improve resiliency and sustainability—implicit targets of Industry 5.0. For this reason, the current paper explores the usage of feedforward autoencoder neural networks for anomaly detection. The proposed approach is designed to capture deviations in the overall operational behavior of a plant, enabling system-wide monitoring rather than being constrained to the identification of specific, predefined fault scenarios. The obtained autoencoder was subject to further experimental testing on synthetic data, and a direct comparison with five other anomaly detection methods (Z-Score, Interquartile Range, Isolation Forest, One-Class Support Vector Machines, and Local Outlier Factor) proved superior performance from the autoencoder in terms of precision, recall, and F1 score. The foreseen case study was focused on data from a real drinking water treatment plant. Full article

Low-voltage ride-through (LVRT) capability is essential for grid-connected photovoltaic (PV) systems, especially as rising renewable integration challenges grid stability during voltage disturbances. Existing LVRT methods often target isolated control functions, leading to limited system resilience. This paper presents a unified control strategy integrating DC-link voltage regulation, reactive power injection, and overvoltage mitigation using a coordinated fuzzy logic framework. The proposed architecture employs a cascaded control structure comprising an outer voltage loop and an inner current loop with feed-forward decoupling, synchronized via a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). At its core is a dual-input, single-output Fuzzy Logic Controller (FLC), featuring optimized membership functions and dynamic rule-based logic to manage multiple control objectives during grid faults. The proposed FLC-based unified LVRT controller for grid-tied PV system was implemented and validated for both symmetrical and asymmetrical fault conditions in MATLAB/Simulink 2023b platform. The proposed FLC-based LVRT controller achieves voltage sag compensation of 97.02% and 98.4% for symmetrical and asymmetrical faults, respectively, outperforming conventional PI control, which achieves 94.02% and 96.5%. The system maintains a stable DC-link voltage of 800 V and delivers up to 78% reactive power support during faults. Fault detection and recovery are completed within 200 ms, complying with Bangladesh grid code requirements. This integrated fuzzy logic approach offers a significant advancement for enhancing grid stability in high-renewable environments and supports reliable renewable utilization, and more sustainable grid operation in developing regions. Full article

This study presents a fault-tolerant control framework for an EMA-based active suspension under yaw-rate and roll-rate sensor faults. Instead of deactivating active suspension functions in the presence of sensor failures, the proposed approach maintains vertical load redistribution within feasible operating conditions. A hierarchical control structure is employed, integrating a multi-residual-based fault detection and isolation scheme with sensor-reliability-based control reconfiguration. The EMA is modeled at the force level, enabling direct integration into vehicle-level dynamics without explicitly modeling internal electrical dynamics. The proposed method is evaluated using ISO 3888-1 double lane change simulations, where peak tire vertical forces and combined tire forces are used as performance metrics. Simulation results indicate that the proposed framework mitigates excessive load concentration compared to passive suspension under sensor fault conditions. Full article