Search Results (2,830)

A large number of practical systems show pronounced fractional-order features. In comparison with integer-order calculus, fractional-order calculus has been demonstrated to possess enhanced precision in the description of the dynamic behavior of complex systems. The increase in control accuracy and flexibility results from this improvement. This study explores a direct-drive wind power generation system featuring permanent magnets, which incorporates fractional-order direct current bus (DC-bus) capacitor and fractional-order inductor–capacitor–inductor (FOLCL) grid-connected filter. For the machine-side rectifier, a fractional-order sliding mode (FOSM) speed outer-loop control and a fractional-order proportional–integral (FOPI) current inner-loop control were designed. A voltage outer-loop control using FOSM and a current inner-loop control using FOPI were developed for the grid-side inverter. Through simulation analyses under various wind speeds and grid fault conditions, it is demonstrated that compared to a control strategy using FOPI controllers in both inner and outer loops, the proposed control scheme which employs a FOSM outer-loop and reduces the overshoot of DC-bus voltage and grid-connected current by 21.51% and 32.49%, respectively, under sudden wind speed changes. Furthermore, during grid voltage sag faults, the maximum drop in DC-bus voltage and grid-connected active power are reduced by 65.38% and 33.38%, respectively. These results highlight the proposed method’s superior dynamic and static performance, as well as enhanced resistance to disturbances. Full article

The electrification of automotive powertrains has accelerated research efforts in the modeling, control, and monitoring of electric drive systems, where reliability, safety, and efficiency are key enablers for mass adoption. Despite a large corpus of literature addressing individual aspects of electric drives, current surveys remain fragmented, typically focusing on either multiphysics modeling of machines and converters, or advanced control algorithms, or diagnostic and prognostic frameworks. This review provides a comprehensive perspective that systematically integrates these domains, establishing direct connections between high-fidelity models, control design, and monitoring architectures. Starting from the fundamental components of the automotive power drive system, the paper reviews state-of-the-art strategies for synchronous motor modeling, inverter and DC/DC converter design, and advanced control schemes, before presenting monitoring techniques that span model-based residual generation, AI-driven fault classification, and hybrid approaches. Particular emphasis is given to the interplay between functional safety (ISO 26262), computational feasibility on embedded platforms, and the need for explainable and certifiable monitoring frameworks. By aligning modeling, control, and monitoring perspectives within a unified narrative, this review identifies the methodological gaps that hinder cross-domain integration and outlines pathways toward digital-twin-enabled prognostics and health management of automotive electric drives. Full article

As automated equipment in PCB manufacturing becomes increasingly reliant on precision hot-air ovens, ensuring operational stability and reducing downtime have become critical challenges. Existing anomaly detection methods, such as Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) Networks, struggle with high-dimensional dynamic data, leading to inefficiencies and overfitting. To address these issues, this study proposes an innovative anomaly detection system specifically designed for fault diagnosis in PCB hot-air ovens. The motivation is to improve accuracy and efficiency while adapting to dynamic changes in the manufacturing environment. The core innovation lies in the introduction of the Adaptive Temporal Feature Map (ATFM), which dynamically extracts and adjusts key temporal features in real time. By combining ATFM with Bi-Directional Dimensionality Reduction (BDDR) and eXtreme Gradient Boosting (XGBoost), the system effectively handles high-dimensional data and adapts its parameters based on evolving data patterns, significantly enhancing fault detection accuracy and efficiency. The experimental results show a fault prediction accuracy of 99.33%, greatly reducing machine downtime and product defects compared to traditional methods. Full article

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development. Full article

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm. Full article

This paper presents a systematic approach to the development and validation of a monitoring and protection system based on the IEC 61850 standard, evaluated through hardware-in-the-loop (HIL) testing. The study utilized an already existing model of a modern power grid consisting of the IEEE 9-bus power system integrated with a large-scale wind power plant (LSWPP). The SEL-487B Relay was configured to protect the PCC using a low-impedance busbar differential monitoring and protection system equipped with adaptive setting group logic that automatically transitions between Group 1 and Group 2 based on system loading conditions. Significant steps were followed for selecting and configuring instrument transformers and implementing relay logic in compliance with IEEE and IEC standards. Real-time digital simulation using Real-Time Digital Simulator (RTDS) hardware and its software, Real-time Simulation Computer-Aided Design (RSCAD), was used to assess the performance of the overall monitoring and protection system, focusing on the monitoring and publishing of the selected electrical and mechanical measurements from a selected wind turbine generator unit (WTGU) on the LSWPP side through the IEC 61850 standard network, and on the behavior of the monitoring and protection system under initial and increased load conditions through monitoring of differential and restraint currents. The overall monitoring and protection system was tested under both initial and increased load conditions, confirming its capability to reliably publish analog values from WTGU13 for availability on the IEC 61850 standard network while maintaining secure protection operation. Quantitatively, the measured differential (operate) and restraint currents were 0.32 PU and 4.38 PU under initial loading, and 1.96 PU and 6.20 PU under increased loading, while total fault clearance times were 606.667 ms and 706.667 ms for faults under initial load and increased load demand conditions, respectively. These results confirm that the developed framework provides accurate real-time monitoring and reliable operation for faults, while demonstrating a practical and replicable solution for monitoring and protection at transmission-level PCCs within renewable-integrated networks. Full article

Bearings are core components in many types of industrial equipment, and their operating environment is often accompanied by strong background noise. This results in a low Signal-to-Noise Ratio (SNR) in the collected vibration signals, making it difficult for traditional methods to extract fault information effectively. Given that bearing failures often manifest as periodic impact signals, a Feature Mode Decomposition (FMD) method has been proposed by researchers which optimizes filter design through correlated kurtosis to enhance the ability to capture fault impact components. However, the decomposition performance of FMD is significantly affected by its parameters (mode number and filter length), and relies on manual settings, resulting in insufficient stability of the results. Therefore, this paper proposes a Dual-domain Impulse Complexity Index (DICI) that combines time-domain impulse characteristics and frequency-domain complexity as an evaluation criterion for FMD parameter optimization. Further, the projection-iterative-methods-based optimizer (PIMO) is adopted to achieve adaptive optimization of parameters. Subsequently, sensitive components are selected based on the maximum Fault Frequency Correlation (FFC) criterion, and their envelope spectra are calculated to recognize bearing fault modes. Simulation and real-signal verification show that the proposed method outperforms several established signal-processing approaches under low SNR conditions. Full article

The long-distance high-voltage transmission tower-line system, frequently traversing active fault zones, is vulnerable to severe symmetry-breaking damage during earthquakes due to asymmetric permanent ground displacements. However, the seismic performance of such systems, particularly concerning symmetry-breaking effects caused by asymmetric fault displacements, remains inadequately studied. This study investigates the symmetry degradation mechanisms in a 1:40 scaled 500 kV tower-line system subjected to cross-fault ground motions via shaking table tests. The testing protocol incorporates representative fault mechanisms—strike-slip and normal/reverse faults—to systematically evaluate their differential impacts on symmetry response. Measurements of acceleration, strain, and displacement reveal that while acceleration responses are spectrally controlled, structural damage is highly fault-type dependent and markedly asymmetric. The acceleration of towers without permanent displacement was 35–50% lower than that of towers with permanent displacement. Under identical permanent displacement conditions, peak displacements caused by normal/reverse motions exceeded those from strike-slip motions by 50–100%. Accordingly, a fault-type-specific amplification factor of 1.5 is proposed for the design of towers in dip-slip fault zones. These results offer novel experimental insights into symmetry violation under fault ruptures, including fault-specific correction factors and asymmetry-resistant design strategies. However, the conclusions are subject to limitations such as scale effects and the exclusion of vertical ground motion components. Full article

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

In this paper, a method to improve the protection of a four-core power cable of a salt-producing floating platform equipped with an automatic breaker with an independent tripping mechanism is suggested. The use of this automatic breaker in combination with a suggested protection device ensures reliable protection of not only the power cables of the platform against all faults but also the personnel of the platform and animals on the reservoir banks against electric shock in the event of a single-phase ground fault in reservoir water. This would be possible due to a voltage sensor made in the form of a metal ring on the power cable and a relay; one terminal of the relay winding is connected to the voltage sensor by a single-core control cable, and the other to the neutral of a power source on the platform. The typically open contacts of this relay are connected to an electric circuit which includes a power source and a coil for an independent tripping mechanism of the automatic breaker. This design ensures reliable operation of the suggested protection device in the event of a single-phase ground fault in the power cable of the platform when underwater cable insulation is damaged. Full article

LoRa is a long-range, low-power wireless communication technology widely used in Internet of Things (IoT) applications. However, its conventional implementation through Long Range Wide Area Network (LoRaWAN) presents operational constraints due to its centralized topology and reliance on gateways. To overcome these limitations, this work designs and validates a gateway-free mesh communication system that operates directly on commercially available commodity microcontrollers, implementing lightweight self-healing mechanisms suitable for resource-constrained devices. The system, based on ESP32 microcontrollers and LoRa modulation, adopts a mesh topology with custom mechanisms including neighbor-based routing, hop-by-hop acknowledgments (ACKs), and controlled retransmissions. Reliability is achieved through hop-by-hop acknowledgments, listen-before-talk (LBT) channel access, and duplicate suppression using alternate link triggering (ALT). A modular prototype was developed and tested under three scenarios such as ideal conditions, intermediate node failure, and extended urban deployment. Results showed robust performance, achieving a Packet Delivery Ratio (PDR), the percentage of successfully delivered DATA packets over those sent, of up to 95% in controlled environments and 75% under urban conditions. In the failure scenario, an average Packet Recovery Ratio (PRR), the proportion of lost packets successfully recovered through retransmissions, of 88.33% was achieved, validating the system’s self-healing capabilities. Each scenario was executed in five independent runs, with values calculated for both traffic directions and averaged. These findings confirm that a compact and fault-tolerant LoRa mesh network, operating without gateways, can be effectively implemented on commodity ESP32-S3 + SX1262 hardware. Full article

The increasing sophistication of grid-connected photovoltaic (GCPV) systems necessitates advanced fault detection and diagnosis (FDD) methods to ensure operation efficiency and security. In this paper, a novel two-stage hybrid AI architecture is analyzed that couples an autoencoder using Long Short-Term Memory (LSTM) for unsupervised anomaly detection with an RF classifier for focused fault diagnosis. The architecture is critically compared to that of a baseline-only RF baseline on a synthetic dataset. The results of this two-stage hybrid AI show a strong overall accuracy of (83.1%). The hybrid model’s first stage trains only on unlabeled healthy data, reducing the reliance on extensive and often unavailable labeled fault datasets. This design has the safety-critical advantage of marking unfamiliar faults as anomalies instead of committing to a misclassification. By integrating anomaly detection with classification, the architecture enables early stage screening of faults and targeted categorization, even in data-scarce scenarios. This offers a scalable, interpretable solution suitable for deployment in real-world GCPV systems where robustness and early detection are critical. While the method exhibits reduced sensitivity to subtle or recurring faults, it demonstrates strong reliability in confidently detecting distinct and significant anomalies. Additionally, the approach improves interpretability, facilitating clearer identification of performance constraints such as the autoencoder’s moderate fault sensitivity (AUC = 0.61). This study confirms the hybrid approach as a very promising FDD solution, in which the architectural advantages of safety and maintainability offer a more worthwhile proposition to real-world systems than incremental improvements in a single accuracy measure. Full article

This paper develops the stability-guaranteed adaptive dynamic programming (ADP)-based fault tolerant control (FTC) for nonlinear systems with an actuator fault. Firstly, a fault observer is designed to identify the unknown actuator fault. Then, a critic neural network (NN) is built to approximate the optimal control of the nominal system. Meanwhile, a stability-aware weight update mechanism is proposed based on the Lyapunov stability theorem to relax the restriction of the initial admissible control on the system stability. By integrating the nominal optimal control and the fault estimation, the stability-guaranteed ADP-based FTC is developed to eliminate the influence of the actuator fault. Furthermore, the observer errors, critic NN weight estimation errors, and the closed-loop system are all shown to exhibit uniform ultimate boundedness by using Lyapunov’s direct method. Finally, simulation examples are given to demonstrate the validity of the proposed method. Full article

Ensuring the stability of surrounding rock in underground excavations is a critical prerequisite for safe mining operations. This study examines the mechanisms of wedge failure formation and determines the performance of a combined support system (rock bolts + shotcrete) through probabilistic analysis. Field investigations in the Zhylandy ore field (Kazakhstan) included fracture mapping, rock mass quality assessment (RQD), fracture frequency (FF), and in situ stress measurements, which confirmed a thrust-faulting regime. Numerical modeling with Dips ver.8 and UnWedge ver.6 software (Rocscience) identified critical excavation orientations of 120° and 141° associated with maximum-volume wedge formation, as well as a “safe orientation window” of 70° ± 10°. The probabilistic analysis showed that rock bolts alone yield a factor of safety (FS) < 1.2, whereas the combined support system increases FS to 2.4–3.5, significantly reducing the likelihood of wedge failures. An adaptive framework integrating numerical modeling with intelligent monitoring (“monitor → update model → adjust support”) is proposed, allowing real-time adjustment of support parameters and optimization of material consumption. The practical significance of this work lies in providing design-ready recommendations for support selection and excavation orientation, contributing to accident prevention and sustainable mining operations. Full article