Search Results (4,022)

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Inter-turn short-circuit faults in induction motors might lead to overheating, torque imbalances, and eventual motor failure. This paper presents a fault detection framework for accurately identifying ITSC faults under various operating conditions. The proposed method integrates negative-sequence current analysis utilizing wavelet-based filtering and symmetrical component decomposition. A fault detection index to effectively monitor motor health and detect faults is presented. Moreover, the fault location is determined by phase angles of fundamental components of negative-sequence currents. Experimental validations were carried out for an inverter-fed induction motor under variable speed and load cases. These showed that the proposed approach has high sensitivity to early-stage inter-turn short circuits. This makes the framework highly suitable for real-time condition monitoring and predictive maintenance in inverter-fed motor systems, thereby improving system reliability and minimizing unplanned downtime. Full article

When faults occur in transmission lines of grid-forming PV systems, the LVRT control and virtual impedance function cause the fault characteristics of grid-forming inverters to differ significantly from those of synchronous generators, which deteriorates the performance of existing protection schemes. To address this issue, this paper analyzes the fault characteristics of PV transmission lines under grid-forming control objectives and the adaptability of traditional current differential protection. Subsequently, a novel pilot protection based on the Jensen–Shannon distance is proposed for transmission line of grid-forming PV systems. Initially, the post-fault current samples are modeled as discrete probability distributions. The Jensen–Shannon distance algorithm quantifies the similarity between the distributions on both line ends. Based on the calculated distance results, internal and external faults are distinguished, optimizing the performance of traditional waveform-similarity-based pilot protection. Simulation results verify that the proposed protection reliably identifies internal and external faults on the protected line. It demonstrates satisfactory performance across different fault resistances and fault types, and exhibits strong noise immunity and synchronization error tolerance. In addition, the proposed pilot protection is compared with the existing waveform-similarity-based protection schemes. Full article

This paper presents a new methodical procedure to monitor in real time the junction temperature of SiC Power MOSFET modules of parallel-connected chips utilized in machine drive systems to develop their reliability modelling and predict their lifetime. The paper implements the on-line measurements of temperature-sensitive electrical parameters (TSEP) approach, particularly the quasi-threshold voltage and the on-state drain to source voltage, to estimate the junction temperature in real time. The proposed procedure firstly applied computational fluid dynamics analysis on the module under study to determine the chip which undergoes the maximum junction temperature during typical operation of the module. Then, a calibration phase, using double-pulse tests on the selected chip, is used to generate look-up tables to relate the TSEPs under study to the junction temperature. Next, the real-time estimation of junction temperature was accomplished during the on-line operation of the three-phase inverter, taking into account the induced distortion/noises due to operation of the parallel-connected chips in the module. After that, a comparison between the two TSEPs under study was provided to demonstrate their advantages/drawbacks. Finally, reliability modelling was developed to predict the lifetime of the studied module based on the estimated junction temperature under a predetermined mission profile. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

This paper presents a microcontroller-based technique for accurately measuring resistive sensors over a wide dynamic range using an adaptive constant current source. Unlike conventional voltage dividers or fixed-current methods—often limited by reduced resolution and saturation when sensor resistance varies across several decades—the proposed system dynamically adjusts the excitation current to maintain optimal Analog-to-Digital Converter (ADC) input conditions. The measurement circuit employs a fixed reference resistor and an inverting amplifier configuration, where the excitation current is generated by one or more pulse-width modulated (PWM) signals filtered through low-pass RC networks. A microcontroller selects the appropriate PWM channel to ensure that the output voltage remains within the ADC’s linear range. To support multiple sensors, an analog switch enables sequential measurements using the same dual-PWM current source. The full experimental implementation uses four op-amps to support modularity, buffering, and dual-range operation. Experimental results show accurate measurement of resistances from 1 kΩ to 100 kΩ, with maximum relative errors of 0.15% in the 1–10 kΩ range and 0.33% in the 10–100 kΩ range. The method provides a low-cost, scalable, and digitally controlled solution suitable for embedded resistive sensing applications without the need for high-resolution ADCs or programmable gain amplifiers. Full article

Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article

Grid-forming (GFM) inverters have recently seen wider adoption in microgrids and inverter-based-resource (IBR)-penetrated grids, and are primarily used to establish grid voltage under a wide array of conditions. In the existing literature, GFM control is almost exclusively applied using voltage-source inverters (VSIs). However, due to the inherent limitations of available semiconductor devices’ current ratings, inverter-side current must be limited in VSIs, particularly during grid-fault conditions. These limitations complicate the real-world application of GFM functionality in VSIs, and complex control methodologies and tuning parameters are required as a result. In the following study, GFM control is instead applied to a buck-type current-source inverter (CSI) using a combination of linear droop-control and finite-control-set (FCS) mode-predictive control (MPC) that will be referred to herein as hybrid model-predictive control (HMPC). The resulting inverter features a simple topology, inherent current limiting capabilities, and a relatively simple and intuitive control structure. Verification was performed on a 1MVA/630V system via MATLAB/Simulink, and the simulation results demonstrate strong performance in voltage establishment, power regulation, and low-voltage ride through under-grid-fault conditions, highlighting its potential as a competent alternative to VSIs in GFM applications, and lacking the inherent limitations and/or complexity of existing GFM control methodologies. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Refractory full-thickness macular holes (rFTMHs) present a significant challenge in vitreoretinal surgery, with reported incidence rates of 4.2–11.2% following standard vitrectomy with internal limiting membrane (ILM) peeling and gas tamponade. Risk factors include large hole size (>400 µm), chronicity (>6 months), high myopia, incomplete ILM peeling, and post-operative noncompliance. Multiple surgical techniques exist, though comparative evidence remains limited. Current options include the inverted ILM flap technique, autologous ILM transplantation (free flap or plug), lens capsular flap transplantation (autologous or allogenic), preserved human amniotic membrane transplantation, macular subretinal fluid injection, macular fibrin plug with autologous platelet concentrates, and autologous retinal transplantation. Closure rates range from 57.1% to 100%, with selection depending on hole size, residual ILM, patient posturing ability, etc. For non-posturing patients, fibrin plugs are preferred. Residual ILM cases may benefit from extended peeling or flap techniques, while large holes often require scaffold-based (lens capsule, amniotic membrane) or fibrin plug approaches. Pseudophakic patients should avoid posterior capsular flaps due to lower success rates. Despite promising outcomes, the lack of randomized trials necessitates further research to establish evidence-based guidelines. Personalized surgical planning, considering anatomical and functional goals, remains crucial in optimizing visual recovery in rFTMHs. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

In the traditional classification system of the Lauraceae family based on morphology and anatomy, the phylogenetic position of the genus Sassafras has long been controversial. Chloroplast (cp) evolution of Sassafras has not yet been illuminated. In this study, we first sequenced and assembled the complete cp genomes of Sassafras, and conducted the comparative cp genomics, phylogenomics, and divergence time estimation of this ecological and economic important genus. The whole length of cp genomes of the 10 Sassafras ranged from 151,970 bp to 154,011 bp with typical quadripartite structure, conserved gene arrangements and contents. Variations in length of cp were observed in the inverted repeat regions (IRs) and a relatively high usage frequency of codons ending with T/A was detected. Four hypervariable intergenic regions (ccsA-ndhD, trnH-psbA, rps15-ycf1, and petA-psbJ) and 672 cp microsatellites were identified for Sassafras. Phylogenetic analysis based on 106 cp genomes from 30 genera within the Lauraceae family demonstrated that Sassafras constituted a monophyletic clade and grouped a sister branch with the Cinnamomum sect. Camphora within the tribe Cinnamomeae. Divergence time between S. albidum and its East Asian siblings was estimated at the Middle Miocene (16.98 Mya), S. tzumu diverged from S. randaiense at the Pleistocene epoch (3.63 Mya). Combined with fossil evidence, our results further revealed the crucial role of the Bering Land Bridge and glacial refugia in the speciation and differentiation of Sassafras. Overall, our study clarified the evolution pattern of Sassafras cp genomes and elucidated the phylogenetic position and divergence time framework of Sassafras. Full article

In the present study, a polarization rotation switch (PRS)-based all-optical ternary inverter circuit and ternary clocked SR flip-flop (TCSR) are proposed and discussed. The present scheme is designed by the polarization rotation of light in a waveguide coupled with a micro-ring resonator (MRR). The proposed scheme uses linear polarization-encoded light. Here, the ternary (radix = 3) logical states are expressed by the different polarized light. PRS-MRR explores the polarization-encoded methodology, which depends on polarization conversion from one state to another. All-optical ultrafast switching technology is employed to design the ternary NAND gate. We develop the ternary clocked SR flip-flop by employing the NAND gate; it produces a greater number of possible outputs as compared to the binary logic clocked SR flip-flop circuit. The performance of the proposed design is measured by the Jones parameter and Stokes parameter. The results of the polarization rotation-based ternary inverter and clocked SR flip-flop are realized using a pump–probe structure in the MRR. The numerical simulation results are confirmed by the well-known Jones vector (azimuth angle and ellipticity angle) and Stokes parameter (S₁, S₂, S₃) using Ansys Lumerical Interconnect simulation software. Full article

LCL (inductor–capacitor–inductor) filters are widely used in grid-connected inverters, particularly in electric vehicle (EV) battery-to-grid systems, for harmonic suppression but introduce resonance issues that compromise stability. This study presents a novel sensorless active damping strategy based on μ-synthesis control for EV batteries connected to the grid via LCL filters, eliminating the need for additional current sensors while preserving harmonic attenuation. A comprehensive state–space and process noise model enables accurate capacitor current estimation using only grid current and point-of-common-coupling (PCC) voltage measurements. The proposed method maintains robust performance under ±60% LCL parameter variations and integrates a proportional-resonant (PR) current controller for resonance suppression. Hardware-in-the-loop (HIL) validation demonstrates enhanced stability in dynamic grid conditions, with total harmonic distortion (THD) below 5% (IEEE 1547-compliant) and current tracking error < 0.06 A. Full article