The design and exploitation of high-property cathode materials and the exploration of their energy storage mechanism have always been research hotspots in the area of zinc-ion hybrid capacitors (ZHCs). In this study, the new RuO2 nanodots/reduced graphene oxide (RuO2 NDs/rGO) composite is designed, and employed as a cathode for ZHC for the first time. Thanks to the synergism of nanoscale design and composite engineering, the RuO2 NDs/rGO//Zn ZHC delivers large specific capacitance (169.5 mAh/g at 0.1 A/g), splendid rate property (74.4 mAh/g at 20 A/g), eminent cyclic property (up to 10,0 0 0 cycles), and high energy and power densities (101.7 Wh/kg and 12 kW/kg). Furthermore, systematic kinetic analyses are used to confirm the rapid ion transport kinetics of the RuO2 NDs/rGO//Zn ZHC. More importantly, systematic ex-situ measurements are employed to illustrate its energy storage mechanism of the coexistence of electric double-layer capacitance (physical adsorption/desorption of SO4 2- ) and pseudocapacitance (insertion/extraction of Zn2 + and H+ and chemical adsorption/desorption between Zn2 + and oxygen-containing functional groups). This study not only offers a good strategy for the design and exploitation of high-performance pseudocapacitive cathode for ZHCs, but also proposes an insight into energy storage mechanism of RuO2 -based pseudocapacitive cathode. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

Fatigue crack growth in SLM-fabricated Ti-6Al-4V cannot be described adequately by the stress intensity factor alone, because manufacturing conditions alter the defect population and microstructural anisotropy that govern crack propagation. Here, a hierarchical prediction framework is developed in which feature interpretation, physical consistency, and parameter optimization are treated as interdependent parts of the modelling problem. A graph attention network (GAT) is first used to extract the relative importance of six process–load variables and to initialize the BPNN input layer. The crack growth rate is then learned in the logarithmic domain, ensuring physically admissible (da/dN>0). Bayesian optimization determines the network architecture and training hyperparameters, while a GA–PSO strategy combines global exploration with local refinement of the network weights and biases. The framework is trained on 5,646 measurements from 21 controlled experimental conditions and evaluated further using two unseen process–load combinations. The resulting GA–PSO–BPNN model gives R2=0.94, RMSE=0.0016, and MAPE=6.41%, and reproduces both da/dN–ΔK relationships and fatigue-life evolution across different process and loading states. GAT analysis identifies ΔK and load amplitude as the principal drivers, while laser power, scanning speed, and build orientation influence crack growth through defect formation and anisotropy. Under unseen conditions, the model retains the most accurate and stable predictions among the compared approaches. The results establish a physically consistent and interpretable route for fatigue crack growth assessment of additively manufactured Ti-6Al-4V.

ObjectiveExcessive computational cost is encountered in traditional Monte Carlo simulation (MCS) method for structural reliability analysis. Poor high-dimensional fitting performance and large memory occupation under low failure probability conditions are presented in Kriging surrogate model. An active learning structural reliability analysis method combining adaptive extreme learning machine (AELM) surrogate model with MCS method was proposed.MethodsFirstly, an AELM surrogate model was constructed, and the number of hidden layer nodes and activation function were optimized to improve the generalization ability of the model. Secondly, a Bayesian framework was introduced to model the uncertainty of hidden layer weights, and the posterior distribution was calculated to estimate the prediction variance. The problem that extreme learning machine cannot directly quantify prediction uncertainty was solved. Then, a active learning function was constructed to select the sample points with the maximum uncertainty for iterative model updating. Finally, the failure probability stability criterion was adopted as the convergence condition, and the structural failure probability was calculated in combination with the MCS method.ResultsThe results show that compared with the Kriging surrogate model algorithms, the number of limit state function calls of the proposed method is reduced by up to 71.64%, and the calculation error is as low as 0.10%. The method has both higher computational efficiency and accuracy in engineering problems with multiple failure domains, strong nonlinearity and low failure probability, and provides a reference for engineering structural reliability analysis.

This article proposes a force rebalance control scheme based on a mode-localized resonant accelerometer (ML-RXL), which is applied to address the limited measurement range problem of the ML-RXL. For the first time, an empirical response model of the weakly coupling resonators for the amplitude ratio output is established. Based on this, this paper builds an overall model of the force rebalance control system to analyze the sensitivity characteristics by simulations, which demonstrates that the scheme can effectively broaden the linear measurement range. It is demonstrated that the sensor exhibits a highly linear output within a measurement range of ±1 g, with a sensitivity of the feedback-control voltage output measured at 2.94 V/g. The measurement range is expanded by at least 6.7 times. Moreover, the results show that the minimum input-referred acceleration noise density of the sensor for the force rebalance control scheme is 3.29 μg/rtHz, and that the best bias instability is optimized to 5.34 μg with an integral time of 0.64 s.

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types.

Inter-turn short circuit (ITSC) faults are among the most critical and frequent failures in power transformer windings. However, conducting a quantitative analysis of the winding insulation state based on MFL remains challenging. This paper proposes a magnetic-electrical spatial state model that links local fault currents to leakage magnetic field variations. A data-driven fault localization framework is developed by combining recursive feature elimination (RFE), Spearman correlation analysis, and support vector machine (SVM) classification. Experimental validation on a 3 kW dry-type transformer, enhanced with FEM-based signal augmentation, shows that the method achieves 97.4% fault localization accuracy under rated load using only 20 Hall-effect sensors. Under no-load conditions, the accuracy remains 92.3%, demonstrating robustness against weak excitation and electromagnetic noise. The optimized sensor layout in the winding gap enhances spatial sensitivity while minimizing hardware complexity. These results confirm the method's potential for scalable, non-intrusive insulation monitoring in practical power transformers.

Remaining useful life (RUL) prediction for power transformers plays a key role in safeguarding the stability and security of power systems. However, due to the dual imbalance in both fault categories and fault severity within transformer monitoring data, it becomes challenging for predictive models to effectively extract key features from minority class samples, thereby limiting prediction accuracy. In order to solve this problem, a novel approach for RUL prediction is proposed, combining oversampling and reweighting techniques. First, a three-step data oversampling strategy is developed to balance the fault category distribution. Next, the Adaptive Boosting (Adaboost) regression method is employed to assign higher weights to samples with key features that are difficult for the model to learn, thereby enhancing the model’s predictive performance for minority classes. Finally, the proposed method's effectiveness is validated using real-world transformer data.

Given that the total labeled fault signals of rolling bearings are difficult to collect, it is urgent to address the challenge of limited labeled vibration signals for intelligent fault diagnosis. In this case, the combination of artificial features and machine learning (ML) techniques is a potential way to handle this problem, because ML techniques have simple structures so that large amounts of labeled signals are not demanded in the training process when compared to deep learning (DL)-based approaches. However, the artificial features usually have a bias since they are based on experts' subjective experience, which may lead to performance degradation of the ML-based approaches. To address this gap, a self-supervised framework of artificial feature bias rectification (SFAFBR) is proposed. Specifically, the considered artificial features of vibration signals are from wavelet packets (WPs), which are combined with deep networks by serving as guides to coax deep network learning. The learned features are closely relevant to the original artificial features but the bias is rectified due to the sparse expression of the deep model. Then, on top of that, an ML-based classifier is constructed to perform the final diagnosis. The proposed framework achieved the state-of-the-art diagnosis accuracy of 97.79% and 94.84% on two bearing datasets with just five labeled samples. The result shows that using deep networks to assist in rectifying artificial feature bias is a promising way to address the fault diagnosis problem of rolling bearings under limited labeled vibration signals.

Inter-turn short circuit (ITSC) faults are among the most critical internal faults in power transformers, often causing severe equipment degradation and economic loss if not detected in time. However, traditional detection methods based on terminal electrical parameters lack sensitivity to early-stage or mild ITSCs. To address this challenge, this paper proposes a novel ITSC fault localization method based on abnormal leakage magnetic field tracing. An electromagnetic field coupling model is first established to analyze the spatial redistribution of leakage flux caused by shorted turns. The resulting magnetic anomaly is then formulated as an inverse problem, in which the fault location and severity are reconstructed by minimizing the discrepancy between simulated and observed flux distributions. A two-dimensional particle swarm optimization (TD-PSO) algorithm is introduced to solve this inverse problem, offering efficient convergence and high localization accuracy. Both simulation and experimental results demonstrate that the proposed method achieves an average localization error of 4.18% in simulation and 9 mm in experimental validation. Compared to conventional methods, it exhibits superior performance in sensitivity and robustness, especially under weak fault conditions. This study provides a physics-informed, optimization-driven framework for accurate and timely localization of ITSC faults, offering new insights for transformer condition monitoring and predictive maintenance.

Fe-Si-B-P-Cu-C amorphous alloys with high amorphous forming ability and saturation magnetic induction were developed by optimizing B and P concentrations alongside Ni doping. Gas-atomized Fe80.5Si0.5B10.5P5Cu0.5C2Ni1 powders exhibited full amorphous structure, achieving a high saturation magnetic induction of 180 emu/g after annealing at 420 degrees C. The annealed powders demonstrated excellent soft magnetic properties, including high permeability and low core loss. Phosphoric acid passivation further reduced core loss by 26 % while retaining a high saturation magnetic induction of 176.8 emu/g. These properties make the alloy a promising candidate for high-power-density and miniaturized magnetic applications. (c) 2025 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper investigates the dynamic characteristics of the mode-localized resonant accelerometer (ML-RXL) both in small-signal and large-signal models. The analytical model of ML-RXL bandwidth at various amplitude ratio (AR) operating points (OPs) was derived and validated through simulations and experiments. For the small-signal model, the OP with larger AR results in a further broadening of the ML-RXL’s bandwidth. Specifically, when AR is from 1 to 4, the effective bandwidth is expanded from 46Hz to 152Hz. For large-signal model, this study reveals the emergence of multiple harmonic peaks in the AR output response curve, which become more pronounced as the intensity of the dynamic acceleration signal increases at a given OP. The findings indicate that the bandwidth of ML-RXL is constrained by mode frequency difference and dynamic acceleration signal intensity, and it can be expanded by adjusting the operating point. 2024-0209

Accurately determining the internal temperature field distribution of transformers under different operating conditions is crucial for ensuring their reliable operation. However, due to the complex structure of transformer housing, directly obtaining the heat transfer coefficient for various operating states using empirical formulas presents a significant challenge. To accurately determine the internal temperature field distribution of transformers, this paper proposes a novel method for dynamically calculating the heat transfer coefficient of transformer housing. Initially, a high-precision multiphysics simulation model is established. For analyzing the housing and important factors influencing the temperature field, laying the foundation for constructing the dynamic heat transfer coefficient model for the housing. Subsequently, a mathematical fitting model is developed based on the identified key factors, and genetic algorithms are employed to optimize the dataset, ensuring accurate estimation of the heat transfer coefficient under various operating conditions. Finally, the temperature field distribution is verified through reverse engineering, and the internal temperature field of the transformer is determined by combining the dynamic heat transfer coefficient with the operational data of the transformer. The proposed method not only accurately reflects the distribution of the transformer's temperature field but also provides a new solution for ensuring the stable operation of transformers.

Interturn short-circuit (ITSC) faults represent a critical threat to the operational reliability of power transformers, often developing rapidly and eluding early detection. Existing diagnostic methods face challenges in fault localization due to limited observability, low sensitivity to internal disturbances, and high background interference. This article presents a sensor-integrated digital twin (DT) framework for real-time ITSC fault localization, driven by micromagnetic field analysis. A finite element-based DT model is constructed to simulate the spatial distribution of leakage magnetic flux under various ITSC scenarios. Magnetic field signals are acquired from an optimized array of Hall-effect sensors deployed along the transformer winding surface. To bridge the gap between observed magnetic responses and internal fault evolution, a spatiotemporal data fusion and inversion strategy is developed. A regularized and gradient-informed 2-D particle swarm optimization (PSO) algorithm is employed to estimate fault parameters by minimizing magnetic field mismatch under physical constraints, enabling accurate localization across fault positions and severities. Experimental validation demonstrates that the proposed DT-assisted method significantly improves the localization accuracy and responsiveness of sensor-based fault diagnostics. This approach provides a practical pathway for deploying real-time, noninvasive monitoring systems in transformer health management.

As one of the most common types of internal faults in power transformer, interturn short-circuit faults can lead to a rise in temperature and damage the lifetime of the transformer. However, it is challenging to accurately localize fault in time with the existing techniques. Digital twin (DT), with its capabilities of integrating and synchronizing the virtual and physical worlds, is an essential tool for accurately monitoring the internal state and health of transformers. It not only enables the timely detection and resolution of issues but also improves maintenance efficiency and reduces costs. In this article, a multisensor data fusion method for capturing hot spot based on microthermal field DT is proposed to improve the fault localization accuracy and timeliness. First, a DT model of temperature distribution based on microthermal field is constructed by finite element analysis (FEA). The raw temperature signals in multisensors installed in the oil channel are collected by value game. Second, the key information for monitoring the spatiotemporal variation is obtained by conjoint analysis. A data fusion method of capturing hot spot temperature is proposed. Finally, through feature extraction, a diagnosis method based on Bayesian inference is developed for different degrees of faults according to different oil flow line symmetry characteristics. The simulations and practical results all show that the proposed method can not only ensures the accuracy of the localization but also improves the timeliness of the detection. This work provides a novel solution for the localization of interturn short-circuit fault and is of constructive idea for solving the fault diagnosis problem by physical fields DT.

The power transformer is crucial to the power system. Traditional transformer protection measures cannot quickly and sensitively identify coil-related faults. Magnetic flux leakage (MFL), as an intermediate product of energy conversion during the operation of a transformer, undergoes changes when there is a coil fault in the transformer. However, there is limited research on the analysis and fault diagnosis of the MFL field before and after transformer faults. In this study, a three-dimensional finite element simulation model of the transformer is established. The simulation model data are analyzed to select appropriate measurement paths. Through local analysis of measurement point data and overall analysis of measurement lines, the periodic characteristics of the transformer MFL field distribution are further elucidated. Find the significant MFL change characteristics after transformer inter-turn short circuit fault, and try to analyze the causes of the characteristics. This article proposes to use the peak point detection algorithm- Automatic Multiscale-based Peak Detection (AMPD) algorithm to find the peak point of the characteristic waveform, and realize the fault diagnosis of the transformer through the MFL data. The research supplements the correlation analysis of the MFL characteristics of inter-turn short circuit faults in transformers. The proposed method provides new insights into the detection, localization, and assessment of inter-turn short circuit faults in transformers.

Magnetostrictive vibration energy harvester (MVEH) has obvious advantages in output stability, strain capacity and electromechanical coupling. For MVEH, multiple bidirectional coupling of mechanica-magnetic-electric and nonlinear characteristics occur in the process of energy conversion. So, the models based on the linear piezomagnetic equation will have large prediction errors in the output characteristic analysis. In this study, the magnetostrictive material, Galfenol alloy, is used as the core component of harvester, and the magnetic properties of material are tested under different magnetic excitation and compressive stress. Using the Gibbs free energy as theoretical underpinning, the nonlinear constitutive model of the Galfenol material is constructed. The inverse hyperbolic sine function is introduced to characterize the saturation effect and nonlinearity of materials. The unknown parameters in the model are identified by nonlinear least square method according to the magnetic properties test data. Then, for MVEH, considering the influence of dynamic vibration force, magnetic flux leakage and bias magnetic field, a mechanical-magnetic-electrical nonlinear three-port equivalent circuit for harvester is constructed. In the circuit, the nonlinear controlled voltage source is used to express the coupling relationship between each port. Finally, a prototype harvester made of two Galfenol rods can withstand large vibration forces is designed, and the change trends of the output voltage of the harvester are studied under dynamic vibration force and different load resistance. The results calculated by the proposed model are highly consistent with the experimental ones, which shows the validity of the nonlinear equivalent circuit model for MVEHs.