Accurate imbalance detection in turbocharger cores under ultra-high-speed operation is challenging.A high-pressure air-driven balancing test system was developed.The system architecture was built based on high-speed rotor dynamics.A universally adaptable rigid support structure was designed using balancing machine principles.Key parameters of the air-drive structure were optimized via simulation, and an auxiliary air-drive component was proposed.The constructed system underwent performance and imbalance identification tests.The results show that  the system drives the core to a maximum balancing speed of 
35 480 r/min, achieving a 918% speed increase with the auxiliary component, enabling tests at working speeds.
 The rigid support accurately replicates the core’s vibration characteristics across all speeds.The imbalance identification error remains below 10% at the core’s working speed, verifying the system’s effectiveness and accuracy.

As a high-precision and high-efficiency mechanical transmission component,the ball screw plays a vital role in precision transmission processes of computer numerical control machine tools, aerospace and other fields.The rigidity performance of the ball screw will directly affect the positioning accuracy and motion smoothness of the equipment.At present, research on rigidity mainly focuses on theoretical modeling, and the exploration of the dynamic variation laws for rigidity of ball screws during service is relatively insufficient.The existing rigidity measurement methods are time-consuming and inefficient.In order to effectively monitor the stiffness values of the ball screw during operation, a dynamic identification method was proposed for identifying the axial stiffness of the ball screw based on multi-source signal fusion and multi-view broad learning.Firstly, an automatic interception method was designed for constant speed running section signals in high-frequency to extract effective running section information from multi-source signals.Secondly, empirical mode decomposition was used to filter the signals to eliminate noise and retain key features.Then, the Pearson correlation coefficient was employed to extract features and screen out signal components highly correlated with stiffness.Finally, in order to improve the accuracy of stiffness identification, the neural network and multi-view broad learning system (NN-MVBLS) was enhanced by leveraging the lightweight advantages of broad learning and powerful information extraction capability of neural networks.The NN was used as a feature extractor to conduct deep information mining on selected features with high correlation and strong independence, and the output was then used as the feature input for MVBLS to complete stiffness identification.Experiments show that the stiffness identification accuracy of the NN-MVBLS model reaches 92.7%, showing higher accuracy compared with other models.

Traditional bounding surface constitutive models for sand often struggle to accurately describe the continuous evolution from the solid state to liquefied state under cyclic loading.To address this limitation, a stiffness and dilatancy degradation formulation was proposed to precisely capture the stiffness reduction, large shear strain development, and excess pore pressure generation in the quasi-liquefied state.Based on undrained cyclic triaxial tests, an excess pore water pressure ratio of rE=0.9 was adopted as the criterion for quasi-liquefaction.Numerical simulations of representative sand under various initial relative densities and confining pressures demonstrate that the improved model accurately reproduces stress paths, shear strain accumulation, and the continuous progression of the sand liquefaction process under cyclic loading.Validation against shaking table tests further confirms the reliability of the proposed approach.The outcomes provide a robust framework for constitutive model improvement, liquefaction criterion determination, and dynamic response analysis of liquefiable sites.

To effectively control the structural vibration induced by metro operation in collapsible loess strata, a method combining on-site measurement and numerical simulation was adopted to analyze the vibration characteristics and propose targeted control measures.The research results show that the vibration energy of the subway is mainly concentrated in the frequency band of 55-80 Hz, and it has significant randomness and discreteness.The piecewise function design of the response spectrum model based on the 95th quantile of the response spectrum can effectively envelope the maximum dynamic response of the structure.Based on this, non-stationary artificial waves that can envelope measured recordings are synthesized.Compared with the non-isolated structure, after adopting the three-dimensional variable stiffness isolation bearing (3D-VSIB), the peak horizontal acceleration of the isolation structure decreases from approximately 6.500 m/s2 to less than 1.700 m/s2, and the peak vertical acceleration decreases from 1.790 m/s2 to less than 0.124 m/s2.The natural frequency of the isolation structure deviates from the main vibration frequency band of the metro, realizing wideband isolation within the range of 31.5-80.0 Hz.The vibration level of each floor stably drops from above 80 dB to below 70 dB, meeting the relevant specification limits.This provides an effective technical path for vibration control of metro operation in collapsible loess strata.

The single-axis solar trackers are prone to torsional aerodynamic instability under strong winds, which can lead to severe damage to the support structure.The layout patterns of different driving column have a certain influence on the critical wind speed at which the photovoltaic support experiences aerodynamic instability.To enhance the overall critical wind speed of the photovoltaic support, this study conducts field measurements of the natural vibration characteristics of single-axis solar trackers.The finite-element calculation method was adopted for cross-verification.The influence rules of factors such as the spacing of drive column on the torsional natural vibration frequency were explored.By combining with the wind-tunnel test of the full aeroelastic model, the optimal ratio of driving column spacing and the critical wind speed under different inclination angles were calculated, providing reference for practical engineering applications.The results indicate that the inclination angle has basically no impact on the torsional natural vibration frequency.The torsional natural vibration frequency in the middle region decreases as the ratio of the span between the middle region and the end region increases, while the torsional natural vibration frequency at the end region shows the opposite trend.Under all wind directions, the 0° tilt angle exhibits a higher critical wind speed and is recommended as the protective angle for high wind conditions.For vertical wind direction and a 20° wind angle, the optimal driving column spacing ratio is suggested to be 3.62, while a spacing ratio of 4.37 is advised for a 45° wind angle.

For the debonding damage assessment at the concrete-foundation ring interface in wind turbine foundations, an intelligent recognition method based on multi-domain feature fusion and extreme gradient boosting （XGBoost）iwas proposed. The vibration response signal of the concrete around the foundation ring was collected by the acceleration sensor, and seven7 types of time-domain features, five5 types of frequency-domain features, and wavelet packet time-frequency-domain features were extracted to construct a high-dimensional feature matrix; after eliminating the differences in data scales throughby Z-score standardization, PCA dimensionality reduction (,with the top 10 principal components accounting for 95.7% of the cumulative variance) ,was adoptedemployed to optimize feature inputs; finally, the XGBoost classification model was used to realize the identificationrecognition of the degree of foundation debonding damage (normal, incipientpreliminary damage, moderate damage, and severe damage). Results of laboratory tests (involving 2000 samples) indicate show that the classification accuracy of the proposed method reaches 99.0%, with all accuracy rates of 5-fold cross-validation being ≥ 99.0%. This method significantly outperforms the SVM (86.3%), MLP (96.3%) and Random Forest (97.0%) models. The classification accuracy of the method on field test data is 87.0%, which preliminarilyinitially verifies its engineering applicability.

Dynamic signals of aircraft tire hydroplaning wereare susceptible to external noise interference, which may affect the assessment of hydroplaning state and summarization of hydroplaning mechanisms. In this paper, a hybrid denoising algorithm based on empirical mode decomposition(EMD) and singular value decomposition(SVD) was proposed. Intrinsic mode function(IMF) components dominated by noise were eliminated dueowing to low correlation coefficient. Proportional coefficients were reasonably determined via a series of trial-and-error tests, in accordance with hydroplaning signals characteristics. Both signal-to-noise ratio(SNR) and root mean square error (RMSE) were adopted to evaluate the denoising effect. Denoising case studies were subsequently carried out by accounting for different aircraft landing conditions, hydroplaning data types and signal sources, to verify the effectiveness and feasibility of the proposed algorithm. Research results indicated show that EMD-SVD SVD-based algorithm can effectively denoise the additional water resistance signal of tires, with SNR exceeding 15.0 and RMSE below 1.0. The denoising performances are is superior to those that of empirical formula fitting method. The proposed algorithm is applicable for to common aircraft landing taxiing parameter conditions. As for signals of tire-pavement contact area and vertical supporting force, a satisfactory denoising effect is achieved while preserving the key characteristics. Denoised curves are smooth and hydroplaning limit states are clear; thus thus,subjective deviation of traditional criteria can be overcome. The algorithm exhibits an outstanding denoising effect on the experimentally measured dynamic signals of hydroplaning force, which facilitates the identification of the characteristic values of hydroplaning force and its evolution laws under different pavement conditions. The denoising algorithm may serve as a reference for related research and data post-processing.

To explore the effect of rotor-stator interaction mechanism on the unsteady flow, the paper used the SST k-ω model was used to calculate the centrifugal pump flow under multiple operating conditions. The time-frequency characteristics and deterministic decomposition method were employedused to analyze the flow characteristics. The results show that the head uncertainty of the grid is 1.03%, the efficiency uncertainty is 1.53%, and the maximum error between the simulation and experiment is 4.40%. The main characteristic frequency frequencies of pressure pulsation and radial force are both the blade passing frequency(BPF). The pressure pulsation amplitude is positively correlated with the flow rate. The radial force amplitude is related to the circumferential  uneven distribution of pressurepressure uneven distribution. The jet wake is caused by the velocity gradient between the blades, and the flow blockage effect originates from the volute circumferential asymmetric structure. These two factors together induce The two together create unsteady characteristics. The unsteady characteristics are more significant under low flow , and have temporal features. ,It iswhich are related to the flow separation on the blade suction side. The unsteady characteristics under high flow have significant spatial features. ,which areIt is related to the impeller outflow velocity. The study clarified clarifies the unsteady mechanism under multiple operating conditions. It ,provides providing a basis for vibration noise control of centrifugal pumps.

The relative rotation angle between the disks during the operation of the circumferential rod rotor will change the contact state of the disk joint interfaces, making the bending stiffness of the rod rotor difficult to predict.The operation of the circumferential rod fastening rotor entails relative rotation angles between disks, which alter the contact state between contact interfaces and complicate the prediction of the rotor's bending stiffness. The digital model of rough surface was generated using the autocorrelation function method, and the contact between the disks was simulated at micro and macro scales via the finite element method, which resulted in the relationship between the contact stiffness or sectional moment of inertia and relative rotation angle. Subsequently, an equivalent bending stiffness model for the contact interfaces was formulated, incorporating factors such as the relative rotation angle between disks, contact surface roughness, and tie-rod preload force. Model analysis reveals that the equivalent bending stiffness remains constant until the relative rotation angle reaches the interval node θc, whereupon it undergoes a steep decline, followed by a linear increase beyond the interval node θd. Notably, the equivalent bending stiffness and interval nodes θc and θd all increase with an increase in preload, while the equivalent bending stiffness is negatively correlated with surface roughness. The proposed bending stiffness model provides a valuable theoretical reference for the dynamic analysis and design of rod fastening rotors.

The inerter, as a novel passive structural control element, is introduced into the aircraft landing-gear oleo-pneumatic shock-absorption system, which can enhance drop-impact buffering performance and expand the structural design space without significantly increasing the system mass. Based on the ISD (Inerter–Spring–Damper) structure theory, dynamic models of three buffering configurations, namely SD, ISD1 (parallel inerter), and ISD2 (series inerter), were established. Numerical simulations and parameter optimization were completed in MATLAB, and a systematic comparison was conducted in terms of peak load, maximum stroke, and shock-absorption efficiency. The results show that, under the stroke constraint, the ISD1 configuration can simultaneously reduce the peak load and improve the shock-absorption efficiency, whereas the performance gain of the ISD2 configuration is limited and it requires higher inertance and damping parameters. Based on force decomposition and instantaneous power analysis, the energy-improvement mechanism of the inerter is clarified: in the ISD1 configuration, the inerter branch absorbs and temporarily stores the impact energy during the early compression stage, and releases it with feedback in the mid-to-late stage, realizing time-domain redistribution of the impact energy, thereby producing a “peak-shaving and valley-filling” load response and reducing the system’s reliance on high damping energy dissipation; in the ISD2 configuration, constrained by the series same-force coupling, the energy storage and feedback effects of the inerter branch are difficult to be fully exploited, resulting in limited overall benefit. Furthermore, a Simcenter 3D-AMEsim co-simulation model is developed to perform dynamics–hydraulics coupled simulation, and its results are consistent with the MATLAB simulations in terms of load-response trends and energy-distribution characteristics, verifying the reliability of the conclusions.

Underwater blasting has been widely applied in infrastructure construction. As an important technique tofor protecting against the hazards of underwater explosion shock waves, the study of protective mechanisms and technical parameters of bubble curtains is of great significance for to the safety of underwater blasting engineering. To investigate the influence of bubble curtain morphology on protective effect, the attenuation rate of peak shock wave pressure at measurement points was used as the evaluation index. Firstly, laboratory water tank explosion tests were conducted for verification, and then numerical models with different bubble morphology distributions in the curtain were established by using LS-DYNA program combined with self-developed codes. The results show that the presence of a bubble curtain can significantly alter the propagation of underwater shock waves. Even a low air flow rate can remarkably attenuate the shock waves, and increasing the number of bubbles enhances the curtain’s protective performance, with the final attenuation rate exceeding 95%. At the same air volume, reducing bubble size significantly improves the protective effect, and this characteristic is more pronounced at lower air flow rates. 

We cannot evaluate the safety of the human neck under impact loading through experiments on human subjects. A detailed biomechanical finite element model of the neck offers a valuable simulation tool for investigating neck injury risk under impact conditions. However, currently established neck models often lack comprehensive validation, particularly in the vertical direction, and extensive verification is a prerequisite for broad application of the human model. Therefore, this study first establishes a detailed, anatomically-based neck model, including the main tissue components of the human neck, such as vertebrae, intervertebral discs, facet joints, ligaments, and muscles, with defined constitutive properties and corresponding material parameters for each component. Then, the model was validated against horizontal impact based on the experimental data from literature (2 frontal collision conditions, 1 rear collision condition and 1 lateral condition ). Subsequently, volunteer ejection tests were conducted under three near-vertical conditions, with the collected response data used tofurther validate the model in the vertical condition. In total, the model was validated across seven conditions, and the results show a high degree of agreement between the simulation and the experimental results. This suggests its potential for predicting neck injury risk in diverse impact scenarios, offering a valuable simulation tool for the design of protective devices and their safety evaluation.

This study investigates the damage mechanisms of tungsten alloy long-rod projectiles hypervelocity penetrating concrete targets at hypervelocity using experimental and numerical methods. Penetration tests were conducted with projectiles (length-to-diameter ratio = 10) accelerated by a two-stage light gas gun (57/10). The hypervelocity penetration process was simulated using the LS-DYNA finite element software. Key results indicateshow that: (1) Penetration depth exhibits an "inverse reduction phenomenon," initially increasing then decreasing with the increase of impact velocity, peaking at approximately 2.5 km/s. (2) Concrete crater morphology consistently shows a "crater plus tunnel" mode; crater diameter, depth, and radial crack quantity increase significantly with the increase of velocity. Radial cracks demonstrate a "transitional" surge near 3.5 km/s. (3)The spall phenomenon in concrete targets is induced by the interaction between the stress waves generated by projectile penetration and the tensile waves reflected from the target’s rear surface. Moreover, the spall location moves closer to the target’s back surface as the impact velocity increases. (4) Increasing the projectile length-to-diameter (L/D) ratio that is less thanbelow 10 enhances penetration capability. These findings elucidate damage mechanisms in concrete under hypervelocity penetration and can inform the design of critical protective structures against hypervelocity threats and the optimization of kinetic energy weapons.

To elucidate the distribution characteristics and underlying mechanisms of dynamic load on shaft walls in layered rock-soil media under lateral blast, this study employs an integrated approach, including 1:5 scaled model explosion tests, numerical simulations, mechanistic analysis, and data fitting. The axial and circumferential distribution features of the dynamic load were examined. The causes of asymmetric axial load distribution were revealed, influencing factors and distribution patterns were analyzed, and characterization methods for both axial and circumferential load distributions were developed. The results show that the effectiveness of a soil modeling approach combining Lagrangian and Eulerian grids was validated, and the relative errors of key parameters in test and numerical simulation results are within ±20%.When the blast source is located in the sand layer, the influence of load from the rock layer on the structural response is negligible, thus the analysis can be focused on the asymmetric load within the sand layer.Reflected waves from the rock layer induce asymmetry in the axial distribution of the shaft wall load. As the scaled distance increases, the position of the maximum reflected load shifts upward.The circumferential distribution correlates weakly with the reflected wave, while the axial distribution is effectively modeled by the superposition of the dynamic loads generated by the incident and reflected waves. Thus,the proposed calculation method is demonstrated to be rational and reliable. The analysis of load distribution patterns and the proposed predictive method can provide a reference for the blast-resistant analysis, calculation, and assessment of shaft structures in layered media.

To address the bottlenecks of low efficiency and limited coverage in conventional scale-removal methods, this study introduces an ultrasound-enhanced waterjet technique. Its descaling performance is systematically benchmarked against that of a pure waterjet, while the individual and combined effects of ultrasonic frequency, jet pressure, and stand-off distance on descaling efficiency are quantitatively elucidated. Results show that the ultrasound-enhanced jet markedly amplifies descaling efficacy, raising the breakage efficiency by 69.14%. An optimal ultrasonic frequency exists for maximizing both penetration depth and affected area; higher jet pressure monotonically improves descaling performance; and the stand-off distance exhibits a nonlinear influence, with both depth and area peaking at an intermediate value. The best descaling outcome is achieved at an ultrasonic frequency of 20 kHz coupled with a jet pressure of 20 MPa. The findings provide critical parameter guidance and a technical roadmap for developing high-efficiency, low-energy scale-removal processes.

Structural health monitoring (SHM) systems generate massive amounts of monitoring data during long-term operation. To achieve rapid prediction of the safety status of wharf structures, it is essential to establish efficient data prediction models. To address issues such as low training efficiency, insufficient prediction accuracy, weak generalization ability, and susceptibility to local optima in existing SHM data prediction models, this paper proposes a monitoring data prediction and analysis model that integrates Transformer networks, long short-term memory (LSTM) networks, and a multi-strategy improved whale optimization algorithm (MSWOA). This model, referred to as the MSWOA-Transformer-LSTM (MTL) model, was applied to analyze and predict multi-source data, including structural strain, protective potential, and temperature, collected from the health monitoring system of a high-pile wharf in Tianjin Port. The results show that the introduction of the MSWOA method significantly enhances the optimization accuracy and convergence speed of the MTL model. Furthermore, compared with prediction results from neural network models such as Transformer-GRU (gated recurrent unit), Transformer-LSTM, and WOA-Transformer-LSTM (WTL), the MTL model demonstrates higher accuracy and better stability in prediction tasks.

The inerter-based damper (IBD) is an effective device for mitigating stay cable vibrations, and various design methods based on it have been explored. However, the performance of IBD is highly dependent on the precise tuning of its parameters. Most existing optimization methods fail to consider practical parameter uncertainties, leading to performance degradation in real-world conditions. To overcome this limitation, this paper develops a novel interval model-based optimization approach that explicitly incorporates multi-source uncertainties. The goal is to minimize the upper bounds of the cable’s dynamic response, thereby ensuring robust control performance despite parameter variations. Monte Carlo simulations confirm that the proposed method significantly enhances the system's robustness by reducing its sensitivity to parameter changes. This study provides solid theoretical support for the robust design and practical application of IBD in engineering.

With the continuous extension of super-long cable-stayed bridges and the escalating needs for both traffic and navigation, the installation heights of external dampers are designed to be progressively higher. Conventional vibration mitigation schemes suffer from low efficiency and the inapplicability of the rigid support assumption. This paper develops a ternary passive vibration control device that incorporates inerter, negative stiffness, and eddy current damping elements—the Inerter-Negative Stiffness-Eddy Current Damper (INSECD). Experimental research on its mechanical performance and vibration reduction effect analysis under elastic support stiffness were conducted, and a design method for INSECD considering support stiffness was proposed. In this paper, a full-scale INSECD prototype for real bridge stay cables was developed first, and a series of mechanical performance tests were carried out to validate the design method rooted in multi-field 3D finite element simulation analysis. Then, a comprehensive mechanical model of INSECD incorporating support stiffness was established to analyze the influence of support stiffness on the mechanical characteristics of INSECD and the equivalent damper installation height. Finally, the equations of motion for the stay cable-INSECD system considering support stiffness were formulated, and simulation of vibration reduction under various working conditions with elastic support were performed, leading to the proposal of an INSECD vibration mitigation design method that accounts for elastic support. The results show that the experimentally measured damping characteristics of INSECD align well with theoretical predictions. The support stiffness of the damper leads to a shift in the damper’s equivalent parameters and a degradation of vibration reduction performance. Taking the first-order optimal damping design under the actual support conditions of the side-span cable of an existing bridge as an example, the maximum additional damping ratio decreases by 12.8%–17.9%. At an actual installation position of 3.62%, it provides an additional damping ratio of 3.97%, which is 2.45 times that of an equivalent conventional passive optimal vibration control solution. At the actual support stiffness and installation position (3.62%), the INSECD vibration mitigation solution provides an additional damping ratio of 3.97% for the 1-s9 stay cable, which is 2.45 times that of the optimal passive vibration mitigation solution under equivalent conditions. This study proposes an efficient and universal INSECD vibration mitigation design method for stay cables under elastic support conditions, supplementing and refining the existing theoretical system for stay cable vibration control.

The service environment of pile foundations in high-pile wharves is complex, and traditional dynamic-response-based health monitoring faces challenges such as high sensor costs and deployment difficulties. Digital Image Correlation (DIC) is a non-contact measurement technique suitable for harsh environments and capable of nondestructive full-field measurement. However, the dynamic response data obtained via DIC are dense and quasi-continuous field data, which present difficulties in feature extraction due to their large volume. Currently, there is a lack of research on feature extraction theories and pile foundation damage identification methods based on DIC continuous field data. To address this gap, this study draws on the theoretical framework of in-plane free vibration correlation to clarify the theoretical significance of residual analysis in fitting continuous field data.  It further derives The theoretical basis for constructing a damage identification index was derived using displacement fitting residuals and develops a residual spectral distance metric was developed. Accordingly, a damage identification method for wharf pile foundations based on DIC field measurement data is was proposed. The effectiveness of the proposed method is was validated through physical model experiments. This work establishes a theoretical foundation and provides a practical methodology for damage identification using residual analysis of continuous field data, offering a new approach for damage detection in pier pile foundations based on full-field measurements.

In view of the challenges in the field of bearing remaining useful life(RUL) prediction, an interpretable cross-condition transfer (ICCT) method was proposed. Multiple factors were combined to calculate similarity to select suitable datasets, adaptive screening of training datasets was realized, and thetransfer efficiency was improved. ICCT was combined with multi-channel dilated convolution and long short-term memory （LSTM） network to extract time-frequency features, and high-dimensional features with dimensionality reduction were visualized and analyzed, thus mutual feedback adaptive optimization of balance coefficients and feature differences in transfer learning was realized. Experimental verification was carried out on the IEEE PHM 2012 dataset and XJTU-SY dataset. The results show that ICCT can achieve better prediction results with a small number of datasets, and the ablation experiment shows that the average prediction accuracy of the ICCT method is about 20% higher than that of the traditional transfer learning method, which provides guidance for the optimization direction of the model in transfer learning.

To address the issues of low detection accuracy and poor robustness for various gear surface defects, as well as high rates of missed and false detections of micro-cracks and broken teeth under complex backgrounds, this paper proposes a gear defect detection algorithm based on dynamic multi-scale fusion and structure-context guidance. First, a dynamic inception mixer block (DIMB) was designed, using a multi-branch convolutional structure and a dynamic weight fusion mechanism to adaptively capture defect features of different shapes and orientations, such as broken gear teeth and cracks. Next, a polar-coordinate guided (PCG) module based on dilated convolutions was designed to explicitly model the annular structure of gears, describing long-range periodic context relationships and effectively suppressing interference from inherent structural features like normal tooth gaps during downsampling. By employing the novel DIMB as the core module of the backbone and using the PCG module for encoding downsampling, an efficient and robust feature extraction framework was constructed, allowing more precise representation of crack defects. Experiments conducted on a public dataset show that the proposed algorithm achieves an accuracy, recall, and mAP of (99.14±0.21)%, (99.22±0.13)%, and (99.25±0.32)%, respectively, with a detection speed of 73 FPS. In terms of both accuracy and efficiency, it outperforms the original Detection Transformer（DETR） series and several advanced detection models, significantly improving the ability to accurately identify micro-cracks in gears and the reliability of defect recognition under complex backgrounds.

To address the issue of insufficient output voltage from the self-powering unit of intelligent bearings under low-amplitude operating conditions, a rotational magnetic force-driven hybrid energy harvester for intelligent bearings iwas proposed based on magneto-electric-piezoelectric-triboelectric multi-physics coupling and synergy. This design effectively enhances Voltage output was effectively enhanced in low-amplitude vibration environments. The intelligent bearing adopts An extended structure was adopted by the intelligent bearing, consisting of an outer ring sleeve, an inner ring expansion ring, magnetic poles, and a hybrid energy harvester. The outer ring sleeve is was nested onto the bearing’s outer race and secures the hybrid energy harvester was secured, while the inner ring expansion ring is was fixed to the bearing’s inner race and houses the magnetic poles were housed. The hybrid energy harvester employs A simply supported single-crystal piezoelectric beam structure was employed, integrated with a contact-separation triboelectric nanogenerator (TENG) unit along the vibration direction, achieving synergistic power generation between the piezoelectric and triboelectric units. Furthermore, a dynamic model of the energy harvester was established, and the influence of different structural dimensions and magnetic pole configurations on the output voltage was analyzed using COMSOL software. Experimental validation confirmed its effectiveness. The results demonstrate show that the energy harvester operates efficiently under varying rotational speeds of the bearing. Within a speed range of 0–1200 r/min, the piezoelectric module stably outputs a peak voltage of approximately 16.6 V, while the triboelectric unit delivers a stable peak voltage of about 4.4 V, effectively meeting the self-powering requirements of the intelligent bearing.

Bearings, as critical rotating components in mechanical systems, faced engineering challenges due to limited real fault data and suboptimal diagnostic performance. A deep reinforcement learning approach with curriculum learning was developed for rolling bearing fault data generation and diagnosis.Firstly, a signal modeling–based supervision mechanism  employing Twin Delayed Deep Deterministic Policy Gradient (TD3) ) was built,generated generating simulation fault signals closely matching real data, and mitigating sample scarcity. Secondly, a curriculum learning strategy based on sample complexity was introduced to improve the policy optimization accuracy.A sample complexity–based curriculum learning strategy enhanced policy optimization Finally,, while a Deep Double Dueling Q-Network (D3QN)  combinedwithwith long short-term memory (LSTM) and, attention pooling, integratingand class-aware prioritized experience replay,was built to enabled cross-domain transfer from synthetic to real data. Experiments on public datasets show that the proposed method outperforms advanced approaches in diagnostic accuracy and demonstrates strong noise robustness and generalization.

Aiming at the multi-stage and non-linear degradation characteristics inherent in the degradation process of rolling bearings, a degradation indicator construction method based on multiscale one-dimensional deformable convolutional network (1D-MSDCN ),and a multi-stage remaining useful life (RUL) prediction method integrating an time series segmentation (TSS) algorithm with a BiLSTM network wereere proposed. First, 1D-MSDCN with multiple one-dimensional deformable convolutional kernels was utilized to fuse multiple time-domain features of vibration signals into a one-dimensional bearing degradation index. Second, based on the time series segmentation algorithm, a comprehensive evaluation metric was introduced specifically for assessing the results of degradation stage division, thereby obtaining the optimal segmentation of bearing degradation stages. Finally, a stage-based RUL prediction method using BiLSTM was employed, which adding a module for phased network parameter updating to enhance the prediction accuracy at the final failure moment of bearings. The experimental results on XJTU-SY dataset shows that the proposed method has outstanding performance in the rationality of degradation stage division and the accuracy of RUL prediction, achieving a root mean square error of 5.07%, a mean absolute error of 3.96%, and a coefficient of determination of 0.88 in the RUL prediction of rolling bearings.

To address the inaccurate identification of the degradation onset point (DOP) and the low prediction accuracy of remaining useful life (RUL) under complex operating conditions, this paper proposes a rolling bearing RUL prediction method based on unsupervised anomaly detection and transfer learning. Two main components were included: The proposed framework consists of two stages: DOP detection and RUL prediction. In the DOP detection stage, an autoencoder (AE) iwas employed to extract features from raw vibration signals, and a health indicator (HI) is was constructed using a one-class support vector machine (one-class SVM). A Streaming Peaks-Over-Threshold with Drift (DSPOT) algorithm is was then adopted to determine adaptive thresholds, enabling reliable detection of abnormal degradation and accurate identification of the DOP. In the RUL prediction stage, a model-based transfer learning strategy is was applied. An auxiliary dataset is was introduced to fine-tune a Bidirectional Long Short-Term Memory (BiLSTM) network using a small portion of target-domain data, thereby improving cross-condition prediction performance. Experimental results on the IEEE PHM 2012 Challenge dataset demonstrate show that the proposed method achieves significant performance improvements, outperforming six existing RUL prediction methods by 65.8%, 56.2%, 51.0%, 13.8%, 32.1%, and 50.1%, respectively.

To address the scarcity or absence of composite fault samples in aero-engine rolling bearings under complex operating conditions, this paper proposes a zero-shot learning fault diagnosis method based on counterfactual generation. From the perspective of causal inference, a counterfactual generation framework iwas designed using dual generative adversarial networks. Specifically, by disentangling non-causal features from fault characteristics and constructing fault semantic vectors via a residual convolutional denoising autoencoder, counterfactual fault samples are were generated to enable fault diagnosis. This approach mitigates The influence of random noise on sample authenticity was mitigated and more accurately simulates the fault features in complex environments were simulated more accurately. Moreover, a dual-classifier structure is was introduced into the dual generative adversarial networks, which classifies classifying both the generated fault features and semantic vectors under orthogonal constraints, significantly enhancing the discriminability of generated samples and the effectiveness of feature disentanglement. This improves the diversity and authenticity of composite fault features. Experimental results demonstrate show that the proposed model achieves an accuracy of 85.33% in composite fault recognition.

Rolling bearing fault diagnosis is a crucial aspect of industrial equipment health management, and deep learning-based transfer learning methods have made significant progress in this field. However, most existing research focuses on knowledge transfer from a single source domain. Real-world industrial scenarios often involve multi-source data from multiple devices, different operating conditions, or different sensors, and the fault categories in the target domain are often unpredictable, potentially including unknown fault types not occurred beforepresent in the source domain. This poses a challenge to traditional transfer learning methods. To address this, this paper proposes a progressive transfer learning diagnostic method applicable to multiple source domains and capable of identifying unknown faults, aiming to improve the accuracy and robustness of fault diagnosis under complex operating conditions. This method First， introduces a convolutional block attention mechanism was introduced to enhance the model's ability to extract key fault features. Then, a two-stage progressive training framework is was designed, including an isolation stage and an alignment stage. In the isolation stage, known and unknown fault samples in the target domain were distinguished by adaptively learning decision boundary, and optimized by combining source domain classification loss and open set identification loss, effectively mitigating the interference problems caused by multiple source domain differences and unknown categories. The isolation stage adaptively learns the decision boundary to distinguish between known and unknown fault samples in the target domain, and optimizes this by combining source domain classification loss and open set recognition loss, effectively mitigating the interference problems caused by differences between multiple source domains and unknown categories. In the alignment stage, sample weighting strategy and adversarial training were used to promote the alignment of multiple source domains and target domains in the feature space, enhance the compactness and discriminative power of intra-class features, and thereby extract domain-invariant features with greater generalization ability. The alignment phase employs a sample weighting strategy and adversarial training to align multiple source and target domains in the feature space, enhancing the compactness and discriminative power of intra-class features, thereby extracting domain-invariant features with greater generalization ability. This method was systematically validated on a multi-condition gearbox fault dataset. Experimental results show that, in open-set transfer scenarios, the proposed method improves diagnostic accuracy by 6.2% to –14.4% compared to existing mainstream methods. Ablation experiments further confirm that the attention mechanism and adaptive weighted adversarial strategy play a crucial role in improving the model's cross-domain generalization ability. This research provides an effective and practical solution for intelligent fault diagnosis in complex industrial environments. 

Earthquakes and their induced landslides represent a major disaster threatening the safe operation of ultra-high voltage (UHV) transmission lines in mountainous areas. Currently, the dynamic response and damage mechanisms of UHV transmission lines under seismic and landslide conditions remain unclear, particularly due to a lack of relevant experimental studies. Using a self-designed tower-leg deformation device to simulate earthquake-induced landslides, shaking table tests were conducted on slope-situated UHV transmission tower-line systems considering the coupled effects of earthquakes and landslides. The response and damage characteristics of the slope tower-line system were systematically investigated under three conditions: earthquake alone, landslide alone, and coupled earthquake-landslide. Test results show that the structure remains essentially within the elastic stage under individual hazard actions, whereas the coupled earthquake-landslide leads to a significant degradation of structural frequency and induces severe nonlinear damage. The first and second horizontal bracing levels and the tower base region are identified as structural weak zones. The coupled effect simultaneously aggravates global damage and local member deformation in the lower cross-arm. Under coupled earthquake-landslide, structural strain responses are markedly amplified: main and diagonal members are predominantly governed by seismic action, while tower legs and bracing members are controlled by landslide displacement, accompanied by localized stress redistribution.

This study investigates the impact of seismic source uncertainty on post-earthquake functional losses of urban road networks. Ground motion simulation was integrated into the traditional post-earthquake functional loss assessment framework. Seismic inputs were generated by simulation methods and then linked with fragility functions, combined with network capacity analysis considering post-earthquake link importance. An evaluation method accounting for seismic source uncertainty was developed and applied to the road network of Handan City. For magnitudes M6.0, M6.5, M7.0, and M7.5, 30 rupture models for each magnitude were generated, and network functionality was calculated for all models. Results indicate show that the hypocentral distance of Handan's system ranges from 26–42 km. Seismic source uncertainty significantly affects both the total functional loss and its spatial distribution, resulting in discrete functionality loss characteristics across links. While spatial trends are consistent, functional levels exhibit notable randomness among scenarios. This randomness increases with the increase oflarger magnitudes but and decreases with the increase ofgreater hypocentral distance. The findings provide references for seismic risk assessment and research of on road network resilience.

Carbon nanotubes (CNTs) are regarded as a remarkably promising reinforcement material for concrete, as they not only fill pores at the nanoscale to increase compactness but also act as micro-fibers that effectively inhibit the initiation and propagation of micro-cracks.Based on the orthogonal experimental design, this study prepares CNTs concrete composites with different dosages by volume to optimize their mechanical properties. Firstly,by employing a controlled CNTs dosage of 0.1% , CNTs concrete with a compressive strength of 45.45 MPa and a flexural strength of 6.17 MPa was produced.This optimized CNTs concrete was then used to locally strengthen the potential plastic hinge zone at the lower section of a bridge pier.Subsequently,specimens were fabricated,and comparative pseudo-static tests under compression-flexure-torsion combined action were conducted to evaluate the seismic performance in comparison with ordinary concrete bridge piers.The results show that the locally reinforced pier performs better in terms of crack control, ductility, hysteretic performance and energy dissipation, in which the displacement ductility coefficient is improved by about 10.2% and the equivalent damping coefficient by about 6.8%.These findings can provide valuable references for applying novel materials in enhancing bridge resilience.