To address issues such as the large number of parameters, high computational complexity, and inadequate real-time performance in existing CNN-Transformer hybrid fault diagnosis models, we propose a lightweight fault diagnosis framework, LWConvFormer. This framework comprises two core innovative modules: a dynamic separable multi-scale convolutional module that employs a gated network for adaptive feature extraction, thereby reducing computational load while enhancing adaptability to complex fault modes; and a broadcast self-attention module that substitutes traditional matrix multiplication with broadcast operations, thereby decreasing computational complexity from a quadratic to a linear level. Experimental results based on the planetary gearbox at Xi'an Jiaotong University and the QPZZ-II type rotating machinery test bench demonstrate that LWConvFormer maintains excellent diagnostic performance across various noise levels. The number of parameters and computational load are reduced by a factor of 6 to 10 compared to mainstream methods, while the training speed increases by nearly 7 times. This framework effectively balances diagnostic accuracy, model lightweighting, and noise resistance, offering an efficient solution for real-time fault diagnosis in industrial settings.
The primary mechanism causing energy topological distortion in the target area is the high-order synergy between boiling phase transition and inertial cavitation caused by high-power short-pulse (HPSP) focused ultrasound. A new multi-physics model that links bubble swarms with the thermo-acoustic phase transition at all scales is presented in this research. The pure enthalpy approach is used to objectively quantify a temperature clamping effect (locked at 100±1 ℃) of the latent heat of vaporization (≈ 2.37×10 9 J/m 3 ). The microcompressible Keller-Miksis equation is updated by coupling the elastic deviatoric stress of the fully incompressible solid phase ( υ = 0.5), indicating that the bubble wall collapse Mach number can reach an anomalous breakthrough of 1.2 due to the release of tissue shear elastic energy. The spatiotemporal genesis of the clinical “tadpole-shaped” lesion distortion brought on by bubble cloud shielding is revealed by two-dimensional acoustic field reconstruction. This leads to the proposal of a closed-loop control method with a 20 % dynamic duty cycle. This approach accurately anchors the focal region to the analytical solution (78.4 °C) using thermal relaxation, as confirmed by the construction of a highly sensitive phase transition surrogate model with transient acoustic pressure gating operators. Additionally, it accurately reduces about 99.4 % of unsuccessful mechanical over-kill by displaying the distinct tiny step-like physical fusing features of low-frequency pulses. Intended primarily for medical therapeutic applications, this work establishes a foundation in fluid dynamics and multiphysics mathematical modeling for a novel class of clinical conformal ablation methods by bridging the gap between microscopic dynamics and macroscopic thermal response.
Based on the structural characteristics of asymmetric vortex compressors, a structure of asymmetric variable diameter base involute vortex comprwessors is proposed, and performance analysis is conducted for vortex compressors used in portable oxygen production systems. According to the normal equidistant method, the volume calculation formula for a variable diameter base circle involute vortex disc with both inner and outer profile angles was derived. An asymmetric variable diameter base circle involute vortex disc model with equal content product ratio was established, and its performance was compared with that of an asymmetric circular involute vortex disc under certain conditions. The results showed that, while ensuring the same compression ratio, the structural size of the model was reduced by 7.58 %, the area utilization rate was reduced by 7.47 %, and the material was more economical. Meanwhile, the gas force was reduced, and the mechanical properties were better. Under the condition of ensuring the same size, the suction volume of the model increased by 8.98 % and the compression ratio increased by 16.3 %, indicating better working performance. This study could provide theoretical reference for the design of vortex compressors in specific scenarios.
This study evaluates the applicability of traditional vibration input methods in tunnel seismic analysis to address limited calculation accuracy and ambiguous applicability. The vibration input method, wave input method, and theoretical solutions are compared to clarify the error mechanism and main influencing factors. Through correlation analyses, the relationships between model dimensions, geological parameters, and accuracy are established. Then an innovative approach is proposed to improve the calculation accuracy and refine the scope of applicability. The results indicate that the assumed bedrock surface position and model boundaries can significantly affect the accuracy of the model, with the height and wave velocity of the surrounding rock being key control parameters. When the model height is fixed, the correlation coefficient between vibration and wave input results shows three stages of change with increasing width: rapid increase, deceleration growth, and asymptotic convergence to 1. In the case of a fixed width, as the height increases, the coefficient follows a trend of “approximately stable slightly decreasing”. The optimal aspect ratio is 1:2. Size optimization formulas under different rock conditions and methods for selecting analysis time periods are proposed to clarify the engineering applicability of the vibration input method.
Traditional main shaft rolling bearings often experience localized stress concentration, transient overheating, and fatigue spalling under low-speed, heavy-load, and variable-speed operating conditions, making it difficult to meet long-life service requirements. To address this issue, this paper proposes a novel hybrid rolling-sliding bearing structure with a parallel design of rolling and sliding bearings, and investigates its operational performance through simulation. Via parameter optimization, the length-to-diameter ratio of the sliding bearing is determined as 1.4, and the initial radial clearance as 400 μm. The results show that under two typical operating conditions of 8 rpm and 10 rpm, the sliding bearing system can achieve a static equilibrium state. Based on the Reynolds equation and its static equilibrium conditions, the eccentricity ratio and attitude angle of the sliding bearing at the static equilibrium position are determined. These parameters are then applied as the eccentric parameters of the bearing bush in the design of the hybrid bearing structure. Consequently, once the hybrid bearing transitions from the start-stop phase to stable operation, the sliding bearing can generate hydrodynamic effects and effectively share the radial load, thereby significantly reducing the radial load borne by the rolling bearing. By adopting a parallel load-carrying structure of rolling and sliding bearings, the modified rating life of the rolling elements within the hybrid bearing under load-sharing conditions reaches approximately 43.52 years at 8 rpm and 35.43 years at 10 rpm, corresponding to improvement factors of 4.9 and 5.0, respectively, compared to the pure rolling bearing, which exhibits 8.89 years at 8 rpm and 7.11 years at 10 rpm. Furthermore, over the operating speed range of 3 to 16 rpm, the average Modified rating life of the rolling elements within the hybrid bearing under load-sharing conditions is approximately 23.58 years for the design based on 8 rpm eccentricity parameters and approximately 23.79 years for the design based on 10 rpm eccentricity parameters, compared to 9.55 years for the pure rolling bearing. The service life of the system is markedly improved, meeting the long-life operation requirements of wind turbines.
This study develops a comprehensive Structural Health Monitoring (SHM) strategy applied to the Spyckstraße Bridge, a three-span composite slab-girder structure constructed in 1976, with the objective of evaluating its structural performance under current design standards and increased traffic loads. A short-term monitoring campaign based on non-destructive techniques was carried out to record dynamic responses, including vibrations, displacements, and modal properties. The collected data served to calibrate and validate a Digital Twin model through an iterative optimization process using a genetic algorithm, achieving a deviation below 3 % between experimental and numerical eigenfrequencies. Once validated, the Digital Twin was used to assess the structural response under code-compliant load combinations and to determine utilisation levels for all critical sections. The analysis showed that none of the structural components reached their ultimate limit state, although the maximum tensile utilisation (97.3 %) was identified in the external girder of the central span under combined extreme traffic and thermal loading conditions. Furthermore, a parametric study addressing tendon corrosion effects indicated notable variations in modal characteristics and a decrease in load-bearing capacity. These findings highlight the effectiveness of integrating short-term SHM data with calibrated numerical models as a reliable framework for structural assessment, enabling early damage detection, informed maintenance strategies, and enhanced lifecycle management of existing bridge assets.
Mechanical devices with eccentric shaft synchronization are extensively employed in engineering applications, such as exploration, pile driving, rock breaking, and material selection. In this work, a dynamic model of two eccentric shafts was established to study their synchronization characteristics in a parallel hydraulic motor system. The flow characteristics of two parallel hydraulic motors were considered for nonidentical parameters of the shafts. Our simulation results show that two eccentric shafts can sequentially reach a steady state despite their different parameters. However, their rotational velocities are unequal in the steady state. Further, the viscous damping coefficient more severely influences the synchronization characteristics of the shafts than mass and eccentricity. Finally, we show that the numerical and simulation results are supported by the experimental findings.
To reveal the influence patterns of various internal and external excitations, such as errors, clearances, stiffness, rotational speeds, and loads, on the gear transmission system of the height adjuster, and to optimize the vibration and impact generated by these excitations, theoretical modeling, numerical analysis, and simulation verification methods were comprehensively utilized to calculate and analyze the periodic motion characteristics and response amplitudes of the seat height adjuster. The results show that, to prevent the system from entering a chaotic state, the input gear speed should avoid the speed ranges of 1349 r/min-1359 r/min and 1589 r/min-2060 r/min. This work provides a theoretical basis for the selection of operating conditions for the gear transmission system of the height adjuster, and also offers valuable references and insights for engineers and technicians in related fields.
MEMS micro-resonators often operate in a multi-physics coupling environment involving electrostatic fields, molecular force fields, and structural force fields, exhibiting significant nonlinear natural frequency perturbation and frequency drift, which leads to large fluctuations in output frequency. Existing methods struggle to simultaneously characterize the nonlinear coupling effects of Casimir force and electrostatic force, and lack adaptive compensation capability for time-varying disturbances. To overcome this bottleneck, this paper proposes a frequency stabilization control method based on nonlinear coupled dynamics and adaptive compensation using a fuzzy emotional neural network. The main contributions are as follows: a distributed-parameter nonlinear vibration model incorporating dynamic electrostatic force and Casimir force is established, revealing the intrinsic competitive mechanism through which initial gap and beam length affect frequency drift; a fuzzy emotional neural network with dynamic displacement, dynamic electrostatic force, and dynamic Casimir force as multi-field fusion inputs is designed to achieve high-precision real-time estimation of the system's unmodeled dynamics; an error-constrained barrier Lyapunov function is constructed and combined with backstepping control to realize adaptive feedforward-feedback composite compensation of the excitation voltage between the plates. Experimental results show that the proposed method achieves high-precision frequency stabilization control under different micro-resonator lengths (500 μm, 1000 μm, and 1500 μm), with the root mean square error of frequency tracking as low as 0.46×10 4 Hz, which is reduced by approximately one order of magnitude compared with the PID method. The maximum Allan variance is only 4.5×10 -10 , reduced by approximately two orders of magnitude compared with the open-loop state, meeting the accuracy requirements of industrial-grade frequency sources such as 5G base station local oscillators and inertial navigation system clocks. The proposed method does not rely on modifying the device geometry and is insensitive to changes in structural dimensions, providing a feasible and robust intelligent control solution for the engineering application of high-precision MEMS resonators.
The face teeth of wheel hub bearing are manufactured using a two-step rotary riveting forming process. Controlling the forming force and improving the tooth profile fullness are critical, as these factors directly affect bearing performance. This study employs DEFORM finite element simulation combined with experimental verification to analyze the influence of structural and process parameters-including blank dimensions, pre-riveting state, feed rate, spindle rotation speed, and riveting inclination angle-on the forming force and tooth profile fullness. Optimal structural and process parameters are thereby determined. The results indicate that among blank structural parameters, wall thickness has the greatest impact on tooth profile fullness, followed by inner corner radius, with outer corner radius having the least effect. Optimized blank dimensions improve profile fullness and reduce forming force. The pre-riveting state significantly influences the tooth forming process, a fully riveted state reduces axial forming force by 24.9 % compared to a non-riveted state and markedly improves profile fullness. A smaller feed rate and higher spindle speed reduce forming force and improve profile fullness, but may cause flash at the tooth outer edge. Conversely, excessive feed rate and low spindle speed reduce profile fullness. Experimental verification shows that optimized process parameters increase tooth profile fullness by 4.6 % to 96.2 % and reduce forming force by 17.8 % compared to pre-optimization conditions, the effectiveness of the
With the increasing depth and intensity of coal mining in China, non-natural seismic events occur more frequently, posing higher demands on mine safety and seismic monitoring. a core parameter in earthquake monitoring and hazard assessment, precise determination magnitude is essential for predicting and mitigating dynamic disasters. To address this, conducted 22 controlled blasting experiments in the Weihai Port area of Shandong Province, using a combination of fixed and mobile seismic stations to systematically investigate the attenuation characteristics of near-field seismic waves and to establish a regional calibration function for magnitude (ML). The calibration functions for both horizontal and vertical components derived through least-squares fitting, from which the corresponding local magnitude determination formula was developed. Results from the 22 blasting events indicate good consistency of single-station magnitudes, with small deviations compared to the magnitudes determined by the Shandong Seismic Network, satisfying the required accuracy for magnitude estimation. Overall, this study establishes a calibration function applicable within 5 km of Weihai blasting area, enhancing the consistency between magnitudes of blasting and natural earthquakes. The results provide a valuable reference for improving regional seismic monitoring systems and strengthening early warning capabilities for mine-related hazards.
Compressors serve as fundamental power units in underground natural gas storage facilities and frequently need to accommodate wide-range operational condition adjustments. However, their inherent nonlinear characteristics, coupled with high-noise and uncertain operating environments, pose significant challenges to precise control and safe operation. To accurately predict compressor operational status and provide reliable decision-making information for operation and maintenance, this study proposes an operating-state prediction method for gas storage compressors based on vibration-sequence analysis and a Bidirectional Long Short-Term Memory (BiLSTM) network. Singular Spectrum Analysis (SSA) is first applied to decompose and reconstruct monitoring signals, extracting dominant trend components while suppressing background noise. A Convolutional Neural Network (CNN) is subsequently employed to capture multi-scale local features, after which the BiLSTM network learns temporal evolution patterns from the extracted features. The Adam optimizer is utilized to enhance training stability and prediction accuracy. Experimental validation using real monitoring data from gas storage compressors indicates that the proposed method achieves precise and reliable prediction of compressor operating states.
Domain shift caused by variations in rotational speed and load under cross-condition scenarios may distort the statistical distribution and feature representation of vibration signals, posing a challenge to the reliable deployment of intelligent fault diagnosis systems for rotating machinery. Existing multi-scale CNN-Transformer hybrid architectures generally do not explicitly consider the influence of such condition-induced domain shifts. To address this issue, a multi-scale CNN-Transformer hybrid method integrating a Parallel Attention Mechanism (PAM) and a Local Linear Unit (LLU) is proposed to enhance the stability of feature representations under varying operating conditions. In the proposed method, raw vibration signals are directly used as inputs, and a multi-scale CNN module is employed to capture transient impact features across multiple temporal scales. The PAM then adjusts feature responses in both channel and temporal dimensions through parallel attention branches, enabling adaptive feature reweighting to alleviate condition-induced statistical variations. Furthermore, a Transformer encoder embedded with LLU is adopted as the backbone to incorporate local structural modeling through depthwise separable convolution while preserving the global dependency modeling capability of self-attention. Experiments on three benchmark datasets (PU, PHM09, and CWRU) show that the proposed method achieves average accuracies of 80.15 %, 93.96 %, and 98.17 %, respectively, under unseen operating conditions, consistently outperforming several representative comparative methods. Additional ablation studies further verify the contribution of PAM and LLU to robust feature representation learning. Moreover, noise robustness experiments under multiple signal-to-noise ratio (SNR) levels demonstrate that the proposed method maintains stable performance under moderate noise conditions, highlighting its practical reliability in realistic industrial environments. Our code is available at https://github.com/caie8201/Multi-Scale-CNN-Transformer-Hybrid-Network.
To address the challenges of fault feature extraction and the weak adaptability of diagnostic models for automotive gearbox bearings under complex operating conditions, this study proposes an improved intelligent diagnostic model that integrates Convolutional Neural Networks (CNN) and Transformers with a dual-stage dynamic sparse activation and three-dimensional attention mechanism. First, to overcome the limitations of traditional CNN with fixed architectures and limited perception of multi-domain fault features, a dual-stage dynamic sparse activation mechanism is designed. It enables adaptive computation path selection based on the complexity of input features. Then, to enhance the perception of multidimensional time-frequency-phase fault information, the Hilbert transform is applied to construct a three-dimensional feature tensor containing instantaneous amplitude, frequency, and phase. A 3D self-attention module is embedded to achieve multi-domain feature fusion. Finally, the proposed method is validated using experimental data collected under various gearbox bearing fault states and operating conditions. The results show that the model achieves an accuracy of 99.73 %, with precision, recall, and F1-score of 99.64 %, 99.63 %, and 99.68 %, respectively-all outperforming state-of-the-art methods such as GDS-YOLOv5s. Moreover, the model maintains stable recognition performance under noise and variable load conditions. These findings demonstrate that the proposed approach effectively captures subtle multi-domain fault features and exhibits strong adaptability and robustness, providing a reliable solution for intelligent operation and maintenance of gearbox bearings.
. In order to attain time-lag dynamic compensation control and decrease time-lag influence of high-clearance sprayer semi-active suspensions, a novel suspension intelligent controller based on improved Smith prediction neuroendocrine algorithm is proposed. Firstly, the time-lag semi-active suspension dynamic model of high-clearance sprayers is established, the neuroendocrine intelligent controller is designed combined with the hormone regulation mechanism in the organism. Then, by combining enhanced Smith prediction compensation controller with neuroendocrine intelligent controller, a kind of high-clearance sprayer semi-active suspension intelligent controller based on the improved Smith prediction neuroendocrine algorithm is developed. The research results show the proposed algorithm can significantly reduce body vertical acceleration (BVA) and tire dynamic load (TDL) indicators of high-clearance sprayer semi-active suspensions, effectively improve smoothness and road friendliness of sprayers, significantly enhance comprehensive performance, show strong adaptability and robustness under working conditions, and is very suitable for vibration control of the high-clearance sprayer semi-active suspension with high order time-varying, complex nonlinear and strong coupling.
Owing to its unique structure, the driving motor of a ship's rim propulsion device is subject to coupling effects from multiple physical fields. This makes it difficult for conventional control methods to effectively suppress vibrations caused by high-amplitude, complex harmonics, leading to poor speed and torque control performance. Therefore, a vibration suppression method based on disturbance observer combined with non singular sliding mode control is proposed. First, a disturbance observer is constructed to monitor motor torque in real time and accurately capture torque fluctuations induced by vibration. Secondly, design a non singular sliding mode controller to adaptively and quickly adjust the motor speed when vibration is detected. Finally, the quantum particle swarm algorithm, enhanced by the artificial bee colony algorithm, is used to optimize the controller parameters, thereby improving robustness and accuracy under multi-physics field coupling. The experimental results show that this method can accurately observe the motor torque and quickly stabilize the speed between 500 r/min-800 r/min under vibration state, with the smallest torque fluctuation amplitude. This result holds important scientific significance: it validates the effectiveness of nonsingular sliding mode control combined with intelligent optimization algorithms in decoupling multi-physics field interactions and suppressing complex electromagnetic excitation vibrations, offering a new control perspective for understanding motor dynamics under extreme operating conditions. In terms of application value, this method significantly enhances the dynamic response speed and steady-state accuracy of the driving motor, directly improving propulsion efficiency and maneuverability. It also effectively reduces fatigue wear on mechanical components, extends equipment life, and lowers operation and maintenance costs throughout the ship's life cycle. In the future, we will explore integrating this control strategy with energy efficiency optimization for propulsion devices and investigate predictive vibration suppression methods based on digital twins to achieve smarter, more efficient health management of ship propulsion systems.
In response to the lack of clear calculation methods for the normal section bearing capacity of circular piers strengthened by concrete jacketing in current design codes, this paper derives a calculation formula applicable to the normal section bearing capacity of strengthened circular piers under unloading conditions. The derivation is based on the relevant provisions of the Specifications for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts, incorporating the plane-section assumption and ultimate state theory. Finite element verification shows that the theoretical values agree well with the simulation results, with a maximum error of-1.55 %, and the results are conservative, meeting engineering safety requirements. In terms of impact resistance, increasing the thickness of the strengthening layer significantly enhances the pier's stiffness, reduces the displacement peak, and shortens the dynamic response time, shifting the damage mode from global damage to locally controllable damage. Parameter analysis indicates that the diameter of the main reinforcement and the spacing of the stirrups have a limited effect on the displacement response but play a key role in energy dissipation capacity: increasing the main reinforcement diameter effectively improves the total energy dissipation, while reducing the stirrup spacing enhances the confinement effect on the core concrete and improves energy dissipation efficiency. Considering both economy and construction feasibility, it is recommended to prioritize larger diameter main reinforcement and control the stirrup spacing within the range of 10-15 cm in impact-resistant design to achieve an optimal balance between performance and cost. Combined with a bridge strengthening project in Changchun, this paper summarizes key technical points, forming a theoretically complete and practically verified technical system for pier strengthening.
Shaft vibration (displacement signal) and bearing vibration (velocity signal) are key indicators for evaluating the dynamic characteristics of the rotor and supporting bearing system, and they play a crucial role in the operational performance and safety of equipment. However, in practical applications, collecting shaft vibration or bearing vibration signals often encounters multiple challenges, primarily attributed to limitations in measurement technology, interference from faults, and variations in operating environments. In-depth investigation into the intrinsic correlation between shaft vibration and bearing vibration not only enables data supplementation to improve information completeness, but also offers more precise references for fault diagnosis and condition monitoring. Therefore, this study proposes a method based on homologous information fusion, aiming to explore the intrinsic correlation between shaft vibration and bearing vibration under rub-impact faults. The study first constructs a dynamic model under rub-impact fault condition, and then fuses homologous information using full vector spectrum technology to improve the accuracy of determining the relationship between shaft vibration and bearing vibration at different rotational speeds. Finally, the reliability of simulation results is validated through the establishment of a rotor experimental rig. Experimental results reveal that by mastering this complementary relationship, the operating health status of equipment can be inferred based on the variation tendencies of other critical parameters-even when a specific measured signal is unavailable-and corresponding maintenance and management strategies can thus be formulated.
To address the critical challenge of low diagnostic accuracy in multistate bearing fault diagnosis caused by inefficient discriminative feature extraction under varying operating conditions, this paper proposes a novel Parameter-weighted Viridis Image Encoding (PVIE) method. Unlike conventional image encoding techniques (e.g., GADF, GASF, MTF, RP) that often suffer from high computational complexity and limited feature separability in complex scenarios, PVIE integrates Variational Mode Decomposition (VMD) with a newly designed Parameter-weighted Euler Difference Feature Extraction (PWEDFE) module. This module explicitly enhances the nonlinearity and periodicity of fault signatures, mapping them into lightweight 2D feature images via Viridis Feature Value Mapping (VFVM). Extensive experiments on two benchmark datasets demonstrate that PVIE achieves exceptional diagnostic accuracies of 99.92 % and 99.74 %, respectively. Compared to state-of-the-art encoding methods, PVIE improves average accuracy by 21.06 % to 39.78 % while reducing diagnostic time by 53.3 %, significantly outperforming existing approaches in both accuracy and efficiency. Furthermore, the method exhibits robust performance under strong noise interference and small-sample scenarios. These results confirm that PVIE offers a substantial advancement over current research by providing a more discriminative, lightweight, and robust solution for real-time industrial fault diagnosis.
Mechanical deformation and fatigue fracture of the steel wire layer are the main damage modes of wire reinforced hoses in automotive fuel delivery systems. To investigate the anti-vibration performance of steel wire reinforced hoses, a finite element model covering prestressed modal calculation, harmonic response analysis and random vibration analysis was constructed based on the modal superposition method. Parametric comparative analyses were carried out respectively under varied conditions of hose length (200-320 mm), wall thickness (0.75-0.9 mm) and fuel delivery pressure (0-12 MPa), and the influence laws of each parameter on the anti-vibration performance of steel wire reinforced hoses were obtained. The study revealed that the first six natural frequencies of the model ranged from 66.311 Hz to 108.877 Hz, which were highly overlapped with the energy-concentrated frequency band of 0-100 Hz in the power spectral density (PSD) of road surface excitation. Parametric analyses showed that low-frequency resonance stress could be reduced by lengthening the hose, while stress concentration at the end would be intensified. The vibration characteristics and fatigue damage mechanism of steel wire reinforced hoses under actual service conditions were clarified, which could provide reliable theoretical basis and technical support for the anti-vibration design and parameter optimization of flexible pipelines in automotive fuel systems.
