The transient vibration of a vertical axis washing machine is strong in the dehydration process，so a new planar variable damping structure is proposed to reduce the transient vibration. Firstly，the kinetic energy and potential energy of various rigid bodies of the washer are deduced，the generalized forces of the suspension structure are described，the force of the liquid balancer is analyzed，and the vibration model of the vertical axis washing machine is established using Lagrange's equation. The working principle of the planar damping structure is explored，its damping force is described and its suppression effect on transient vibration of the washer is verified. Secondly，the influence of the planar damping structure on dynamic characteristics of the washer is evaluated，the bifurcation theory is employed to analyze stability of the system. Furthermore，the distributions of the stable regions of the system is analyzed，and the appropriate disengaging speed range of the damping structure is obtained. Finally，the effect of the damping structure for suppressing transient suppression of the washer is validated though experiments，and the appropriate disengaging speed of the structure is analyzed. The results show that the planar damping structure can suppress transient vibrations effectively with little influence on other dynamic characteristics of the washer.

To study the influence of subway wheel polygons on low-frequency vibration of the car body，the polygon wear of wheels of a subway line is investigated，on the basis of grasping the distribution characteristics of wheel polygons of subway lines. A vertical dynamic model of elastic car body considering wheel polygons is established，by the time-domain integral solution method. The relationship between wheel polygon excitation frequency and low frequency vibration of vehicle body is studied. By comparing the low-frequency vibration of wheel polygons of different orders，the effect of changes in the operating speed of metro vehicles under service conditions and changes in the radius of wheel wear on the polygonal action of the wheel body is discussed separately. It is shown that a wheel polygon of order 1—3 at common operating speeds generate a low-frequency excitation frequency of 0—20 Hz，and when the excitation frequency is close to the first-order droop frequency of 10.2 Hz resonance will occur. At the beginning of service，the influence of the second order wheel polygon becomes severe on the low frequency vibration of the car body. With the wear of the wheel radius during service，the influence of the first three order wheel polygon changes on the vibration law of the car body. This paper provides a good reference value for service subway operation and wheel maintenance.

To address the inadequacy of existing models for predicting the flow resistivity of kapok felt，the airflow resistivity of kapok felt with different bulk densities is tested. Initially，the airflow resistivity of kapok felt is calculated using empirical and theoretical models commonly used in fibrous materials. Subsequently，a new empirical model suitable for predicting the flow resistivity of kapok felt is developed by fitting the experimental data. Finally，considering the cross-sectional geometries and arrangement of kapok fibers at different bulk densities，theoretical models for the flow resistivity of kapok felt are derived by determining the average velocity and frictional force within micro-units，for both circular and flattened fiber cross-sections，based on the Tarnow model and the Hagen-Poiseuille flow assumption. The models are further modified using a flattening ratio parameter. Results demonstrate that compared to the measured values，the prediction accuracy of existing models for kapok felt flow resistivity is low. The modified model is applicable to transitional states between the two cross-sectional geometries. Within the bulk density range of 20 to 180 kg/m³，the modified model exhibits high prediction accuracy.

A parameter identification method is proposed to accurately capture the nonlinear dynamic characteristics of dielectric elastomer actuators （DEAs）. First，the response signal of the actuator is acquired under swept-frequency excitation using the time-frequency analysis capability of the transient-extracting transform （TET）. Then，the harmonic and fundamental frequency components are separated and extracted，and the transfer functions for each component are computed to derive the overall transfer function of the DEA. Finally，the results are compared with experimental data. The proposed method achieves a fitting accuracy of 92.11% for the fundamental frequency transfer function and 90.35% for the second harmonic transfer function. This approach does not require prior knowledge of material properties or free energy density functions，and incorporates the influence of high-order harmonic components，offering a novel solution for parameter identification in electroactive material structures.

To investigate the fault mechanism of blade cracks and to analyze the effects of blade crack on the three-dimensional （3D） tip clearance of the rotor system，while comprehensively considering blade radial deformation，flap-wise bending，and chordwise bending，this paper develops a novel dynamic model of the rotor system based on continuum theory. With the blade breathing crack model under the three-dimensional stress state，a 3D tip clearance dynamic response model of rotor system with blade cracks is further established. The accuracy of the dynamic model is validated by comparing it with the finite element model and experiments. On this basis，the effect of blade crack depth and location on the 3D tip clearance in rotor system is further analyzed. The results show that the amplitudes of the high frequency doubling component of the 3D tip clearance increase with crack depth，while both the fundamental frequency and the high frequency doubling component of the 3D tip clearance show a non-monootonic trend as the relative crack location increases. The research results provide theoretical guidance for research on monitoring and diagnosis method of aero-engine blade crack based on 3D tip clearance.

A novel approach based on electromechanical impedance is proposed to evaluate the corrosion degree for grounding conductors，which are difficult to simply detect and evaluate for traditional methods. Firstly，according to the electrochemical corrosion model of metals，the grounding conductor parametric corrosion model is established by importing the change of grounding conductor radius as corrosion parameter. Secondly，the corrosion model between an electromechanical impedance resonance frequency and corrosion parameter is established with the analysis of system electromechanical impedance and conductor mechanical admittance. Then，the resonance frequency and corrosion parameter signal of grounding conductors are obtained via finite element simulation method，while the coefficient of the corrosion model is obtained by the least square method. The results show that the electromechanical impedance detected by asymmetrical sensor layout can reflect variations of the corrosion parameter more clearly than that by symmetrical sensor layout. Linear model by finite element simulation and the least square method can predict the corrosion parameter. Moreover，the high consistency between the datum predicted by the linear model and the experimental datum，indicates the linear model is very accurate and can be used to detect the conductor corrosion in field applications.

In response to the challenge of quantitatively diagnosing corrosion damage thickness within pipelines，a quantitative imaging method for pipeline corrosion damage using ultrasonic guided waves is proposed. Firstly，based on the frequency domain finite difference method，a numerical model for multi-path helical propagation of guided waves in pipes is established，enabling rapid calculation of guided wave reception signals when thickness map is known. Secondly，by calculating the received signals in the presence of randomly distributed damage，a database comprising 3 500 samples of damage signals is generated through iteratively running the numerical model. Subsequently，a one-dimensional convolutional neural network imaging model is constructed. The model is trained using the generated database to establish a mapping relationship between thickness maps and reception signals，and inputting the reception signals into the imaging model yields corresponding thickness maps. Finally，the feasibility of the proposed method is experimentally validated. The mean square error between experimental imaging results and actual values is 8.6048×10^-4，the correlation coefficient is 0.711 6，and the imaging model runtime is 0.538 5 seconds. The results indicate that the proposed method can achieve quantitative imaging of corrosion damage thickness within pipelines with high imaging efficiency.

When early failures occur in planetary gearboxes，the weak fault features are difficult to extract and identify due to the interference of background noise in industrial environments and the attenuation of fault impacts in complex transmission paths. To address this issue，a sparse-guided improved empirical wavelet transform （IEWT） is proposed combined with multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） method for weak fault feature extraction. Firstly，a new fault composite index （FCI） is introduced，and the original signal is adaptively decomposed into a set of IEWT components based on the amplitude envelope of the signal spectrum. Secondly，the sensitive components，selected through the sparse-guided method，are used as the sparse representation of the original weak fault signal. Finally，the MOMEDA technique is applied to the sensitive component signals to reduce signal noise and extract the weak fault feature frequencies for identification. The effectiveness of the proposed method is validated through simulations and experiments，successfully extracting and identifying the weak fault features of planetary gearboxes. This demonstrates that the method has good diagnostic performance for noisy，non-stationary，and non-linear fault signals in planetary gearboxes，providing a new approach for the diagnosis and identification of weak faults in engineering practice.

Wind tunnel pressure tests are conducted on a high-speed railway station roof to study the non-Gaussian characteristics and extreme wind pressure distribution on the long-span roof surface. First，the surface wind pressure is classified into Gaussian and non-Gaussian distributions. Then，the fitting effects of three different single probability distributions （Gumbel，Lognormal，and Weibull） and their corresponding combined distributions （double Gumbel，double Lognormal，and double Weibull） on the wind pressure time history of the roof surface are compared. The extreme wind pressures obtained from the combined probability distributions are compared with the estimates from the modified Hermite method. Finally，the extreme wind pressure distribution on the roof under all wind directions is presented. The results show that the combined probability distributions provide a better fit to the wind pressure time history than the single distributions. The extreme value estimates from each combined distribution at the same guarantee rate are more accurate than those from the single distribution. The combined distributions generally yield better estimates at the 99.90% guarantee rate compared to the modified Hermite method. The extreme wind pressure varies significantly with the wind direction，and under all wind directions，the minimum pressure coefficient reaches its lowest value at the middle of the roof edge side，reaching -5.9.

In order to improve the fault detection performance of wheelset bearings under small sample image conditions，a machine vision inspection method based on a novel multi-resolution siamese neural network （MrSNN） is proposed for surface defect detection of wheelset bearings. First，the siamese neural network （SNN） is used as the basic model framework. A multi-resolution convolution fusion block （MrCFB） containing convolution kernels of different sizes and dilation factors is constructed to comprehensively extract the detailed features and contour features from images. Then，a dual attention mechanism combining channel and spatial information is adopted to recalibrate the multi-resolution feature weights，further enhancing the image feature extraction capability of the model. Finally，the algorithm is validated through the detection and analysis of four types of wheelset bearings images： normal，scratched，pitted and spalled. Experimental results show that the recognition rate for the three types of faulty images reaches 100%，the recognition rate for normal images is 95%，and the overall recognition accuracy is 98.75%. The recognition accuracy is superior to that of traditional SNN and YOLO-V5 models.