In order to improve the carrying capacity of double curved beams negative stiffness structure composed of two curved beams arranged in parallel, the curved sandwich beam negative stiffness structure was proposed. The design idea was to array the sandwich straight beam between the upper and lower curved beams of the double curved beam negative stiffness structure, and the bearing capacity and energy absorption characteristics were studied systematically. Firstly, the negative stiffness structure model was fabricated using 3D printing technology and silicone emolding process, the compressive mechanical response of the curved sandwich beam and double curved beam negative stiffness structure was compared and analyzed by quasi-static compression experiment, and the reliability of the finite element simulation model was verified. Then,the influence of structural parameters (width, spacing, height and angle) of the sandwich straight beam on the bearing capacity and energy absorption characteristics of the negative stiffness structure was studied by simulation. The results indicate that the introduction of the sandwich significantly enhances the load-bearing capacity of the double curved beam negative stiffness structure. Compared with the spacing and angle of the sandwich straight beam, increasing the width and height of the sandwich straight beams can notably enhance the load-bearing capacity and energy absorption capacity of the structure.

To overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment, an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer (IGTO) algorithm, the optimized resonance-based sparse signal decomposition (RSSD), multi-parameter and sparse maximum harmonics-to-noise-ratio deconvolution (SMHD) method. Firstly, taking the squared envelope spectrum correlated kurtosis (SE-SCK) negative value of the low resonance component as the objective function, IGTO was used to simultaneously optimize the quality factor Q, weight coefficient λ and Lagrange multiplier μ of RSSD, for the achievement of the optimal matching of wavelet basis function and dissipation function. Secondly, the obtained optimal low resonance component was inputed into SMHD for filtering processing. Finally, the fault features were extracted by the perform envelope spectrum analysis. The algorithm comparison experiments show that the proposed IGTO algorithm has significantly improved optimization performance. The results of simulation and XJTU-SY bearing full life cycle fault signal test show that the proposed method is more useful in extracting early weak fault characteristics of bearings.

When the gear system starts or stops in non-stationary working conditions, a sharp change of the speed can cause it to exhibit complex vibration characteristics which has a significant impact on the performance and lifespan of the gear. Considering the influence of time-varying meshing stiffness, backlash and gear meshing error, a dynamics model of spur gear system was established. The influence of external load and angular acceleration on the vibration characteristics of the start-stop process was studied. At the same time, the time-frequency analysis of the non-stationary vibration signal of the gear system was carried out by using the short-time Fourier transform. The results show that increasing the load and angular acceleration during the start and stop processes will exacerbate the degree of vibration and impact of the gear pair, and both will make the unstable motion process in the early start period end earlier, and the unstable motion process in the late stop period appear later, but the impact components in the late start period (the early stop period) will increase (decrease). In the frequency domain, increasing the external load will enhance the energy of the harmonic component of the gear system’s meshing frequency, but it has no effect on the fundamental energy of the meshing frequency. However, increasing the angular acceleration will enhance the energy of both the fundamental and harmonic components of the meshing frequency.

To address the problem that it is difficult to label variable working condition gearbox fault samples and the significant data distribution discrepancies in practical engineering, which result in reduced accuracy of fault diagnosis models,a semi-supervised gearbox fault diagnosis method based on masked contrastive learning is proposed. Firstly, a random mask was used to hide part of the information in the unlabeled dataset, generating two different masked instances for each unlabeled sample. Secondly, a dynamic convolutional neural network was employed to dynamically weight and aggregate the masked instances, enabling discriminative feature modeling of different masked instances. Then, a contrastive learning framework was constructed with the optimization goal of maximizing the similarity between features of different masked instances. By enhancing the consistency of feature representations of masked instance pairs, the model's dependency on labels was reduced. Finally, during the fine-tuning phase, a domain-conditioned feature correction strategy was introduced to generate target domain feature corrections. By aligning source domain features and target domain corrected features according to the metric of minimizing domain feature distribution discrepancies, the method explicitly reduces the domain distribution differences caused by varying working conditions. Validation on a variable working condition gearbox fault dataset demonstrates the effectiveness of the proposed method.

In order to study the mechanical properties of high volume fraction ratio metal particle reinforced resin matrix composites, the elastic modulus of the composites was predicted based on the micromechanics theory and the meso-finite element method. Firstly, standard specimens of the composites were prepared, and their macroscopic elastic moduli were tested by uniaxial tensile experiments, and the microscopic properties were observed. Secondly, the elastic modulus of the composites was predicted by using Voigt, Reuss, Mori-Tanaka and Generalized means based on the micromechanics theory.Then, based on the microscopic particle size statistics of the specimens, the gradation of the metal particle size and its quantity were determined by using the Gaussian distribution law, and the random particle placement program was written by Python language to construct a two-dimensional representative volume element (RVE) finite element model consisting of the particles,the resin matrix, and the interface. Finally, the elastic modulus of resin matrix composites reinforced with high volume fraction metal particles was predicted by theoretical and finite element simulations. The analysis results show that the generalized means and finite element models predict the elastic modulus with less error from the experimental test results, and the elastic modulus of the composites increases with the increase of the volume fraction of the metal particles.

In response to the problem of the gearbox fault diagnosis and analysis based on multi-sensor data under dataset imbalanced conditions, a gearbox fault diagnosis method based on a kurtosis index data fusion and a generative adversarial neural networks (GAN) was proposed. This method weighted the fusion of multiple sensor data based on signal kurtosis,highlighting the fault sensitive components of the gearbox in the fused signal. Then, a wavelet packet transform was used to extract the energy coefficients of each frequency band of the signal as signal features. Finally, the classification and recognition of signal features were implemented based on a back propagation (BP) neural network. Due to the fact that in actual working conditions, fault signals were more difficult to obtain than normal signals, GAN was used to expand the fault data section of the dataset, and the expanded dataset was used to train BP neural network. Through test analysis, it is shown that the fault accuracy of the described method is as high as 98%, which verifies the correctness of the proposed method and provides new ideas and methods for multi-sensor data fusion and fault diagnosis problems.

In order to solve the problem of difficult to accurately extract early faults of solar wheels under the strong noise background, an improved grey wolf algorithm (newGWO) was proposed to optimize and improve the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the maximum correlated kurtosis deconvolution (MCKD) for early fault feature extraction of solar wheels.NewGWO was used to optimize the selection of parameters of the white noise amplitude weight and noise addition times that affected the decomposition effect.The fault vibration signal was decomposed by newGWO-ICEEMDAN, and the minimum envelope entropy was selected as the fitness function to obtain several related modal components.Then, the envelope spectrum peak factor was selected as the best modal component index. MCKD signals optimized by newGWO were enhanced for the selected optimal intrinsic mode function（IMF）components. Finally, an envelope demodulation analysis was performed on the obtained signals to extract the solar wheel fault characteristic frequency and multiple frequency components. Simulation signals and experiments show that this method can make the early fault impact characteristics more obvious, and realize the early fault characteristic frequency extraction of solar wheels.

Aiming at the problem of structural vibration modeling and characteristic analysis of rectangular sheets under arbitrary boundary conditions, an improved Fourier series method was proposed.Based on the Rayleigh-Ritz method, the allowable function of vibration displacement of thin plates was expressed as a linear combination of double Fourier cosine series function and auxiliary series function, which effectively avoided the possible discontinuities or singularities of the traditional Fourier series at the boundary. Firstly, the variational equation of the sheet vibration model was established by using the Hamilton energy variational principle, and the energy expressions in the equation were calculated and the displacement tolerance function was brought in. Secondly, the variational solution of the unknown Fourier coefficient was carried out to obtain the matrix equation of the model. The matrix equation was solved by numerical calculation method to obtain the free vibration frequency and eigenvector of the thin plate. Finally, the classical boundary conditions and elastic boundary conditions were used as examples to calculate and analyze. The calculation efficiency and accuracy of the proposed method were verified by comparison with the results of finite element simulation and existing literature. Additionally, the influence of the aspect ratio and constrained the spring stiffness coefficient on the vibration characteristics of the thin plate was discussed.

Single tube towers are widely used as the foundation for carrying 5G communication equipments. Due to construction needs, the mounted equipments often changes with the changes in 5G construction. Due to the small damping of the single tube tower, the increase of mounted equipments may cause an excessive vibration, reducing its load capacity.Therefore, the control of tower top vibration is particularly crucial. A particle damping tuned mass damper (PDTMD) method was proposed to control the problem of excessive vibration at the top of 5G communication towers. Based on a collision theory, a mathematical model using PDTMD to control the vibration of the communication tower was established. The vibration response of the tower under effects of PDTM was verified by the detailed calculation, and the damping mechanism of PDTMD was analyzed. The damping effectiveness of PDTMD was compared with the traditional tuned mass damper (TMD).The results show that the particle damping has good energy dissipation ability. Compared with traditional tuned mass dampers,PDTMD has better damping effect and higher robustness. Finally, based on the actual signal tower, the usage parameters of PDTMD in complex environments were optimized. Effects of gaps between damping particles and honeycomb structures,particle materials, and particle mass ratios on the damping effect of dampers were analyzed.

In order to study the dynamic behavior of stiffened cylindrical shells with composite material sandwiched by co-cured damping films under the clamped boundary condition, the specimens of stiffened cylindrical shell with composite material sandwiched by co-cured damping films were prepared, and the dynamic modal test platform was set up. The fundamental frequency, damping ratio and modal shapes of stiffened cylindrical shell specimens were solved, and the accuracy of finite element model was verified. The influence of geometric parameters on structural vibration characteristics was further discussed by the numerical simulation method. The results show that, the fundamental frequency, damping ratio and modal shape of the structure will change abruptly when the height of stiffeners changes, and there is a suitable height value before the abrupt change to make the whole structure consider both damping and stiffness requirements; when the thickness of composite materials is constant, the fundamental frequency of the whole structure decreases gradually and the damping ratio increases gradually with the increase of the damping thickness or damping layer number of single layer; for the stiffened cylindrical shell of single layer damping composite materials, the closer the damping layer is to the inner skin, the higher the stiffness is, and vice versa, the damping capacity is better.