The float of the oscillating-buoy wave energy converter is the core mechanism to obtain wave energy, while the size of the float's motion stroke has an important influence on the energy conversion performance of the converter. Due to limitation of the stroke, in the real sea condition operation often collide with the stroke limiting mechanism, which causes energy loss and even leads to damage of the converter, so it is necessary to install the limiting mechanism with vibration damping in order to provide a buffer, and at the same time, through the adjustment of the parameter of the damping mechanism in order to reduce the energy loss due to the collision with the limiting mechanism. This paper targets the problem of vibration characteristics of the oscillating-buoy wave energy converter with a limiting mechanism, a mathematical model of energy conversion with an end-stop system has been established, for which a time domain method to avoid the huge potential energy error due to the step change of the spring stiffness in numerical simulation has been proposed. On this basis, the effects of Power Take Off (PTO) system parameters, mass, stroke, and end-stop system parameters on the energy capture width ratio of the convertor were investigated, a parameter optimization design method for the limit mechanism is proposed. The results are instructive for further engineering studies of oscillating-buoy wave energy converter.

Owing to the inherent flexibility of the industrial robot joints, the machinery manifests heightened vibrational tendencies during operation. To address the challenge of isolating fault components within mixed vibration signals acquired during instances of robot joint malfunctions, a vibration separation method for robotic joints based on a mixed drive consisting of models and data is proposed. Initially, the actuator dynamics response model is constructed by amalgamating multi-physics signals with system dynamics. This response signal serves as the benchmark signal in the process of vibration separation. Subsequently, the amplitude spectral percentile sequence was developed. Variable point analysis is employed to ascertain the optimal noise threshold, complemented by the design of a band-pass filter for noise segregation. Additionally, efforts are made to eliminate phase errors arising from measurement and filtering between the reference vibration and mixed vibration signals, a method employing Adjustable Factor Dynamic Time Warping is presented. Ultimately, the separation of fault components is achieved by subtracting the reference vibration from the denoised and phase-corrected mixed vibration. Experimental findings obtained from a robotic joint test platform substantiate the efficacy of the proposed methodology in successfully isolating fault components from joint vibrations.

To investigate the problem of excessive vibration induced by the severe wheel-rail interaction of metro trains passing through the turnout area of the depot, relying on the good elasticity and damping characteristics of the sleeper pad and the ballast pad. Laying sleeper pad and ballast pad in turnout area provides an effective method to realize the homogenization of turnout stiffness and alleviate the problem of vibration over limit. Based on the principle of turnout stiffness homogenization and finite element model of turnout rail-turnout sleeper, the stiffness homogenization of sleeper pad is designed, and the influence of the stiffness before and after the stiffness homogenization of sleeper pad, the layout range of sleeper pad and the stiffness of fastener on the stiffness homogenization of turnout is discussed. Combined with the limit value of rail longitudinal deflection change rat, the preliminary design scheme of switch stiffness homogenization was clarified. Then, the coupled dynamic model of vehicle- turnout -ballast bed is established, and the test shaft drop height before and after the turnout stiffness homogenization is calculated. The indoor shaft drop test is used to evaluate the vibration reduction effect of the combination measures of ballast pad and sleeper pad, and the final design scheme of turnout stiffness homogenization is defined. Finally, field test is carried out to verify the damping effect of turnout stiffness homogenization. The results show that laying the sleeper pad in the turnout area is helpful to improve the uneven stiffness of the turnout along the longitudinal direction. A zoning design is recommended for the non-frog and frog areas to meet the limit value of rail longitudinal deflection change rate and the demand of vibration reduction effect in the turnout area. The stiffness of fastener has no significant effect on the homogenization of stiffness in the turnout area. When the ballast pad is used, the maximum insertion loss of Z vibration level is about 9 dB, and the vibration reduction effect of the combination of ballast pad and sleeper pad can reach about 15 dB. Field measurement results show that the combined damping method of sleeper pad and ballast pad can achieve the expected damping effect.

As space vehicles face increasingly severe mechanical environments, the thermal and vibration conditions have become key factors in causing structural damage, product function degradation, or failure. This paper establishes a virtual test method considering the composite environment of triaxial vibration and heat. Taking the simplified cylindrical cabin structure as the object, the quartz lamp array high-temperature heating simulation model and the triaxial vibration simulation model are established respectively, and the shaker-fixture transfer function is obtained through the experimental data, then the three parts are combined to establish the triaxial virtual thermal vibration simulation model. The accuracy of the model is verified by uniaxial and triaxial thermal vibration experiments, and the transfer function of the central measurement point of the structure with the time-varying temperature is obtained based on the verified model. The accuracy of the finite element model of the structure is verified by a modal test, and the error of the first 6 orders of modal frequencies does not exceed 5%. In the uniaxial thermal vibration experiment, the error between the RMS value of the response of the simulation model and the experimental acceleration response is no more than 15%; in the triaxial thermal vibration test, the RMS error is no more than 20%, which verifies the validity of the proposed virtual thermal vibration method.

To address severe structural damage to support systems under rock bursts in coal mine roadways, this paper proposes a multi-cell circular steel tube sandwich panel. The panel, when combined with metal support structures in coal mine roadways, resists rock bursts through joint collapse deformation and energy absorption. Four sandwich panel specimens with different core thicknesses were designed for drop hammer impact test. Finite element models were established and validated based on experimental data, enabling a parametric analysis of the impact response and energy absorption characteristics of the multi-cell circular steel tube sandwich panels. The study reveals that under impact the multi-cell circular steel tube sandwich panel exhibits a disc-shaped depression, and the core cell and cross ribs undergo wrinkling and sinking deformation. Each chamber works collaboratively, supports each other, and absorbs energy through plastic deformation. At the same time, the peak of the bottom total impact reaction always lags and is lower than the peak impact load of the drop hammer. Under multiple impact conditions, the panel also demonstrates good energy absorption and cushioning effects. The multi-cell circular steel tube sandwich panel can absorb over 90% of the impact energy with over 65% of the energy absorbed by the core Increasing the core thickness from 0.5 mm to 2.0 mm increases the proportion of energy absorbed by the core by 24.17%. Conversely, the panel's energy absorption efficiency decreases by 17.17%, and reducing the core thickness can lower the peak impact load. A core thickness of 1.0 mm shows better energy absorption efficiency. The facesheet thickness has a minor impact on energy absorption performance. It is recommended to choose a thickness close to that of the core. Changing the drop hammer weight and impact velocity has little effect on the structural dynamic impact performance and energy absorption efficiency under a certain impact energy.

Aiming at the problem of a single vibration signal containing fault information being easily hidden and the weak diagnostic ability of a single deep learning model leading to low accuracy in bearing fault diagnosis, a deep learning fault diagnosis method based on multi-domain information fusion is proposed in this paper. Variational Mode Decomposition (VMD) method is adopted to decompose the original vibration signal into multiple IMF components, while fast Fourier transformation FFT transforming each IMF component into frequency domain samples. After that, multiple IMF components and their corresponding frequency domain samples are inputted into multiple deep metric learning (DML) models and deep belief network DBN models for preliminary diagnostic analysis, respectively. And then a simple soft voting method is used to fuse these preliminary diagnostic results to obtain the final diagnostic result. Finally, through the analysis of bearing fault diagnosis experiments, the results show that the proposed method not only has good diagnostic performance, but also outperforms information fusion diagnosis methods based on time domain and frequency domain, respectively.

Considering that the composite fault characteristics of freight car rolling bearings are relatively fuzzy in noisy environments, and the mutual influence between various fault features makes it difficult to effectively distinguish the composite fault features, a total variation filtering (TVF) based on the largest Lyapunov exponents (LLE) and permutation entropy (PE) are proposed A rolling bearing composite fault diagnosis method based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA). Firstly, noise analysis is performed on the composite fault signal, and the Lyapunov exponent and signal chaos are determined based on the presence of noise in the signal. At the same time, the penalty is set to appropriate regularization parameters to achieve adaptive noise reduction in complex environments. Then, the fault signal is deconvoluted by changing the filter length in MOMEDA by permutation entropy, and different fault features are separated, Perform Fourier transform on the signal to extract fault characteristic frequencies, and finally use the Teager energy operator to enhance the deconvoluted fault impact signal, achieving accurate discrimination of composite faults in rolling bearings. By applying this method to simulate composite faults of rolling bearings in simulation signals and actual composite faults of freight car bearings, the results show that this method can accurately separate the characteristics of composite faults and successfully identify the types of faults.

Noise frequently interferes with the fault diagnosis of industrial centrifugal pumps. To address this issue, this paper introduces a feature selection method that combines empirical mode decomposition and an autoencoder. Initially, effective time-frequency features are identified using compensation distance evaluation technology. Subsequently, modal components, which contain various scale and frequency features, are derived through empirical mode decomposition. The component with significant energy ratio variation is selected as the effective analysis component, from which effective features are extracted. These features are then concatenated to construct high-dimensional deep features. An autoencoder is subsequently utilized to reduce the dimensionality of these deep features, further refining the selection process to isolate the final fault-sensitive features for comprehensive feature extraction. In this study, a support vector machine is employed as the fault diagnosis model. Comparative analysis of multiple fault data from industrial centrifugal pumps demonstrates that the accuracy of the proposed method surpasses traditional time-frequency feature-based approaches by 6.13%、7.46% and 12% under -5dB, -7dB, and -10dB strong noise conditions, respectively. This method exhibits robust noise resistance and effectively extracts sensitive equipment state features under noise interference.

Double-layer non-linear resilient fasteners are used as a medium vibration damping track on metro lines in China. However, short-pitch rail corrugation on the track has occurred on the track after the vehicle operation, resulting in severe high-frequency wheel-rail vibration and noise. A finite element model for the track with the resilient fasteners was established using ABAQUS to investigate the dynamic characteristics of the track. In order to achieve a balance between computational efficiency and mitigation of the model’s boundary reflection, the track model was simplified to a beam-shell-solid model using a multi-scale finite modelling technology. The influence of wheel-rail coupling, vertical stiffness and damping of the fasteners on the vertical dynamic characteristics of the track was investigated. The results indicated that (1) the simplified model integrating a 12.5 m solid element with a 37.5 m beam-shell element can reduce the computation time by 65.3%, while the simulation results were essentially consistent with the field test. (2) The rail first-order vertical bending and the pinned-pinned resonance modes were clearly seen in the rail vertical impedance in the frequency bands of 100~150 Hz and 1022~1101 Hz, respectively. (3) The vibration modes of the track below 100 Hz represented the bending and torsion of the whole track. The pre-stress of the track caused by the vehicle static load leaded to wave reflections of the rail between the two wheelsets in the frequency bands of 400~800 Hz. (4) Considering the flexible wheel-rail interaction, the vertical impedance of the track exhibited distinct peaks at 43 Hz, 381 Hz, and 641 Hz, which were attributed to the P2 resonance and the second and third bending of the rail within the bogie wheelbase. In addition, the flexibility of wheel induced new fluctuations in rail the vertical impedance at 180 Hz, 341 Hz, and 504 Hz. The mass effect of the wheelsets significantly suppressed the impedance at the frequencies of 75 to 250 Hz. (5) The frequencies of rail vertical first-order bending, wheel-rail P2 resonance and rail bending within the bogie wheelbase increased with increasing the fastener stiffness. The fastener vertical damping had only a suppressing effect on the amplitude of the rail resonances.

Droplet freezing plays a crucial role in the growth of subsequent frost layers as the initial beginning of frost formation. To study the freezing law of liquid droplets under vibration, numerical simulation analysis was conducted by adding sinusoidal periodic vibration conditions to a horizontal cold surface, and an experimental platform was constructed for verification. The research results found that vibration conditions changed the morphology of the solid-liquid interface, shortened the time required for complete freezing of droplets, and changing the vibration frequency and amplitude would accelerate droplet freezing; In the early stage of droplet freezing, due to the influence of solidification heat release and solid phase growth, the temperature change at the measuring point shows a trend of first slow, then fast, and then tends to be gentle. The average rate of temperature change at the measuring point is directly proportional to the vibration frequency and amplitude; During the freezing process of droplets, the fluctuation degree of the phase interface is mainly affected by the vibration amplitude and droplet size. When the volume is constant, the larger the vibration amplitude, the more obvious the fluctuation degree of the phase interface. Under the same vibration conditions, the smaller the droplet volume, the more obvious the fluctuation degree of the phase interface.

High-end turbomachinery represented by aero-engine and shipboard gas turbine in high efficiency, large maneuvering flight and other working conditions, the internal structure tends to be compact and complex, so that the probability of internal rotor stator rubbing increases, due to the internal less measurement points, difficult to monitor, often difficult to accurately find rubbing faults in a timely manner. In this paper, we propose a rotor rubbing fault diagnosis method by fusing the time-frequency characteristics of the blade tip clearance. Firstly, we obtain the Blade Tip Clearance (BTC) sequence through the blade tip vibration-clearance composite sensor, and then we evaluate the abnormality of the BTC sequence through the machine learning algorithm; if there is an abnormal alarm, we carry out Fourier decomposition of the abnormal signals and summarize the time-frequency energy spectrum of the components. Finally, the occurrence of faults is determined by the rubbing bands in the time-frequency energy spectrum. The experimental results show that the machine learning model is able to diagnose the rotor in real time after applying short-time and continuous rubbing conditions, and the proposed method can completely separate the occurrence of the two types of rubbing and the process of continuous rubbing that is applied slowly and ends quickly after the alarm. Comparison with the rotor vibration signal shows that the method can obtain the BTC and sense rotor rubbing faults through the magazine measuring point, realizing "one sensor and many messages", which provides a new method for the condition monitoring and health management of turbomachinery. 

In the process of shock response spectrum (SRS) test, the specimen is affected by nonlinear, local resonance and other factors, which leads to local out-of-tolerance, and the fundamental wave shape parameters in the iterative time domain need to be corrected many times. By analyzing the main influencing factors and change mechanism of SRS test results, a spatial neighborhood driven strategy particle swarm optimization algorithm based on adaptive learning (PSO-LSN) was proposed. The local space search ability is enhanced according to the particle neighborhood similarity, the optimal position and velocity information is shared, and the update step is adjusted by combining the adaptive learning mechanism to realize the optimization of the time domain waveform synthesis of the shock response spectrum based on the synthetic fundamental wave method. The experimental results show that the time domain waveform synthesis based on PSO-LSN algorithm has a good global search ability in the decision domain space in the early stage of iteration. With the increase of the number of iterations, its local fine search ability is significantly improved, and high precision experimental results can be obtained, which effectively verifies the accuracy and practicability of the algorithm. It can provide support for further improving the accuracy of SRS time domain waveform synthesis calculation.

To study the lubricant temperature-viscosity thermal effect on the conical hydrodynamic/hydrostatic float-ing ring bearing performance, the thermohydrodynamic lubrication governing equations were established, the Finite Element Method and Finite Difference Method were used to solve Reynolds equation, energy equation, temperature-viscosity equation and floating ring equilibrium equation simultaneously, and oil film temperature distribution was described from the bearing temperature measuring experiment further. The variation regular of lubrication bearing characteristics including load carrying capacity, friction moment and side leakage flow were analyzed, the influence of temperature-viscosity thermal effect on the minimum film thickness was revealed. The results show that there exists a non-uniform temperature distribution, the film temperature is lower relatively in the deep pocket, and it climbs rapidly near the bearing land. Thermal ef-fect decreases the load carrying capacity, whereas increases the side leakage flow at high speed. Inner and outer minimum film thickness drop with the increase of inlet temperature, to avoid oil film rupture and even lubrication failure caused by load carrying capacity decline, it is essential to consider the influence of temperature-viscosity thermal effect at the stage of bearing design and analysis.

Squeezed film gas bearings have a wide range of applications in high-speed rotating machinery due to their advantages of good stability and various operating modes. Under high frequency vibration, the coupling between its structural vibration and lubrication gas film is a typical fluid-solid coupling problem, in order to study the lubrication Load-carrying capacity of extruded film gas bearings, a new type of squeezed film gas thrust bearing is proposed based on the principle of Squeeze effect. The finite element method is used to solve the vibration pattern of the bearing structure, and based on the fluid lubrication theory, an analytical model of the levitation Load-carrying capacity of the coupled bearing structure vibration pattern is established, and the influence laws of the bearing structure, driving voltage and other parameters on the levitation characteristics of the extrusion are analysed. The results show that the gas film pressure of the squeezed film gas bearing changes periodically, and the average gas pressure larger than the ambient pressure during the cycle provides the levitation force, and the levitation force increases with the increase of the driving voltage, and the theoretical and experimental results coincide with each other, which verifies the correctness of the levitation bearing model. This study can further enrich the theoretical model of gas thrust bearing.

In order to ensure the safety of automobile driving, the hood needs to meet the requirements of stiffness, modal and pedestrian protection. Aiming at the lightweight design requirements of automobile hood, a finite element model of cast aluminum integrated hood was established to analyze the stiffness, modal and pedestrian protection performance of the hood, and a hybrid RBF-Kriging approximation model was constructed and combined with the AMGA algorithm to perform multi-objective optimization of the hood. Aiming at the Pareto solution set generated by multi-objective optimization, a FAHP-TOPSIS method was proposed to comprehensively evaluate the Pareto non-dominated solution set. Pareto non-dominated solutions were ranked in terms of comprehensive performance, and the optimal solution was selected by combining subjectivity and objectivity. The results show that under the premise of meeting the performance requirements, the mass of the optimal integrated hood is reduced by 32.14%, and the lightweighting effect is remarkable.

The structural pounding response is closely related to the characteristics of adjacent structures and ground motion. Under the influence of many parameters, many inconsistent and even conflicting conclusions have been produced in previous studies. Therefore, the primary difficulty of structural seismic impact research is to solve the influence of a large number of parameters. The dimensional analysis method can effectively solve this problem. In this paper, dimensional analysis method is used to study the collision response of adjacent buildings in a row. Based on the contact element method, the force, deformation and energy dissipation during the contact process are simulated, and the collision response law is clearly expressed through reasonable dimensionless parameters. The influence of the unilateral and bilateral impact in different structural layout is analyzed. The results show that the response of the flexible structure is significantly reduced by bilateral impact compared with unilateral impact; When the middle structure has a larger mass and stiffness, the pounding force is the largest, which is much larger than that in unilateral impact case.

Aiming at the problem that Aquila optimizer (AO) is prone to local optimization and the accuracy of long short-term memory (LSTM) network is affected by parameters, a model of LSTM neural network based on improved Aquila optimizer （IAO） algorithm is proposed and applied to the fault diagnosis of rolling bearings. Firstly, the hypercube strategy is introduced to optimize the initial mass of the population, and the adaptive spiral strategy is designed to balance the global search ability and local search ability of AO algorithm, and the ability of AO algorithm to jump out of the local optimal is enhanced by using Gaussian mutation strategy. Then, the weights and thresholds of LSTM are optimized by the proposed IAO algorithm, and a rolling bearing fault diagnosis model based on IAO-LSTM network is constructed. Finally, the experimental results of Case Western Reserve University (CWRU) bearing data set and Paderborn University (PU) bearing data set show that compared with other fault diagnosis models, the IAO-optimized LSTM model has higher classification accuracy and can effectively identify various fault types of rolling bearings.

On January 1, 2024, at around 15:10 Beijing time (16:10 local time), a magnitude Mw7.5 (or Mj7.6) earthquake occurred near the Noto Peninsula in Ishikawa Prefecture, Japan. The earthquake resulted in loss of life and property damage. The study examines characteristics such as the attenuation characteristics of Peak Ground Acceleration (PGA) and acceleration response spectrum peak with respect to epicentral distance, the amplitude ratio of vertical and horizontal components of acceleration records, strong ground motion duration characteristics, and period-related properties using a selected set of 411 strong motion records. The results indicate that the peak ground motion recorded near the epicenter was relatively high, and the strong earthquake was distributed widely along the northeast-southwest direction on the western side of the Noto Peninsula, resulting in a large affected area. However, the intensity of ground motion rapidly decreased in a linear fashion with increasing epicentral distance, indicating a fast attenuation rate. The analyzed strong ground motion records showed significant long-duration characteristics, with the shortest duration   being 13.38 seconds. Additionally, as the epicentral distance increased, the duration also tended to increase. Moreover, the “bimodal values” phenomenon was observed in the temporal records near the epicenter, which may be attributed to the presence of bi-directional fault rupture with longer fault planes. Furthermore, the strong ground motion records exhibited a relatively abundant low-frequency content, especially when the epicentral distance exceeded 100 km, indicating pronounced long-period characteristics. It can be attributed to the soil conditions of soft ground sites and the characteristics of earthquake clusters. The identified features can contribute to seismic design considerations for engineering structures.

Regarding the inverse identification problem of the elastic constraint stiffness for the bolted hard-coating cylindrical shells, there still remains a challenge in effectively reducing vibration testing and time costs while maintaining high accuracy and efficiency. To address the problem, an inverse identification method is presented for the elastic constraint stiffness of the bolted hard-coating cylindrical shell, based on the Marine Predators Algorithm and modal shape identification. This method establishes a dynamic virtual prototype of the bolted hard-coating cylindrical shell and a dynamic finite element model of the elastic-constrained hard-coating cylindrical shell, by employing the ANSYS and MATLAB co-simulation technology to iteratively identify the constraint stiffness under different pretightening conditions. Numerical experimental results indicate the favorable comprehensive performance of this method in inverse identification as well as its cost-efficiency. Meanwhile, taking the NiCoCrAlY+YSZ hard-coating cylindrical shell as an example, further improvement is focused on the action pattern of constraint stiffness on the vibration characteristics of the hard-coating cylindrical shell under bolted conditions, particularly from the aspects of single-variable and multi-variable constraint stiffness influence analysis. The results demonstrate that as the constraint stiffness increases, the shell natural frequency exhibits a trend of rapid rise, followed by a gradual stabilization. When a larger stiffness value transforms the bolted elastic constraint into fixed-support constraint, and the lower circumferential wave number natural frequencies are more sensitive to the variation of constraint stiffness. The axial constraint stiffness ku has a significant impact on the shell natural frequency, while radial constraint stiffness kw and torsion constraint stiffness kt exerts relatively minor effects, and the magnitude of impact is depended on the circumferential wave number n. When the torsion constraint stiffness kt≥1×104 N∙m/rad, its impact on the natural frequency could be neglected.

In response to the challenge of simultaneously meeting the requirements for bending stiffness and multi-level seismic resistance of prefabricated bridge piers with single-form connections, the prefabricated bridge pier with hybrid connections was proposed for the first time in the Beijing-Xiong’an transit express line. The connection was comprised of unbonded prestressed tendons, grouted sleeves, concrete shear keys and shallow sockets. A refined numerical model was established and validated through the indoor quasi-static test of a full-scale prefabricated bridge pier model. The difference of seismic performance between single connected pier and hybrid connected pier is compared. The effects of four connection parameters on the seismic performance of bridge pier were systematically investigated, including prestressed level, the position of prestressed tendon, grouted sleeve length, and socket depth. The results show that, the prefabricated bridge pier with hybrid connections was a typical flexural failure mode under horizontal loading, indicating reliable connection performance and good seismic performance. Increasing prestressed level enhances the horizontal load-bearing capacity of bridge piers but reduces deformation capability, leading to increased residual displacement. When prestressed levels increase from 30% to 70%, the load capacity increases by 10.16%. Changing the position of prestressed tendon minimally affects the seismic performance of bridge piers. Increasing grouted sleeve length significantly decreases the plastic hinge length of piers. Increasing the socket depth from 0.06D to 0.18D enhances the lateral restraint effect of the pier cap, increasing hysteresis energy dissipation by 15.87%. To ensure the prefabricated bridge pier with hybrid connections have good re-centering and energy dissipation, prestressed level should not exceed 50%, and the socket depth of the pier base plug should not exceed 0.12D.

In order to study the effect of pounding between adjacent buildings on their seismic response, adjacent buildings were simplified into adjacent single-degree-of-freedom systems and the concept of pounding response spectrum was proposed. Pounding response spectrum can reflect the impact of pounding on the seismic response of adjacent structures, and also includes the characteristics of ground motion. The pounding force and the seismic response of adjacent structures with different parameters are analyzed statistically in the elastic range under action of 273 ground motion records in high intensity area. The results show that when the mass of adjacent structures is the same, the smaller the mass, the smaller the pounding force and the larger the acceleration response. When the mass of adjacent structures is different, the reaction of structures with low mass is more affected by the pounding, and the greater the mass difference, the greater the impact. The effect of peak ground acceleration(PGA) and seismic joint width on the pounding spectrum is similar, that is, the larger the PGA is, the smaller the seismic joint width is, the larger the structural response is, and the vice versa. Under the action of ground motion in sites of class IV, the acceleration response amplification effect of adjacent structures pounding is the smallest, and the effect is similar under the action of ground motion in the other three types of sites.

The Bayesian SFFT (Scaled FFT, SFFT) modal parameter identification method is proposed to address the problems of uncertainty in identification results and single quantification index in traditional Bayesian approaches. It involves solving a four-dimensional numerical optimization problem to obtain the optimal estimation of modal parameters. Monte Carlo sampling is employed to generate posterior covariance matrices and information entropy, enabling dual uncertainty quantification of the identification results. The effectiveness of this method is validated through numerical simulations and engineering applications. The study investigates the impact of the frequency bandwidth coefficient k on the identification results and compares the quantification effects of the coefficient of variation and information entropy. The results indicate that restricting the frequency bandwidth coefficient k between 7 and 9 ensures a balance between error and uncertainty. In quantifying the identification results of damping ratio, information entropy exhibits superior quantification performance compared to the coefficient of variation.

In the process of structural damage identification and health monitoring using unscented Kalman filtering, the noise is often unknown and time-varying, and the traditional unscented Kalman filtering is prone to performance degradation and dispersion when the noise is not selected properly. Therefore, an online structural damage identification algorithm based on an adaptive unscented Kalman filter is proposed. The proposed method uses covariance matching and forgetting factor method to identify and update the measurement noise and process noise in real time by residuals and innovations, which improves the accuracy of the unscented Kalman filtering for the identification of unknown parameters and damages of the structure while guaranteeing the positive characterization of the noise matrix. Numerical examples of the bridge model and the nonlinear model are used to verify the effectiveness of the proposed method. The results show that the method in this paper can effectively recognize the location and severity of damage in large civil engineering structures and nonlinear structures and is adaptive and robust to time-varying noise.

Establishing a simulation model for the entire lifecycle of rolling bearings is of great significance for their degradation performance analysis and health management. However, there is little research on how to establish a physical model for the full life cycle of rolling bearings. In order to better study the degradation law of rolling bearings, this paper proposes a dynamic modeling based model for the full life cycle degradation of rolling bearings. Firstly, based on the structural and motion parameters of the rolling bearing, a four degree of freedom dynamic model for the inner and outer ring faults of the rolling bearing was established, and the accuracy of the model was verified through experimental verification; Then, this article constructs a defect index function; Finally, the defect index function was substituted into the dynamic model to obtain a full life cycle degradation model for rolling bearings. The effectiveness of the degradation model was verified by comparing the degradation vibration signals obtained from the simulation model with the measured signals throughout its entire lifecycle. The research results of this article provide theoretical support for monitoring the health status and predicting the service life of rolling bearings.

Inertia dampers are a new type of mechanical element, which are often interconnected with spring and damping elements to form inertia dampers to synergize energy dissipation and vibration damping. In the vibration control of engineering structures, inertia dampers (e.g., TIDs and TVMDs) often have better vibration damping capabilities than conventional viscous dampers. In order to investigate the vibration damping mechanism and advantages of the two types of inertia dampers, TID and TVMD, this paper, based on a simplified SDOF structure, utilizes the kinetic theory to derive the expressions for the additional equivalent stiffness coefficients and damping coefficients provided by the two types of inertia dampers to the structure under dynamic conditions. The explicit conditions for the inertia dampers to provide additional positive and negative stiffness and to produce the damping enhancement principle are derived from the analytical study of these expressions. In addition, this paper shows the negative stiffness characteristics of the inerter element based on the hysteresis curve and illustrates the amplification of the response of both ends of the viscous damping element by the inertia element and the spring element inside the damper under the damping enhancement principle, which intuitively explains the vibration-damping advantages of the inertia dampers.

The relevant regulations for the amplification effect of ground motion on irregular terrain were all based on isolated terrain. However, mountainous topography often existed in the form of mountains, and adjacent topography would affect the earthquake wave propagation and change the law of ground motion. Therefore, it was of great significance to study the ground motion amplification factor of non-isolated terrain for the seismic design of mountain buildings and improving the accuracy of post-earthquake disaster assessment. In this paper, the typical topographic amplification effect occurred in the unfavorable section of Moxi platform during the Luding MS6.8 earthquake was described. Then, the influence of complex topography (ridge and canyon) on the amplification factor of ground motion of the platform was deeply explored by simulation. The spatial distribution of amplification factor, Fourier spectrum of acceleration and amplitude ratio were quantitatively studied, and the motion rules of complex topographic on platform surface were obtained through a large number of analyses. The results showed that the regulations in “Seismic Code” underestimated the topographic effect of the platform in some cases, and the suggestive value of the amplification factor was difficult to ensure the safety of the structure. Thus, it needed to be adjusted and refined. In addition, the adjacent ridges and canyons had obvious effects on the platform surface and should not be ignored. Therefore, it was suggested that the relevant specifications should increase the adjustment coefficient to consider the interaction between adjacent landforms.

This study presented the derivation of oscillation equation for bridge piers taking into account the water-structure-soil interaction, and verified its accuracy with field measurement. The pier-cap-plie structure system was simplified as Bernoulli‑Euler beam, with water-structure interaction(WSI) represented by the added mass and soil-structure interaction(SSI) represented by elastic Winkler model. Structural vibration mode functions were derived using variable separation method. Natural frequencies were determined by solving the eigenvalues of a matrix equation established in accordance with boundary conditions. The E11 pier of Fuzhou Pushang Bridge was used as a case study. The core seismograph of the ocean bottom seismograph (OBS) was installed on bridge cap, respectively, to collect the vibration responses. Additionally, the natural frequencies were extracted. A land seismograph is synchronously installed to validate the effectiveness of OBS underwater monitoring. The rationality of the semi-analytical solution of the natural frequency for bridge piers was verified by comparing it with the finite element solutions and measured values. Furthermore, the impact of SSI and WSI on the natural frequency was analyzed. The results show that OBS was able to meet the environmental requirements for underwater testing of bridge piers. The semi-analytical solution for bridge piers considering water-structure-soil interaction was reasonable. The SSI and WSI were able to reduce the first natural frequency of the bridge piers, among which the influence of SSI is more significant than that of WSI.

Compact and lightweight acoustic metamaterials designed for achieving low-frequency and wide bandgap characteristics hold extensive application prospects in manipulation of elastic waves. Herein, a multi-bandgap integration acoustic metamaterial is proposed, capable of integrating Bragg-scattering bandgaps, local-resonance bandgaps, and inertial-amplification bandgaps, having rich degrees of freedom in bandgap regulation. Based on the established finite element simulation model, the bandgap characteristics and formation mechanism of the proposed acoustic metamaterial are theoretically calculated and analyzed. The frequency response functions of a finite-sized plate attached with acoustic metamaterial were measured to verify the predicted bandgap characteristics and to quantify the low-frequency broadband vibration suppression effect. It shows that the proposed acoustic metamaterial can achieve an average reduction of 21 dB in structural vibrations and 6 dB in near-field sound radiation within a wide frequency range of 100 Hz to 1000 Hz. Additionally, for the curved host plate, the proposed acoustic metamaterial also demonstrates the effectively low-frequency broadband vibration suppression effect in experiments. Finally, the influence of structural parameters on the bandgap characteristics is investigated. This study aims to provide ideas and methods for the study of acoustic metamaterials with low-frequency and wide bandgap characteristics.

Based on the digital laser dynamic caustics experimental system, this study investigates the effect of uncoupling coefficient on the propagation law of blasting cracks by changing the diameter of the central charge borehole. The results show that as the uncoupling coefficient increases, the impact of the explosion shock wave on the borehole wall weakens, and the expansion and compression effect of the explosive gas gradually strengthens. The damage effect of explosion on the non-slit direction of the blast hole gradually weakens as the diameter of the blast hole increases. When coupled charging, the crushing area around the blast hole is the largest, and there is almost no crushing area around the blast hole when the diameter of the blast hole is 12mm or more. From the side, it can be explained that when the diameter is 10mm, the shock wave in the air layer still has a certain strength, which can crack the surrounding plate. The results of fractal dimension are consistent with the results of dynamic caustics experiments, and the extension length of the main crack follows a trend of first increasing and then decreasing. When the uncoupling coefficient is 1.67, this "weakening strengthening" effect has the best promoting effect on crack propagation. The research results provide certain guiding significance  
for the charging parameters of slot blasting in practical engineering.

Effectively reversing the stiffness control can improve the torsional vibration performance of the system by adjusting the inherent characteristics and resonance frequency of the system. Proper torsional damping adjustment can suppress the amplitude of torsional vibration by dissipating the energy of torsional vibration. For the different influence laws of torsional stiffness and torsional damping on the torsional vibration of transmission systems, a hybrid Taguchi genetic algorithm-based Human-Simulated Intelligent torsional vibration control strategy for magnetorheological (MR) transmission systems with variable stiffness and variable damping was proposed. Firstly, the dynamics model of MR variable stiffness and variable damping transmission system was established. A hybrid Taguchi genetic algorithm was adopted to dynamically seek for the optimal combination of torsional stiffness and torsional damping parameters for MR transmission system under various excitation frequency conditions, and then a human-simulated intelligent controller (HSIC) is designed. Finally, simulation analysis and experimental investigation were carried out. The results indicate that the hybrid Taguchi genetic algorithm-based HSIC with variable stiffness and variable damping can effectively suppress the torsional vibration and significantly improve the output characteristics of the MR transmission system. When the excitation frequency of the test is 3.5Hz, the proposed HSIC system reduces the peak angular displacement and angular velocity of the system by 44%, 48% and 14%, 18%  respectively compared to the passive transmission system and the improved skyhook control system. Furthermore, the control performance of the proposed human-simulated intelligent control algorithm is superior to that of the improved skyhook control algorithm.  

Acoustic metamaterials, as a new category of artificial composite structural materials, possess many innovative and anomalous physical properties that are not available in natural materials, which provide a brand-new research pathway and application opportunity for the effective control and precise regulation of acoustic waves. However, in order to obtain the specific structure-function response of acoustic metamaterials, the traditional design methods need to continuously and repeatedly adjust the material parameters or structural morphology during theoretical derivation, numerical simulation, and experimental validation, which substantially increases the research computational cost. Machine learning, with powerful nonlinear fitting capabilities, can bypass the physical modelling process through optimization algorithms and directly construct appropriate mapping relationships in the parameter space to meet the target function, providing the possibility of breaking through the high limitations of traditional physical design strategies. This paper reviews advances in the application of machine learning to acoustic metamaterials over the last few years. Firstly, the basic development of acoustic metamaterials and the fundamentals of mainstream machine learning algorithms are briefly outlined, and then the latest research results on the application of machine learning in phononic crystals, acoustic metamaterials, and acoustic metamaterials topology design are presented in detail, and finally, the current research status in the field is discussed and outlooked accordingly.

To solve the problem of poor comfort and impact resistance for all-terrain tracked vehicle, a torsion vibration damper suspension system with arc spring damping structure is proposed. A single load wheel torsion vibration damper suspension system is designed, and its dynamic modelling and comfort analysis are carried out. The comfort and impact resistance of the whole vehicle under different working conditions are studied. It is found that: (1) the static and dynamic parameters of the single load wheel suspension system are within reasonable ranges; under the same road conditions and the increase of vehicle speed, the relative growth rates of the single load wheel suspension system body vertical acceleration root mean square and suspension dynamic deflection root mean square decrease; with the increase of road surface grade and vehicle speed, the body vertical acceleration transmissibility (8.5%~6.5%) shows a slight downward trend with smaller values; (2) when the whole vehicle passes through D, E grade road surfaces and extreme road surfaces, the proposed suspension system shows excellent comfort and impact resistance compared with the traditional torsion bar spring suspension system, which verifies the correctness and effectiveness of the suspension design scheme.

To deeply understand the impact load of offshore structures under extreme waves, this study investigates the temporal and spatial variation of wave impact load induced by focused waves on the rounded-square column based on model experiments. Wavelet analysis and locally weighted linear regression were utilized to extract the wave impact pressure from the wave load measurement data. Subsequently, the effects of wave period, wave steepness, focus position and wave incident angle on the distribution of the wave impact peak pressure and impulse were analyzed in detail. The results demonstrated that the upper part of the column experiences mainly short-duration, high-peak wave impact load, while near the still water level, it is mainly subjected to slowly changing quasi-static wave load. Additionally, increased wave steepness and period significantly enlarge the spatial range of wave impact pressure and impulse, and the impact load on the upper part of the column increases notably. It was found that the impact load is the largest when the focus position is set on the rear surface of the column.

The governing differential equations of beams have many engineering applications. This study proposes a method to identify the coefficients of the governing equation from sparsely measured displacement responses. First, the full-field displacement is reconstructed by the compressive sensing theory. The reconstructed displacement field is then fitted by B-spline surface basis functions, and the derived control points are further used to calculate other responses, which are derivatives of displacement. The genetic algorithm is finally utilized to seek the coefficients of the governing equation by substituting the fitted responses into the equation. The proposed method is both numerically and experimentally validated, together with a parametric study to evaluate its performance under various conditions. The salient advantages of the proposed method are that it requires only few sensors for measurement and the derivatives are calculated in a robust and easy way.

In order to address the issues of enormous numbers of parameters and long training time in traditional deep learning methods, a new structural damage identification method based on convolutional neural network (CNN) model with dual attention mechanism and improved Inception module is proposed. Firstly, the vibration response signals from engineering structures are firstly transformed into two-dimensional time-frequency spectrograms using local maximum synchrosqueezing transform, and then the obtained time-frequency spectrograms are used as inputs of CNN. Secondly, a two-dimensional CNN is established based on an improved Inception module. Finally, a dual attention mechanism is introduced to explore damage features with higher relevance, leading to a successful identification of structural damage location and severity. The effectiveness of the proposed method is validated by two numerical simulation cases of IASC-ASCE SHM Benchmark Phase I model and the Qatar University Grandstand Simulator dataset. The results demonstrate that the proposed method not only reduces model parameters and accelerates model convergence, but also has high accuracy and robust noise resistance in the context of multi-category damage identification on frame structures.

In response to the challenges posed by distributional differences between the source and target domains leading to negative transfer in transfer diagnostics, as well as the data privacy concerns stemming from excessive reliance on source domain samples, this paper proposes a Source-free Domain Adaptation (SFDA) method for transfer diagnostics. This method leverages neighborhood information to optimize pseudo-label supervised training, enabling transfer diagnostics in the absence of source domain samples.Initially, the data undergoes denoising using Singular Spectrum Decomposition (SSD) to enrich the samples with more abundant fault information. Then, a one-dimensional convolutional neural network is employed to construct a feature extractor for extracting domain-invariant features. Subsequently, a contrastive learning framework is applied to bring closer the features of samples from the same class, utilizing refined pseudo-labels derived from the aggregation of neighborhood information for self-supervised learning. Finally, an intelligent diagnostic model is deployed to accomplish the recognition of the health status of rolling bearings under varying operational conditions across different devices. The effectiveness of the proposed method is validated through cross-device transfer diagnostics between two rolling bearing datasets. Experimental results demonstrate that the proposed method effectively uncovers fault feature information across different devices, thereby enhancing transfer diagnostic accuracy under source-free and unsupervised cross-domain conditions.