To enhance the energy absorption efficiency of conventional sandwich panels, a biomimetic tree-like fractal core (BTLFC) inspired by the dendritic fractal structure of the royal lotus leaf vein was designed. Firstly, quasi-static compression tests revealed that the 2-order BTLFC exhibited a specific energy absorption 5.69% higher and an average load 4.46% greater than traditional honeycomb cores. Secondly, a finite element numerical model of the BTLFC was established;combined with quasi-static compression test data, the finite element model error was within 2.2%, demonstrating high accuracy of the model. Finally, Latin hypercube test design, Kriging surrogate model, and the non-dominated sorting genetic algorithm-II (NSGA-II) were employed to perform multi-objective optimization on the structural parameter combinations of the BTLFC (size ratio r, bifurcation angle θ, fractal order D). The optimized BTLFC structure exhibited superior comprehensive performance, with specific energy absorption increased by 10.19%, peak crushing force reduced by 12.27%,and mass decreased by 11.79% compared to traditional honeycomb cores. The findings provide novel biomimetic design insights for developing high-performance energy absorption structures.

Rolling bearing looseness faults are likely to induce transmission system fault. Considering the factors such as nonlinear contact force of rolling bearings, rub-impact force, damping force between outer ring and housing, a six-degree-of-freedom nonlinear dynamics model was established under the fault of rolling bearing outer ring looseness, and the vibration characteristics of rolling bearing outer ring looseness were analyzed. The simulation results show that the characteristic frequency of the outer ring looseness fault is presented as the rotational frequency of the rotating shaft and its multiple components, and the actual tested wind power bearing ring fault data verifies the accuracy of the model. The results of this paper show that the rolling bearing outer ring looseness is caused by loosing between the outer ring and the housing, and the cyclic impact and friction between the outer ring and housing are formed under the unbalance force with the rotational speed of the shaft and its harmonics. The research results provide a theoretical basis for realizing the mechanism analysis of rolling bearing outer ring looseness and fault diagnosis of the rolling bearing.

A fault diagnosis method based on improved dung beetle optimizer (IDBO)-time varying filtered empirical mode decomposition (TVFEMD) with improved wavelet threshold functions was proposed aiming at that the vibration signal of rolling bearing fault tends to be disturbed and overwhelmed by strong noise background. IDBO was primarily developed to iteratively optimize B-spline order and bandwidth threshold ξ in TVFEMD，and the optimal parameter combination was obtained. Applying TVFEMD on the original signal, the decomposition for intrinsic mode function (IMF) component series were achieved, among which the irrelevant components were removed by correlation coefficient criterion, and target signals were reconstructed. Then the improved wavelet threshold function was employed on the new signal for further denoising.Finally, the envelope spectrum of the processed signal was calculated, from which the typical fault characteristic frequency was extracted. Through simulation signal and fault simulation test analysis, the fault diagnosis method combined with IDBO-TVFEMD and improved wavelet threshold function was compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN) denoising methods. The research results show that the algorithm model proposed in this paper has higher efficiency.

A set of anti-impact protection device was designed for the test fracture accident of 30 MN tension sensor calibration device. Firstly, based on the kinematic theory, the kinematic model of each component in the fracture process of test fixture was established. Then, different protective device structures at three impact locations were designed. Finally, the finite element models of three buffer structures were established and verified, calculated and optimized. The results show that the egg-box structure protection device at the top plate of the upper reaction rack and the upper ball joints can effectively solve the problems of small protection space and large impact value. The whole device dissipates 59.5% impact kinetic energy of the upper reaction rack, 60.7% impact kinetic energy of the lower reaction rack and 100% impact kinetic energy of the lower ball joints. After the improvement, the initial peak load of the protective device at the lower ball joints is reduced by 62.7%.

To evaluate the performance of various equivalent stress intensity factor models in predicting mixed-mode fatigue crack growth and to address the challenge of parameter estimation under limited sample conditions. A crack growth parameter estimation method based on the Bootstrap method resampling technique was proposed firstly. Mode Ⅰ fatigue crack growth tests were conducted on CT specimens to obtain the material parameters, and the proposed method was employed to expand the sample set and mitigate the issue of data scarcity. Then, using the statistically augmented material parameters,mixed-mode Ⅰ+Ⅱ fatigue crack growth experiments were performed on 6005A-T6 aluminum alloy CTS specimens under loading angles of 0°, 30°, 45° and 60°, employing a Richard-type loading fixture, to validate the accuracy of various equivalent stress intensity factor models. The results indicate that the Irwin model achieved the highest goodness-of-fit, with a value of 0.942 1, demonstrating the best crack growth prediction performance. Increasing the loading angle was found to reduce the initial crack growth rate, highlighting the need for angle-specific experiments to obtain appropriate Paris law parameters. This study confirms the applicability of multiple ΔK_eq models and provides theoretical support for fatigue life prediction in mixed-mode crack growth scenarios.

In view of the problem that the formulary design load based on the design standard can't truly reflect the actual service conditions of the welded frames of subway vehicles, and a large number of online measured data information has not been fully explored in the structural design of the frame, an anti-fatigue design spectrum compilation method based on the stress-time history sample information measured at the weak position of the subway vehicle frame was proposed.The small stress threshold value was determined by clustering ordered samples using the rain-flow counting method to compile measured stress spectrum reflecting structural damage, determining the stress-frequency-damage relation based on the fatigue damage theory, and using the methods of Bayesian parameter estimation and kernel density estimation to obtain stress extrapolation results. Considering the stress concentration caused by structural geometry changes in the cross-section of the welding site, the hot spot stress method and stress linearization method were used to obtain the stress concentration coefficient, and the measured stress spectrum was corrected to achieve the compilation of the design load spectrum. The research results show that the small stress threshold value is 3.18 MPa determined by extrapolation of stress extremes and the relation analysis between normalized stress-frequency-damage, which is 8.19% higher than the stress threshold value determined by traditional methods, the effect of discarding the number of small stress cycles is significant. Considering the load dispersion and stress concentration factor at the weld seam, while ensuring that the structure meets the current service conditions, the relation between the design mileage and equivalent stress is determined, the necessity of compiling the design spectrum is emphasized further. The above research builds the construction method of load spectrum compilation and the equal strength design of structures.

To enhance the computational efficiency of structural lightweight design for complex structures, a structural lightweight design method based on the Kriging surrogate model is proposed. The proposed method incorporates a hybrid addition strategy and a sample deletion strategy considering a distance threshold，aiming to rapidly improve the fitting accuracy of the Kriging surrogate model. This model was then applied to a multi-objective lightweight design model of the truck frame, with the optimization objectives of minimizing frame mass and maximum stress. Subsequently, the multi-objective lightweight model was solved using the non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed hybrid addition strategy and sample deletion strategy considering the distance threshold effectively enhance the update process of the Kriging surrogate model. The structural lightweight design method based on the Kriging surrogate model exhibits significant advantages in both computational efficiency and lightweight performance.

Abnormal yaw positioning during yaw operations induces progressive deviation in yaw alignment accuracy,thereby compromising wind-tracking precision and risking excessive cable twisting that threatens operational safety.Concurrently, frequent position oscillations or repetitive short-duration position holding generate transient control errors,destabilizing the yaw control system. These coupled mechanisms collectively escalate yaw system failure frequency and operational maintenance costs. To proactively mitigate these risks, a data-driven fault diagnosis methodology is proposed for early detection of anomalous yaw positioning in wind turbines. Firstly, a large amount of data in a supervisory control and data acquisition (SCADA) system was processed using a standardized interaction gain and Relief-F (SIG-Relief-F) feature selection algorithm to identify multiple feature parameters with the strongest correlation with the target variable (which in this case may be yaw system failure). The advantage of this method lied in its ability to consider effectively the correlation between features,thus maximizing the retention of relevant features related to yaw system failures and interaction features. Secondly, a back propagation neural network (BPNN) yaw position prediction model was established, and the distribution of residuals was statistically analyzed using a sliding window method to determine the fault threshold. Finally, through empirical verification,the effectiveness and accuracy of the proposed method were demonstrated, and compared with multivariate state estimation technique (MSET) and support vector machine (SVM) algorithms, it was shown to have superior abnormal warning performance. The conclusions drawn can serve as a reference for the fault diagnosis of a practical yaw system.

The six-component forces at the wheel-road interaction represent the sole coupling between the vehicle and the road surface, and obtaining these forces is critical for conducting reliability and durability assessments of the entire vehicle. In response to the high cost, long cycle, and low efficiency associated with traditional methods for obtaining wheel six-component forces, a data-driven approach for rapidly predicting wheel loads was proposed. Firstly, for the non-stationary random signals on real vehicle roads, a joint method of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), and wavelet threshold denoising (WTD) was applied for the data denoising.Secondly, the easily obtainable and low-cost data, such as wheel center acceleration, damper displacement, and center of mass acceleration, were used as inputs. Various neural network architectures with nonlinear transfer relationships were designed for multi-surface wheel six-component force prediction. A multi-dimensional load prediction evaluation system was established in the time domain, frequency domain, and damage domain. Finally, in order to overcome the challenges of a large and costly training dataset, an input channel compression method based on the correlation and coherence analysis of neural network inputs and outputs was proposed. Minimum load signal segment division criteria were introduced, and the minimum segment duration for each road surface was determined to compress the training dataset. Through continuous model iterations, the predicted values of the wheel six-component forces closely match the measured values, and the load characteristics are preserved. This demonstrates that the minimal dataset model can achieve a high level of prediction accuracy with fewer input channels and shorter load segment durations, resulting in a 28.85% improvement in computational efficiency.

Pipelines are frequently connected to power equipment such as compressors and pumps, serving critical functions including material transport and pressure transmission, thereby constituting the “highways” for material transfer in industrial production. Prolonged excessive vibration is the fundamental cause of structural fatigue damage in pipelines,detachment of instruments mounted on pipelines, and desensitization of auxiliary components. Research on pipeline vibration,noise, and their control technologies is a fundamental prerequisite for meeting industrial production requirements. Due to their significant damping effects, high reliability, and ease of installation, particle dampers are commonly employed for vibration control in industrial pipelines. However, the damping mechanisms and configuration methods of particle damping materials remain incomplete, resulting in difficulties in predicting their vibration attenuation performance. Firstly, a theoretical calculation method was developed for particle dampers used in L-shaped industrial pipelines, and the energy dissipation mechanisms of particles were analyzed under two states: “equivalent solid” and “equivalent fluid”. Then, based on variations in vibration intensity at damper installation locations, a theoretical calculation approach for particle dampers was proposed.The results indicate that under small vibration conditions without slip flow, the energy dissipation by particles can be equivalently represented by impulsive collision forces between particles and the pipeline as well as frictional energy loss;under large vibration conditions, slip flow occurs among particles exhibiting viscous damping effects. Both theoretical analysis and test results demonstrate that when particle dampers operate within an environment characterized by a reduced acceleration Γ≤3.8, collision-based damping models are appropriate to characterize their dissipative performance; conversely,when operating under reduced acceleration conditions Γ>3.8, multiphase flow frameworks should be employed to predict the vibration attenuation efficacy of particle dampers.