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.

Aiming at the problem of the low fault diagnosis accuracy caused by the lack of fault samples for the rolling bearings of doubly fed wind turbines under normal conditions for a long time, an improved generative adversarial network fault diagnosis method based on expanding high-quality fault samples and using dual feature extraction was proposed. Firstly,a finite number of rolling bearing fault samples were expanded through a Wasserstein type generative adversarial network with maximum mean discrepancy and penalty constraints. Secondly, based on the dual feature extraction model, the time-frequency converted temporal features and local features were extracted separately. Finally, the fault diagnosis of the rolling bearing balance data was completed through a classifier. The standard dataset and test results show that the proposed method improves the fault diagnosis performance while lacking fault samples.

The gap between the rotor and the ball bearing in a wide temperature range increases with the increase in temperature. At the same time, the internal clearance of the bearing changes with the temperature. The inner ring of the bearing is subject to increased friction torque, which reduces its speed. This results in the deviation of the characteristic frequency of the outer ring defect, which is not conducive to the fault diagnosis of the ball bearing and the stable operation of the equipment. Considering the temperature-variable gap between the rotor and the bearing, a dynamic model of the bearing with an outer ring defect in a wide temperature range was established. The time-domain waveform and frequency of the model were analyzed. The simulation and test results show that the bearing vibration increases with the temperature, and the characteristic frequency of the outer ring defect decreases with the increasing temperature. Properly increasing the interference between the rotor and the bearing according to process requirements is beneficial for reducing the bearing system vibration in a wide temperature range and improving the accuracy of bearing defect frequency identification. The results provide reference for the use and health monitoring of ball bearings in a wide temperature range.

Considering the time-varying stiffness characteristics of mechanical systems, a single-degree-of-freedom time-varying impact vibration system model with clearance stiffness was studied. The dynamic model and Poincaré map were established, and numerical calculation methods were given. The influence of the ratio of time-varying stiffness amplitudes on the dynamic response and characteristics of the system was analyzed using numerical simulation and the maximum Lyapunov exponent. By combining multiple initial value bifurcation diagrams, attraction domains, phase diagrams, and Poincaré mapping diagrams, the evolution and bifurcation of coexisting attractors in the system were studied by applying the continuation shooting method. When the bifurcation parameter changes and the system exhibits the coexistence phenomenon,the reasons for the appearance and disappearance of local attractors and the distribution mechanism of unstable attractors in the attraction domain before and after bifurcation are revealed. The stability change rule of coexisting attractors is obtained.

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.

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.

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.

In order to achieve simulation prediction of the isolation performance of rubber isolators, simulation and testing comparative research on the vibration transmissibility of standard specimens were conducted. Firstly, a compression test was conducted on the standard specimen to obtain pressure deformation data. Based on the theory of the large deformation, the data was processed to obtain the true stress-strain curve. The Mooney-Rivlin model was chosen to define the material properties. Secondly, a vibration transmissibility testing system was established, an exciter to excite the standard sample was used, and the measured vibration transmissibility curve was obtained. Then, a simulation model based on the testing system was built, and the simulation transmissibility curve based on the transient dynamic analysis was obtained.Finally, the test results were compared with the simulation results to achieve the simulation prediction of the rubber isolation performance. The results show that the simulated isolation rate curve is highly consistent with the test results, with a peak frequency error of only 4.8%, which can achieve the simulation prediction and provide a simulation guidance for the optimization design and performance improvement of isolators.

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%.