Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.

The dynamic of human body with three-dimensional (3D) anatomical structure is not considered in the vehicle system dynamics. Moreover, the dynamic of the lumbar spine is not studied in the finite element (FE) model of the seated human, and the influence of external conditions on it is not considered. The aim of the study is to propose a comprehensive dynamic model and an analysis method to study the dynamic of the lumbar spine in a complex system. It is investigated by extracting the first six modals of different lumbar spine FE models (eight models), and the dynamic responses of the lumbar spine under white noise excitation are analyzed by random response method. It is found that the mode deformation and the resonant frequencies of the lumbar spine in vivo and vitro model are significantly different, and the peak response value of the lumbar spine is at the resonant frequency. It is concluded that the influence of vibration on the lumbar spine could be more comprehensively and visually studied by analyzing the displacement response power spectrum of FE model of the seated human body. This study provides a new method and reference for the subsequent study of the vibration behavior of the lumbar spine in complex system (especially in vehicle system dynamics), and the finding is helpful for us to find more and better protection methods of human body.

This study aimed to explore the wear characteristics and evolution mechanisms of large-scale wind power gears under the impact load of particles of the three-body abrasive Al₂O₃ (0.2 mg/mL) from four aspects: oil analysis, vibration analysis, amount of gear wear, and tooth-surface-wear profile analysis. A magnetic powder brake was used to simulate the actual working conditions. Combined with the abrasive particle monitoring and the morphology analysis of the tooth-surface-wear scar, by setting quantitative hard particles in the lubricating oil, the gears are mainly operated in the abrasive wear state, and wear monitoring and wear degree analysis are carried out for the whole life cycle of the gears. Oil samples were observed and qualitatively analyzed using a particle counter, a single ferrograph, a metallographic microscope, and a scanning electron microscope. The experiments demonstrate that the initial hard particles have a greater impact in the early wear stage of the gears (<20 h), and abrasive particle concentration increases by 30%. This means that Al₂O₃ particles accelerate the gear wear during the running-in period. The loading method of the impact load on the oblique gear exacerbates the abrasion particle wear and expands the stress concentration, which reduces the surface of large milling particles on the surface, and reduces the width of the tooth (the part above the pitch line is severely worn), which causes the gear to break into failure. The research provides help for analyzing the mechanism of abrasive wear of gears and predicting wear life.

Spindle bearing with high rotational speed is the kernel of many rotating machines which plays massive important role in the performance of the whole machine. There are also some deficiencies of the present quasi‐dynamic mathematical models of the rolling bearing in analyzing the bearing dynamic parameters. This paper develops the 3D quasi‐dynamic model with ball’s 6‐DOF, cage’s 6‐DOF, and driving ring’s 5‐DOF under steady working stage, based on the analysis of the relative position of each component to determine the normal forces between each component and the analysis of the relative velocity of the contacting point to determine the tangential force between each component. This model can analyze the dynamic parameters, such as the spin‐to‐roll ratio of inner and outer rings, the angular velocity of rolling elements in y‐axis of the location coordinate, the normal contact force of cage pocket and ball, the cage angular velocity and ball revolution velocity, and the gyroscopic moment under different working condition. Compared with the numerical calculation results of ADORE program, the results of the proposed model are able to accurately describe the change laws of the spindle bearing parameters.