WS2-C is produced from a hydrothermal reaction, in which WS2 nano-sheets are coated with carbon, using glucose as the carbon source. In order to investigate the tribological properties of WS2-C as a lubricant additive, WS2-C was modified by surfactant Span80, and friction tests were carried out on an MRS-10A four-ball friction and wear tester. The results show that Span80 can promote the dispersibility of WS2-C effectively in base oil. Adding an appropriate concentration of WS2-C can improve the anti-wear and anti-friction performance of the base oil. The friction coefficient reached its lowest point upon adding 0.1 wt % WS2-C, reduced by 16.7% compared to the base oil. Meanwhile, the wear scar diameter reached its minimum with 0.15 wt % WS2, decreasing by 26.45%. Moreover, at this concentration, the depth and width of the groove and the surface roughness on the wear scar achieved their minimum. It is concluded that WS2-C dispersed in oil could enter friction pairs to avoid their direct contact, thereby effectively reducing friction and wear. At the same time, WS2-C reacts with the friction matrix material to form a protective film, composed of C, Fe2O3, FeSO4, WO3, and WS2, repairing the worn surface.
Wavelet methods are widely used in mechanical transient vibration signature detection and fault diagnosis. Undesirable artifacts (e.g., spurious noise spikes and pseudo-Gibbs components) are serious issues for the mainstream methods, probably resulting in inaccurate analysis results. For this reason, a new wavelet sparsity enhancement methodology is proposed to achieve artifact-free extraction of bearing transient vibration signatures. The wavelet sparsity is enhanced through two aspects—that is, wavelet basis design and wavelet coefficient processing. Specifically, we use a new family of wavelets called fractional B-spline wavelets on bearing fault signal analysis and propose the adaptive undecimated fractional spline wavelet transform which addresses the shift-invariant problem and also permits to customize an optimal wavelet basis according to the signal itself adaptively. Meanwhile, we introduce a two-step wavelet processing method including a nonlinear operator and a generalized hard-thresholding (with symmetric or asymmetric thresholds determined automatically by wavelet coefficients at each level). Moreover, unlike the most existing wavelet methods, the approximation coefficients are also processed along with the detailed coefficients to remove the possible low-frequency noise. The final transient vibration signatures are reconstructed with the processed coefficients and would be sparser, more accurate, and almost free of artifacts. The validity of the methodology is verified with the analysis results of vibration signals measured from fault-injection experiment and industrial wind turbine transmission system. The comparisons highlight the advantages of the methodology over several common methods in suppressing artifacts and extracting the sparse transient vibration signatures of bearing structural damages.
The microscopic topography of tooth surface affects the nonlinear dynamic characteristics of the gear system. However, few studies have fully taken into account the effects of microscopic topography on time-varying meshing stiffness (TVMS) and backlash in gear dynamics. In this context, this study derives TVMS and timevarying backlash with fractal characteristics based on fractal theory, and introduced them into a 6-DOF nonlinear dynamic model. With various nonlinear dynamics analysis tools, the dynamic characteristics of the gear system under different fractal parameters are investigated. The results indicate that the increase of the fractal dimension or the decrease of the characteristic scale coefficient leads to a smoother tooth surface, larger TVMS, and smaller amplitude of backlash. The effect of fractal dimension is more sensitive than characteristic scale coefficient. Furthermore, in the low-speed region, the increase of fractal dimension has a positive effect on the dynamic response of the system, and can reduce the amplitude of transmission error. In the high-speed region, the opposite is true. It is worth pointing out that the influence of fractal dimension on gear dynamic characteristics is nonlinear. Considering the machining cost and dynamic response of gear, the fractal dimension of 1.5 is the best choice. The influence of characteristic scale coefficient on system dynamics is similar to that of fractal dimension but weak.
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