Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a high-order complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brand-new state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.

By considering the effects of multiple grooves, rubber deformation, and actual boundary conditions, the steady elastohydrodynamic lubrication mathematical model and the dynamic Reynolds equation of water-lubricated rubber bearings are presented. The Reynolds equation is solved numerically by the finite difference method, and the stiffness and damping coefficients are calculated from the real and imaginary parts of the integrated pressure. Analysis results suggest that angular velocity, eccentricity ratio, groove configuration, and radial clearance have significant effects on the steady state and dynamic stiffness as well as damping characteristics, which also help in designing proper structure parameters of water-lubricated rubber bearings to obtain better dynamic performance.

Suppressing vibrations and noises is essential for our automated society. Here, inspired by the hierarchical dynamic bonds and phase separation of mussel byssal threads, we synthesize high-damping supramolecular elastomers (HDEs) via simple onepot radical polymerization of butyl acrylate (BA), acrylic acid (AA), and vinylimidazole (VI). Interestingly, AA and VI not only form hydrogen bonds and ionic bonds simultaneously but also segregate into aggregates of different sizes, thereby successfully mimicking the hierarchical structure of mussel byssal threads. When applying external forces, the weak hydrogen bonds are broken at first and then the ionic bonds and aggregates are disrupted progressively from small to large deformations. Such multiple energy-dissipation mechanisms lead to the outstanding damping property of the HDEs. Therefore, the HDEs outperform commercially available rubbers in terms of sound absorption and vibration damping. Furthermore, the multiple energy-dissipation mechanisms impart the HDEs with high toughness (41.1 MJ/m 3 ), tensile strength (21.3 MPa), and self-healing ability.

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