In this paper, the formulas of elastohydrodynamic traction coefficients of four Chinese aviation lubricating oils, namely, 4109, 4106, 4050, and 4010, were obtained by a great number of elastohydrodynamic traction tests. The nonlinear dynamics differential equations of high-speed cylindrical roller bearing were built on the basis of dynamic theory of rolling bearings and solved by Hilber–Hughes–Taylor (HHT) integer algorithm with variable step. The influence of lubricant traction coefficient on cage's nonlinear dynamic behavior was investigated, and Poincaré map was used to analyze the influence of four types of aviation lubricating oils on the nonlinear dynamic response of cage's mass center. The period of nonlinear dynamic response of cage's mass center was used to assess cage's stability. The results of this paper provide the theoretical basis for selection of aviation lubricating oil.

In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

Due to the coupling of the damper journal with the elastic ring and oil film, the elastic ring squeeze film damper (ERSFD) shows better dynamic performance in comparison with the traditional squeeze film damper (SFD). Therefore, a novel rigid–elastic–oil coupled mathematical model was established. The elastic ring deformation, as the key point, is solved according to the planar bending theory. Then, based on the pressure governing equation of the oil film, using the central finite difference method, the oil film pressure was addressed. Meanwhile, the Simpson method was implemented to calculate the dynamic characteristic coefficients (equivalent stiffness $Ke$ and damping $Ce$) of ERSFD (DCCEs). Also, we analyzed the influence of journal eccentricity, oil film radius clearance, flexibility coefficient and damping hole diameter on the DCCEs, and the results were compared and verified with the existing literature. The sensitivity of each parameter to the DCCEs was analyzed by using the linear regression method. According to the results, the flexibility coefficient has the greatest effect on the DCCEs, followed by the oil film radius clearance. The eccentricity of the journal and damping hole diameter have the least impact. This work will provide a theoretical basis for reflecting on the bearing dynamic characteristics more truly and accurately.

Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS.

Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within $\pm 2X_{\mathit{rms}}$ are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.