Due to the variable working conditions, there are compound defects in the gear shaft-bearing system easily, vibration signals are very complex, and the fault diagnosis of the system becomes more difficult. Thus, a 36 degrees of freedom (36-DOFs) dynamic model is established for discussing the vibration characteristics of the gear shaft-bearing system, the gear pair spalling defect is considered, there are localized defects on the inner raceway and outer raceway of the supporting bearing, the work conditions contain variable speed, variable load, speed fluctuation, and load fluctuation. The obtained vibration signal is processed by the short-time Fourier transform for the time–frequency distribution map. When the gear shaft-bearing system with compound defects operates under variable conditions, roller passing outer raceway frequency, roller passing inner raceway frequency, gearing meshing frequency, and the relative harmonic frequencies can also be found. The defect frequencies and frequency amplitude are increasing with the speed while the system makes the accelerated movement. While the load acting on the system increases, the defect frequencies remain unchanged, but the frequency amplitude becomes larger. If there are fluctuations of the speed and load, the apparent defect frequency fluctuation and amplitude fluctuation is generated. The mathematical model and the analysis results are verified by the experiment, which will provide the theoretical basis for the fault diagnosis of the gear shaft-bearing system.
In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.
Due to their excellent stability and zero leakage capability, thrust bearings with herringbone spiral grooves are frequently used in transmission mechanisms. However, the lubrication mechanism of thrust bearings has not been clearly understood and explained, preventing the optimization of the bearing performance. Thus, this paper is devoted to solving this problem by building a three-dimensional finite element flow model. In this model, the change in viscosity temperature is considered using Roelands equation, and the turbulence and cavitation are taken into consideration. Using the established model, the influence of parameters such as spiral angle, groove width ratio, and rotational speed on the cavitation area of the thrust bearing are analyzed. The pressure contour and speed distribution are obtained inside the clearance, as well as the volume fraction of the gas phase at the end face. Finally, according to the analysis results, the optimum structural parameter for the herringbone spiral groove structure is proposed, which enables higher bearing stability and provides a reference for engineering practice.
As an important component, the gear pair is frequently used in the mechanical transmission. If there is a localized spalling defect on the gear pair, the double shock phenomenon in the vibration response can be observed. In order to reveal the mechanism, an 8DOFs dynamic model for the gear pair is established, the symmetric and asymmetric rectangular spalling defects are considered, the torsional deformation is introduced. Meanwhile, the change law of the double shock during the defect expansion is explored. From the results, the double shock is caused by multiple changes in meshing stiffness, the amplitude and distribution range are closely related to the defect sizes. When the localized defect is extended along the length and depth directions, the amplitude of the double shock is significantly increased. If the defect area is asymmetric along the center plane, the torsional deformation of the gear pair will be generated. Then, the meshing stiffness is reduced, and the double shock becomes more obvious. Finally, through the finite element method and the experiment, the established model is confirmed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.