Liang et al. established three preset models for pitting at different point erosion degrees: mild pitting, moderate pitting and severe pitting, as shown in Fig. M2. An improved analytical method for analyzing the meshing stiffness based on the potential energy method was proposed by building the geometric defect equation of tooth surface under the pitting erosion characteristics, the related section equation and the inertia equation, which was used to predict and study the variation of the meshing stiffness under different erosion degrees. In order to further verify the reliability of the improved model, the three-dimensional approximate finite element analysis model was established and the boundary analysis conditions were set to realize the simulation calculation of torsional stiffness. The results of the two algorithms are compared with each other, and the results are basically consistent, which shows that the improved analytical model and the finite element model established have certain engineering application significance. Lei et al. established the position distribution of tooth surface pitting effect based on Gaussian distribution or random distribution function, and successively established the time-varying meshing stiffness of the irregular distribution of pitting effect, and established an approximate finite element model for comparative verification.
As early as in the 1990s, Tan Guanjun and Zhu Xiaolu put forward the application of grey prediction method and random time series analysis in fatigue pitting life prediction of gear. Cheng Peng studied the calculation method of the meshing stiffness under the pitting fault of the planetary gear teeth in the planetary reducer and the establishment of the fault dynamics model. Xiong Qi for parts invibration signals, such as mixed signal response, based on WPICA (wavelet decomposition), MEMD (multidimensional empirical mode decomposition), SDICA (narrowband independent component analysis) and the IMF (intrinsic mode function), and other advanced signal processing algorithm for matrix mutual information operation, complete the multi-channel data integration to the study of the characteristics of gear pitting failure frequency and failure sensitive order.
Feng wei applications such as test-bed is set up the default simulating pitting characteristics (looked a bit, and many more, etc.) at three test gear, iron spectrum analysis and vibration analysis technology was used to study vibration intensity and quantity of the scrap iron to reflect the degree of tooth surface wear and tooth surface corrosion damage degree, research reveals that the pitting and wear the more severe the greater the boundary the system vibration, the more intense. Pan Yuanfeng et al. also carried out artificial intervention preset simulation test of gear pitting, extracted vibration acceleration signals of relevant measuring points, and used wavelet analysis technology to discretize low-frequency and high-frequency envelope modulated signals of the signals to analyze vibration signal response of pitting. Based on Hertz contact theory, Meng Zhaoming et al. deduced the involute geometric equation and found that the maximum contact stress occurred at the boundary of the pinion’s single-double meshing boundary, and the pitting corrosion mainly occurred under the pinion’s pitch circle, while above the pinion’s pitch circle.