From the engineering point of view, through the establishment of mathematical models and contact fatigue test platform under different physical characteristics of gears, the geometric characteristics, meshing stiffness and dynamic response characteristics of the system without pitting and with pitting are studied in detail. Although the dynamic response characteristics of the system are studied from multiple perspectives and multi parameter characteristics, due to the limited knowledge level and time, there are still many deficiencies to be discussed and improved. It is summarized as follows
(1) In order to improve the calculation accuracy of gear meshing stiffness, only regular rectangle is used to simulate the pitting pits on the tooth surface, and advanced irregular curve shape is needed to simulate the pitting characteristics of the tooth surface, so as to improve the calculation accuracy of the gear meshing stiffness under the pitting characteristics; the tooth surface roughness fractal model used in this paper only studies the contact characteristics of the tooth surface, and further research on the influence of the tooth surface geometry on the tooth stiffness is needed And the research of comprehensive meshing stiffness can more truly predict and evaluate the response characteristics of gear meshing stiffness under different machining processes.
(2) The dynamic simulation models all adopt the rigid simplification of components, and the large-scale system coupling calculation and analysis of rigid flexible coupling or pure elastic components still need to be further carried out, especially considering the fine characteristics of tooth surface pitting.
(3) Due to the limited measurement accuracy and location of gear measurement center, the measurement data reflect the evolution process of fatigue pitting from the macro effect of tooth surface. It is still necessary to use high-precision three-dimensional scanning measuring instrument to measure and evaluate the geometric characteristics of tooth surface, and combine the micro and macro scale to show and deduce the forming process of fatigue pitting. In this paper, only the basic statistical indicators are used to compare the process vibration signal values and describe the pitting response characteristics of the collected vibration signals. In the later stage, the advanced signal processing algorithm is still needed to carry out the time domain or frequency domain research analysis and signal decomposition of the collected signals, and the unbalanced response characteristics of the system in the process of pitting characteristics are discussed in detail, so as to provide reference for the engineering realization of theFault diagnosis and identification to pave the way for preliminary basic research.