Research On Gear Fatigue Life Prediction Based On Finite Element And Oil Analysis

Gear as an indispensable transmission part in modern mechanical equipment.its transmission accuracy directly affects the work efficiency of the equipment.In the actual operation process of the equipment,the wear and failure of the transmission gear are caused by the influence of friction.The prediction of gear wear life can know the operation condition of the equipment well,make the maintenance and repair work of the equipment well,avoid the occurrence of major accidents,and timely replace the parts,improve the operation status of the equipment and improve the work efficiency.In the design process of transmission gear,the use of finite element analysis technology can achieve the prediction of transmission gear fatigue life,but in the actual operation process,there are several factor influence prediction accuracy,such as working environment,working load,lubrication environment and so on.As the lubricating medium of transmission gears,lubricating oil can not only reduce the friction between transmission gears,but also judge the wear degree and running state of transmission gears according to the detected oil information.Based on the change law of oil detection information,the relevant detection information can be analyzed to realize the prediction of gear fatigue life.In order to better realize the prediction of gear fatigue life,this paper selects the method of combining finite element and oil analysis technology to carry out the prediction of gear fatigue life.The specific research contents are as follows:(1)The fatigue life of gear is studied by simulation and experiment.Based on the tooth surface strength calculation formula and life calculation formula to determined the load and speed of the gear,and the simulation and test under this working condition.Through the simulation and experimental comparison of two kinds of meshing width,it can be seen that the error between ANSYS workbench static analysis results and theoretical calculation results is less than 5%,and the error between ANSYS ncode life analysis results and experimental results is less than 5%.(2)The viscosity,acid value,particle size,PQ value and Ferrography of the lubricating oil in the process of operation are analyzed by means of oil analysis.Considering the influence factors in the detection process,to study the correction and elimination of the oil detection data.According to the final detection data to evaluated the lubrication environment and operation state of the transmission gear.On this basis,to study the change of oil index of two kinds of meshing width gears under the same working condition.According to the change of the oil index to analysis the causes of the phenomenon,and the prediction index of the gear wear is determined according to the analysis index.(3)BP neural network prediction model is used to predict gear wear,and on this basis,to study the GA-BP neural network and FPA-BP neural network prediction model.Through compared with the prediction accuracy and prediction effect of the three prediction models.It is known that the prediction accuracy of BP neural network is 95.05%,the prediction accuracy of GA-BP neural network is 95.81%,and the prediction accuracy of FPA-BP neural network is 96.60%.(4)Based on the existing prediction model,study the designed of combination algorithm,and an optimized BP neural network prediction model based on GA-FPA combination algorithm is proposed.The prediction model of GA-FPA-BP neural network was verified by using the selected prediction indexes,and the prediction accuracy reaches 97.19%,which indicates that the combination algorithm can better realize the prediction of gear fatigue life.