Transmission Error Prediction And Rating Method Of Multi-gear Transmission Planetary Gear Train

Planetary gear train is the main transmission structure of automatic transmission,which has the advantages of high transmission efficiency and smooth transmission,and is widely used in high-grade passenger cars.However,the transmission error between gears will cause vibration among multiple planetary gear trains.To solve this problem,the transmission error prediction and rating of 9-gear automatic transmission were studied.The transmission error of the planetary gear train was indirectly considered from the Angle of bent-torsional shaft vibration of the multi-gear planetary gear train.The fusion characteristics of vibration signals were analyzed,and the prediction model and vibration recognition model of the first-and second-order planetary gear train of the 9-gear automatic transmission were established to complete the transmission error evaluation.Firstly,the error sources and influencing factors in the process of gear transmission were analyzed to obtain the error model in the process of gear meshing transmission.Then,the dynamics equation of multi-gear planetary gear train was established to analyze the relationship between the transmission error and the force of the planetary gear train.Based on the finite element method,the modal analysis of the 9-gear automatic transmission planetary gear train was carried out.Firstly,the error sources and influencing factors in the process of gear transmission were analyzed to obtain the error model in the process of gear meshing transmission.Then,the dynamics equation of multi-gear planetary gear train was established to analyze the relationship between the transmission error and the force of the planetary gear train.Based on the finite element method,the modal analysis of the 9-gear automatic transmission planetary gear train was carried out.Secondly,the flexural-torsional-axis vibration data set of planetary gear train was established to analyze the spectrum diagram of vibration signals,and carry out feature extraction,complete the optimization of feature parameters,and determine the representative feature parameters.Aiming at the complex multi-source signals and multi-feature information of 9-speed automatic transmission,a feature fusion model based on the combination of quantum genetic algorithm and self-attention mechanism is established.Then,the bent-torsional shaft vibration prediction model based on the improved support vector regression was established.Based on the single step prediction,the vibration trend caused by the transmission error was predicted.A recognition algorithm supporting tensor machine based on quantum particle swarm optimization algorithm was established.The fused characteristic parameters were identified and classified,and the training time,recognition rate and accuracy of vibration signals under different algorithms were compared.Finally,the test bench of planetary gear system gearbox was built to obtain the vibration acceleration signals of the gearbox in different directions,verify the rationality of the selection of characteristic parameters,predict the accuracy of the results and the accuracy of fusion classification,analyze the influence of the experimental results of the gear transmission error on the vibration,and put forward the measures to reduce the transmission error.