基于 PSO-BP 神经网络湿式摩擦元件损伤预测模型

In order to solve the multi factor damage relationship of wet clutch, a wet friction element damage prediction model based on PSO-BP neural network was constructed by using multi-source data fusion method. Taking rotational speed and joint oil pressure as input parameters of the model, taking the extracted circumferential temperature gradient of friction plate, the change rate of Fe and Cu concentration and the change rate of friction plate surface roughness Ra as output parameters of the model, a finite element simulation model was established, and the comprehensive friction and wear test-bed of wet clutch was built. The effects of oil pressure and speed on the damage characteristic parameters of friction elements were studied by using the control variable method. The results show that the input condition takes on a nonlinear relationship with the four types of damage characteristic parameters, the variation trend of the predicted value and the measured value is consistent with the working condition, and the damage characteristic parameters are more sensitive than the change of oil pressure. Compared with similar models and test data, the prediction model can provide higher prediction accuracy and can effectively predict the multi condition damage of wet clutch.