Real-time lifetime forecasting has extensive application in the fields of machine system manufacturing and integration, which is a good way to promote the dependability and operation stability. In this paper, a closed loop adaptive forecasting model with feedback channel of state monitoring information is built up for the real-time lifetime forecasting. The difference of working state between prediction and monitoring information is used to evaluate the prediction performance. The dynamic fuzzy neural network introduced into the prediction model, in which the fuzzy rule, membrane function and structure parameters can be adjusted according to the evaluate results. A service lifetime testing experiment of gear case is utilized to validate the prediction model. The proposed model achieved reasonable precision with an error of less than 1 hour between the failure time of experimental results and the forecasting remaining lifetime. The adaptive prediction method can deal with the real-time lifetime forecasting for multiple factors and nonlinear system without specific parameters structure.
Learning of closed-loop motion control
