Operating condition information-based reliability prediction of cutting tool

For the reliability prediction to equipment, such as machine tools, whose failure is mainly due to degradation, a forecasting method based on operating condition information is proposed, which includes extraction of condition characteristic index, computation of instantaneous reliability, construction and application of the neural network prediction model. The key issue of prediction accuracy is the computation of instantaneous reliability. A novel approach incorporating Bayes theorem and Kaplan-Meier (KM) estimator principle is employed to calculate the instantaneous reliability. According to the time-varying data of the tool wear, the trained network is available for forecasting the accurate failure time judged by the criterion of reliability. The results show the feasibility and effectiveness to predict reliability by operating condition information.