Fatigue Life Prediction Method of Remanufacturing Blank Based on Magnetic Memory Online Monitoring
It is of great engineering significance to online monitor and predict the fatigue life of in-service components.Based on the tension-tension fatigue test of 45 steel notched specimens,the magnetic signal variation of the notched position during the whole fatigue cycle was tracked and recorded in real time by using the metal magnetic memory online monitoring system.By applying Kalman filtering to the original monitoring signals,the results show that the whole fatigue process can be divided into three stages by the x-direction and y-direction magnetic signals,and y-direction magnetic signal is more sensitive to fatigue damage evolution.Furthermore,the parameters including standard deviation and kurtosis of y-direction magnetic field gradient are introduced as characteristic parameters,and their corresponding peak points can be used as the separating indicators for the first and second stages,as well as the second and third stages,respectively.Moreover,it was proposed that the peak value of the x-direction magnetic signal can be used to characterize the pre-warning information before the fracture of the specimen,and the mechanism underlying the variation of the magnetic signal was explored,which provides a reference for predicting the fatigue life of remanufacturingblanks.
metal magnetic memoryonline monitoringfatigue life predictionKalman filteringstandard deviationkurtosis
万方数据
维普
