The paper attempts to establish a failure prediction method for machine tools.
With respect to these problems of failure prediction for machine tools, by analyzing the dimensional accuracy of the machined parts of the main shaft bearing to predict the bearing replacement period, from the perspective of an inverse problem, considering the influence of bearing type and other factors, based on data mining technology and prediction methods, we propose a grey periodic extension model combined with multi-scale graph correlation (MGC) to screen out the dimensional accuracy types closely related to the bearing failure and predict and analyze the change trend of the part accuracy.
The results show that the proposed model can help predict the change in dimensional accuracy of parts by building a combinatorial model and the bearing replacement time and has stronger universality compared with judging the condition of bearings based on their own data changes.
From the perspective of inverse problems, based on data changes, the proposed approach can well deal with failure prediction for machine tools.
