Fuzzy Reasoning based Reliability Fault Prediction of CNC Machine Tools

Abstract

CNC machine tools are the infrastructure of the manufacturing industry, and many fields cannot do without them. This paper studies the fault data of a series of CNC machine tools, and predicts the fault level based on the activity parameters of Gutenberg Richter curve and fuzzy information theory. Apply the Gutenberg Richter curve model to the reliability analysis of CNC machine tools, and use this model to fit the curves separately. Fit the activity parameters of each stage with curves, and the results show that the b value can reflect the fault activity frequency of CNC machine tools. Due to the correlation and fuzziness between system faults, it is more appropriate to use a fuzzy neural network with strong adaptability and good learning ability, which can easily adjust parameters, and can express a more complex, high-dimensional nonlinear system through fewer conditions. The use of fuzzy reasoning can link the nonlinear relationship between fault level, b-value, and N-value. Analyze the error between the predicted fault level and the original level, and the small error indicates that the model has good predictive ability. Applying this predictive ability to the reliability research of CNC machine tools will yield good results