Five-axis machine tool fault monitoring using volumetric errors fractal analysis

Abstract Detecting machine tool condition deterioration affecting its accuracy is a constant challenge for industrial machine maintenance. Machine tool volumetric errors (VEs) exhibit complex variations due, for example, to normal thermal variations, wear or faults and defective components. A monitoring technique based on the fractal analysis of VEs, estimated with the scale and master ball artefact method, is studied. Different fractal parameters from the VE vectors are compared with magnitude based quantities for the detection of abnormal machine states. Results using both actual data with real and pseudofaults and synthetic data from simulated faults using ISO230-1 error parameters are presented.