Fault diagnosis of a machine tool rotary axis based on a motor current test and the ensemble empirical mode decomposition method

Abstract

Faults frequently occur in the rotary axis of machine tools due to their complicated drive mechanism. The machining accuracy and life time span will decline when there is a fault in the machine tool. Hence, it is necessary to develop a method to detect faults efficiently. In this article, a fault diagnosis method of the rotary axis of a computer numerical control (CNC) machine tool based on servo motor current analysis is proposed. A two-degree-of-freedom dynamic model of worm and worm gear is used in this study to analyse the relationship between the current signal and the fault of the rotary axis of the CNC machine tool. The ensemble empirical mode decomposition method is used as a self-adaptive low-pass filter to extract valuable information from the current signal. A number of experiments are carried out on a CNC machine tool which is in a fault state to validate the presented method. The result shows that the current of the servo motor contains enough information to detect the fault state of the feed axis of the machine tool. After being calculated by the dynamic model, the current signal clearly reveals the fluctuation of stiffness of the teeth which is caused by the fault of the rotary axis.