On-line backlash-based feed-axis wear condition monitoring technology

Wear is an important cause of the instability for a CNC machine tool servo drive system. And the backlash is one of the results of the feed-axis wear. Therefore, a novel kind of online feed-axis wear condition monitoring methods is proposed, which uses the backlash for estimating the status of the wear. Firstly, the on-line sensorless and rapid estimating principles of the feed-axis backlash are profoundly investigated. And the measuring method of translational and rotary axis backlash is respectively suggested. Secondly, to estimating the wear comprehensively, the testing route planning algorithms with circular-test and triangle-curve-test array are studied. Finally, the mathematical models have been established between the feed-axis wear condition and the backlash. The results of the experiment show it is feasible and effective to use the backlash for estimating feed-axis wear condition, which lays the foundation for its on-line condition monitoring and fault diagnoses.