Intelligent Monitoring System for Screw Life Evaluation

The effects of rotating speed and load on screw life were investigated to study the performance degradation of screws of NC (numerical control) machine tools under different machining conditions. The real time data of vibration and cutting forces were collected. The key effects on the screw life were identified by analyses on time and frequency domains and wavelet analysis following filtering of the collected data with an empirical mode decomposition. A screw life prediction model was proposed using a multi-model fusion and a B-spline fuzzy neural network. Experimental results show that the maximal error of life prediction is 846 h, and the proposed system meet the need of active maintenance of screw.