An online damage identification approach for numerical control machine tools based on data fusion using vibration signals

In this paper, a new approach is proposed based on data fusing with vibration signals using time-frequency parameters, probabilistic principal component analysis (PPCA) and statistical inference, for improving the accuracy and visibility of damage identification for numerical control (NC) machine tools. Time-frequency feature principal components are put forward, which extracted from eight dimensionless parameters statistically in the time and frequency domains by PPCA. The Chi-2 statistic is established according to statistical inference principle, and the feature figure of principal components is built that can acquire damage distribution of tools by measured data. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy and visibility of the proposed approach.