This paper focuses on the fault diagnosis for NC machine tools and puts forward a fault diagnosis method based on kernel principal component analysis (KPCA) andk-nearest neighbor (kNN). A data-dependent KPCA based on covariance matrix of sample data is designed to overcome the subjectivity in parameter selection of kernel function and is used to transform original high-dimensional data into low-dimensional manifold feature space with the intrinsic dimensionality. ThekNN method is modified to adapt the fault diagnosis of tools that can determine thresholds of multifault classes and is applied to detect potential faults. An experimental analysis in NC milling machine tools is developed; the testing result shows that the proposed method is outperforming compared to the other two methods in tool fault diagnosis.

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.