Statistical approaches for semi-supervised anomaly detection in machining

Abstract

Numerous methods have been developed to detect process anomalies during machining. Statistical approaches for semi-supervised anomaly detection compute decision boundaries using information of normal running processes for process evaluation. In this paper, two statistical approaches for semi-supervised anomaly detection in machining based on envelopes are presented and compared. The proposed parametric approach assumes normal distributed envelopes to compute decision boundaries. However, experiments show that deviations from a normal distribution can reduce the monitoring quality. The new approach is non-parametric and employs kernel density estimation (KDE) to estimate the probability density function of the envelopes. Both approaches were evaluated for several machining processes. It is found that the parametric approach is robust against high scattering processes and yields low false alarm rates. By means of the selected safety factor, the number of detected anomalies can be increased using the non-parametric approach.