Detection of Machine Tool Anomalies from Bayesian Changepoint Recurrence Estimation

Abstract

In this study, we consider the problem of detecting process-related anomalies for machine tools. The similar shape of successive sensor signals, which arises due to the same process step sequence applied to each workpiece, suggests extracting shape-related features. In recent years, shapelets dominated the field of shape-related features. Unfortunately, they involve a high computational burden due to hyperparameter optimization.We introduce alternative shape-related features relying on abrupt signal changes (changepoints) reflecting the changes of process steps. During normal operation, changepoints follow a highly recurrent pattern, i.e., appear at similar locations. Thus, being able to distinguish regular, recurrent from abnormal, non-recurrent changepoints allows detecting process anomalies.For changepoint recurrence estimation, we extend the Bayesian Online Changepoint Detection (BOCPD) method. The extension allows distinguishing normal and abnormal changepoints relying on empirical estimates of the changepoint recurrence distribution. Subsequently, changepoint-related features are introduced and compared to shapelets and wavelet-based features in a case study comprising real-world machine tool data.Qualitative results verify changepoint locations being comparable to shapelet locations found by the FLAG shapelet approach. Furthermore, quantitative results suggest superior classification performance both to shapelets and wavelet-based features.