Chatter Detection in Hot Strip Mill Process based on Modified Independent Component Analysis Multivariate statistical process monitoring (MSPM) have been applied to process monitoring for industrial processes. The conventional method for a statistical monitoring model is principal component analysis (PCA). However, this is not sufficient to extract meaningful information in nonGaussian data, which is the property of the process data in many industrial processes. Alternatively, the modified independent component analysis (MICA) method can be used to give meaningful information up to higher order statistics, which improves some drawbacks of independent component analysis (ICA) method. In this paper, we propose a protocol to monitor a chatter phenomenon in a hot strip mill process (HSMP) based on modified independent component analysis (MICA). First, we develop the chatter index (CI) that represent the degree of a chatter numerically. The statistical monitoring model for a chatter detection is constructed by using the chatterfree data, which is classified by CI. From the chatter monitoring model, a chatter detection rate of 86.7% is achieved.

Detection of Machine Tool Anomalies from Bayesian Changepoint Recurrence Estimation In this study, we consider the problem of detecting processrelated 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 shaperelated features. In recent years, shapelets dominated the field of shaperelated features. Unfortunately, they involve a high computational burden due to hyperparameter optimization.We introduce alternative shaperelated 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, nonrecurrent 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, changepointrelated features are introduced and compared to shapelets and waveletbased features in a case study comprising realworld 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 waveletbased features.