Kernel Ridge Regression Based Modelling and Anomaly Detection for Temperature Control in Textile Dyeing Processes

This article proposes the use of Kernel Ridge Regression (KRR) for modeling and anomaly detection in the temperature control of textile dyeing processes. The anomaly being considered is the inability to heat or cool the dyeing machine temperature. It is concluded that anomaly detection results of the KRR model are satisfactory. The ultimate goal is to find problematic temperature controls rapidly and solve equipment failures of dyeing machines to increase right-first-time dyeing.