A phase I multi-modelling approach for profile monitoring of signal data

Many industrial processes exhibit multiple in-control signatures, where signal data vary over time without affecting the final product quality. They are known as multimode processes. With regard to profile monitoring methodologies, the existence of multiple in-control patterns entails the study and development of novel monitoring schemes. We propose a method based on coupling curve classification and monitoring that inherits the so-called ‘multi-modelling framework’. The goal is to design a monitoring tool that is able to automatically adapt the control chart parameters to the current operating mode. The proposed approach allows assessing which mode new data belong to before applying a control chart to determine if they are actually in control or not. Contrary to mainstream multi-modelling techniques, we propose extending the classification step to include a novelty detection capability, in order to deal with the possible occurrence of in-control operating modes during the design phase that were not observed previously. The functional data depth paradigm is proposed to design both the curve classification and the novelty detection algorithm. A simulation study is presented to demonstrate the performances of the proposed methodology, which is compared against benchmark methods. A real case study is presented too, which consists of a multimode end-milling process, where different operating conditions yield different cutting force profile patterns.