Statistical monitoring methodologies are constantly evolving to cope with various data complexities, non-standard situations and related questions that often arise in modern day applications. In this context, researchers in the Statistical Process Monitoring (SPM) literature have recently considered Statistical Learning (SL) techniques to define models under more practical assumptions, and thus to allow setting up control charts for monitoring process stability in a variety of situations. However, a rigorous investigation of some of the key issues related to the implementation of the SL based control charts, supporting on-line (Phase II) SPM, has been lacking and is much needed. As a first step in this direction, here we consider a control chart based on the Isolation Forest (IF), an unsupervised SL technique running an ensemble of decision trees, which has recently been extended to the SPM area. We examine key implementation issues related to the selection of a proper Phase I reference sample. In addition, a critical yet underexplored component of ML-based control charts is Post-Signal Diagnostics (PSD), or the fault identification stage after a signal, the interpretation of alarms and the determination (identification) of which features contributed to the detected process anomaly. This study addresses this gap by introducing the SHapley Additive exPlanations (SHAP) framework into the control chart based SPM process. Specifically, we demonstrate how SHAP values can help with PSD, by providing both visual and quantitative insights into the features responsible for the observed shifts in a post signal setting.
zoom link: https://uoc-gr.zoom.us/j/88177475079?pwd=ik9vJRdyknP8xneS5NumlqgQdnplaz.1
