Speaker
Description
Modern industrial systems generate real-time multichannel profile data for process monitoring and fault diagnosis. While most methods focus on detecting process mean shifts, identifying changes in the covariance structure is equally important, as process behavior often depends on interdependence among multiple variables. However, monitoring covariance in multichannel profiles is exacerbated by the high dimensionality and unknown, possibly sparse, shift patterns.
We address these problems by leveraging functional graphical models to represent conditional dependencies among profiles and enable interpretable monitoring. The proposed approach combines penalized likelihood ratio tests with varying penalties to adapt to diverse covariance changes. A diagnostic procedure based on change-point detection then identifies which relationships have shifted. A simulation study and a real-world case involving temperature profile monitoring are carried out to demonstrate the method’s effectiveness and practical applicability.
Acknowledgments
The research activity of C. Capezza, D. Forcina, and A. Lepore was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004).
The research activity of B. Palumbo was carried out within the MOST - Sustainable Mobility National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1033.17-06-2022, CN00000023).
This work reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
| Special/ Invited session | Young Statisticians |
|---|---|
| Classification | Both methodology and application |
| Keywords | Profile Monitoring, Statistical Process Control, Functional Graphical Models, Multichannel Profiles |