Speaker
Description
The research presented showcases a collaboration with a leading printer manufacturer to facilitate the remote monitoring of their industrial printers installed at customer sites. The objective was to create a statistical model capable of automatically identifying printers experiencing more issues than expected based on their current operating conditions. To minimize the need for extensive data collection, a unified model was developed for all printers, using a hierarchical approach. By incorporating a hierarchical set of random effects, information sharing among the installed printer base was enabled, while also accounting for each printer's unique characteristics. The model was implemented using a Bayesian framework, enabling automatic identification of out-of-control situations.
| Classification | Both methodology and application |
|---|---|
| Keywords | SPC Bayesian conditional moniitoring |