Beyond conventional statistical process control: Bayesian hierarchical modeling to track distributed industrial equipment performance

This article presents the challenges faced when applying statistical process control (SPC) in today’s evolving industrial landscape. The increased adoption by manufacturers of remote condition monitoring of their products in the hands of customers, driven by enabling technologies like the Internet of Things, makes statistical process control a pivotal tool. However, its implementation requires a careful consideration of the peculiarities imposed by these new usages. All available equipment data and their nuances, such as customer usage conditions and unmeasurable yet impactful variables on product performance, must be considered and used in some way. It is also not possible in this new environment to distinguish between standard phases I and II of SPC. Thus, a model able to automatically identify out of control behavior is required to both signal potential issues and keep out of control observations from contaminating the on-going model estimated parameters. To address these challenges, the authors propose a Bayesian hierarchical model with built-in outlier detection, demonstrated through its application to a real-world remote monitoring system for industrial printers deployed at customer sites. The primary goal extends beyond motivation and aims to deepen the understanding of potential new usages of SPC and their challenges in modern industrial settings.