To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel processing test bed. The Beartooth test bed will allow researchers the opportunity to study fuel separation operations that include the use of centrifugal contactors and solvent extraction equipment. In addition to solvent extraction operations, the test bed is designed to support data collections that will allow for the monitoring of process operations in real-time and implementation of machine learning algorithms. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes. Measurements include vibration, acoustic, colorimetric, and thermal among others. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and nontraditionally used sensors for the discovery and identification of important process events. The identification of process events has the potential to provide information to process operators and indicate process anomalies. This paper presents an overview of planned sensors and experiments that will focus on signal discovery.