I-STEP – A Case Study: Machine Learning Powered Condition Monitoring of a Linear Motion Industrial Vibrating Screen

Vibrating screens have become nearly ubiquitous across various industries, primarily used to classify feed after comminution in diverse processes. The vibration of the screen enhances screening efficiency by minimizing the blinding effect on screen surfaces, while simultaneously increasing the potential throughput rate. These benefits, among others, have spurred the development of numerous designs of vibrating screens, with the most fundamental types being linearmotion and circular-motion screens. (Schubert, 2003)
Given the ubiquity of vibrating screens, analyzing their maintenance requirements and operational efficiency is crucial. Many screens are currently operated inefficiently, often in a "run- ofailure" manner (Wang et al., 2020). This multi-year research project at the Chair of Mineral Processing in collaboration with IFE Aufbereitungstechnik GmbH, aims to develop a solution for improving the operational efficiency of the screening process as well as draw possible conclusions about the condition state of the screen, based on its measured vibration pattern.