Integrating IIoT Sensors, Edge Computing, and Machine Learning for Increased Equipment Uptime
Mineral processing, fertilizer and cement processing plants utilize high-horsepower semi-autogenous/ball/rod grinding mills in their operations. These large industrial machines are mission-critical to continuous plant operation and suffer from reliability challenges associated with bearing and gear failures stemming from compromised lubrication.
This presentation explores how an intelligent lubrication condition monitoring system that utilizes industrial internet of things (IIoT) sensors, edge computing and machine learning, can be coupled with used oil analysis to provide operators with real-time online data in order to make data-driven decisions. You will discover how integrating machine performance data with online lubrication quality and locally computed algorithms can deliver actionable information that can prevent potentially catastrophic failures and maintain greater plant uptime while extending the useful life of these high capital cost assets.