TRC-2018 Learning Zone 40:27
by Stuart Gillen, SparkCognition

Many organizations today are facing challenges in increasing reliability and uptime. Current industry solutions do not offer advanced notice for performing proactive, predictive maintenance. The use of intelligent edge devices to acquire asset sensory data, along with machine learning algorithms to predict when an asset will fail, is becoming more attractive to maintenance managers as they seek new methods to get maintenance costs under control. The use of this technology can augment or even supplement human subject matter experts while providing significant advanced notice of asset health issues by analyzing and learning from past asset health data. In this presentation, we will discuss practical ways in which utilities can get started today and see how others are implementing this technology.