A physics-driven analytics platform aids in improvements to the reliability and efficiency of connected mechanical systems. The solution analyzes large quantities of time series data from IoT sensors to help identify issues affecting system performance in real-time as well as provide accurate data for predictive maintenance. Our presenter chose a time series database for its high ingest and storage of time series data as well as its ability to easily send this data into their systems for predictive analytics.
During this latest Data Science Central webinar in association with IoTCentral, learn how using a purpose-built time series database helps to continuously optimize reliability of their customers’ connected mechanical systems.
Speaker:
Jon Herlocker, President and CEO – Tignis
Hosted by:
Rafael Knuth, Contributing Editor – Data Science Central
