Visual analytics and data science tools can be used to process sensor data to surface patterns that predict equipment problems.
For example, in the Energy sector, sensors attached to production equipment transmit parameter readings like pump intake pressure, current, and temperature. The data is analyzed to produce models correlating parameter changes to equipment problems and then used to flag potential problems in new sensor data, so potential issues can be investigated before production is affected.
The same machine learning methods can be applied in your industry to maximize product yield and other use cases.
In this event, we describe the visual analytics, data science, and real-time stream processing capabilities of the TIBCO® Connected Intelligence portfolio for ongoing, real-time equipment management and production optimization.