Industrial equipment is typically serviced on a fixed schedule, irrespective of actual operating condition, resulting in wasted labor and risk of unexpected and undiagnosed equipment failures.
Once instrumented with sensors and networked with each other, devices can be monitored, analyzed and modeled for improved performance and service. This creates a “digital twin” to any physical object, reflecting the state and attributes of the object – regardless of time, state and position.
“Twinning” a piece of equipment allows human operators to constantly monitor performance data and generate predictive analytics. Complex twins like those of gas turbines interpret data from hundreds of sensors, understand failure conditions, track anomalies, and can be used to regulate production based on real-time demand.
But “digital twins” can be crated for both simple objects and for whole factories. Insights can be applied to a lot of business issues helping increase quality and efficiency.
Companies working within predictive maintenance- and analytics include; DataRPM, C3IOT, Uptake and SparkCognition.