Equipment failure is a big deal! The global process industry alone is said to lose billions of dollars annually due to costly downtime caused by failing machinery. The situation is similar in other industries. But it doesn't have to be that way. The downtime can be avoided thanks to modern health monitoring and predictive maintenance techniques. By optimizing the maintenance, they help you increase the availability of the equipment!
Based on a digital twin
Predictive maintenance is an Internet-of-Things (IoT) application where sensor data from the physical process acts on an operational digital twin that predicts the remaining life of the equipment. The digital twin can either be a data-driven model, a simulation-driven model or based on a hybrid approach, combining machine learning (ML) and simulation.
Why predictive maintenance?
- Detect anomalies for early knowledge
- Avoid costly downtime
- Cost effective maintenance
- Performance optimization
- Root cause analysis
Contact us to learn more about how we can support your predictive maintenance initiatives!