Predictive analytics
Early insights for better decisions in plant operations: Digital twins provide transparency for predictive maintenance, high availability and cost-effective operation.
Digital predictive maintenance solutions for maximum plant reliability
High levels of reliability and availability of plant are essential for cost-effective operation and are hallmarks of successful, optimised asset management. Digital tools used in the context of predictive maintenance provide an excellent basis for the reliable early detection of process changes and emerging damage. This improves planning, creates scope for work preparation and allows the conversion of unplanned downtime into planned downtime. Substantial costs for procuring balancing energy or short-term ad hoc work can thus be reduced. Maintenance and operations staff are alerted to process weaknesses and emerging issues in a targeted, reliable and proactive manner and can develop solutions based on the underlying digital twins, regardless of how and under what conditions the plants are currently being operated.
Predictive Maintenance (HQ)
Reliable detection of changes at critical points in the process using digital twins is essential for the acceptance and usability of predictive solutions. The SR::SPC HQ approach integrates known technical correlations into data-driven modeling, combined with robust statistical methods designed for energy-intensive plants.
Anomaly Detection (ML)
To ensure that nothing is overlooked, the SR::SPC ML module monitors the sensors of (sub)systems using state-of-the-art machine learning techniques. This makes it possible to identify trends and changes in the systems—essentially acting as a fingerprint of the plant. For example, unintended deviations can be detected quickly and reliably following shutdowns or maintenance work.
Asset Management of Decentralized Facilities
The smaller the assets become, the more important it is to truly integrate asset management and predictive maintenance. This is the only way to identify the critical, economically relevant processes amid the flood of data, locations, and reporting requirements. Together with an external partner, we provide an integrated platform for the comprehensive fulfillment of this task, thereby enabling the optimization of operations—from a techno-economic perspective, so to speak.
