Equipment Health Monitoring
MapExDVR is integrated as a module within PreMA — the Predictive Machinery Analyzer developed by Dundics Enterprises Inc. MapExDVR supplys reconciled plant data that powers PreMA's equipment health and diagnostics capabilities.
MapExDVR as a Module in PreMA
MapExDVR provides PreMA with a complete, reconciled set of flows, temperatures, pressures, and enthalpies — including virtual sensors derived from first-law physics — that PreMA uses as inputs to its equipment health and diagnostics models. The screen images on this page are from PreMA
PreMA — Predictive Machinery Analyzer by Dundics Enterprises Inc.
What MapExDVR Contributes
MapExDVR performs heat-balance data validation and reconciliation, producing a complete set of reconciled thermodynamic states throughout the plant cycle. This includes virtual sensors — calculated values at locations where no physical sensor exists. These outputs are passed to PreMA as enriched inputs for its equipment health and fault detection models.
Covariant Stress Factors
The reconciled MapExDVR outputs serve as covariant stress factors within PreMA's equipment health models. For example, the health of first-stage blades in an aero-derivative gas turbine may use measured HPT exhaust gas temperatures (T48) alongside blade metal temperatures calculated by MapExDVR as stress inputs to PreMA's hazard models.
Fault Detection & Diagnostics (BBN)
PreMA uses Bayesian Belief Networks (BBN) at the equipment level to detect anomalies and identify probable failure modes. Sensor deviations from expected behavior — informed by the richer data set that MapExDVR provides — are evaluated to calculate Failure Mode Health values as the probability of a failure given all available evidence.
Predictive Machinery Analyzer (PreMA)
PreMA is a predictive equipment health and diagnostics platform developed by Dundics Enterprises Inc. It integrates MapExDVR as an analytic module to leverage heat-balance reconciliation and virtual sensors. The combination enables PreMA to apply its health and diagnostics models to a more complete and accurate data set than measured plant data alone can provide.