Issue link: https://resources.mouser.com/i/1442808
advanced analytics, each DT is used to forecast its specific asset's health and performance over the asset's lifetime. In most industrial use cases, DTs run on cloud-based industrial platforms, which are designed to ingest and manage massive volumes of machine sensor data. The platforms execute the DTs along with their integrated analytics models for components of the power plant. This process sets in motion the measurement of asset health, wear and tear, and performance, using customer-defined key performance indicators (KPIs) and business objectives. The platforms can also integrate business applications and a business rules engine to allow the plant's executives, managers, and workers to interact with the DTs in real-time. Functionally, a DT can perform a few discrete roles. These roles include: Lifing Lifing assesses how each asset in the power plant will age relative to its operation and exposure, specifically to optimize the maintenance versus mission reliability of each asset. Anomalies Detections of anomalies and predictions of faults occur using physics and data-based prognostic models. When fused with lifing models, anomaly models can increase the accuracy of machine-life curves and customize maintenance needs. Thermal Efficiency Simulation of all parameters can affect thermal efficiency, plant capacity, and emissions. Transients Simulation of a plant's ability to react to changes in environmental 14