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A digital twin (DT) monitors states of the physical twin (PT) counterpart andprovides a number of benefits such as advanced visualizations, fault detectioncapabilities, and reduced maintenance cost. It is the ability to be able todetect the states inside the DT that enable such benefits. In order to estimatethe desired states of a PT, we propose the use of a Kalman Filter (KF). In thistutorial, we provide an introduction and detailed derivation of the KF. Wedemonstrate the use of KF to monitor and anomaly detection through an incubatorsystem. Our experimental result shows that KF successfully can detect theanomaly during monitoring.

Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter