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

TL;DR

This paper introduces the use of the Kalman Filter for state estimation in digital twins, demonstrating its effectiveness in monitoring and anomaly detection within an incubator system.

Contribution

It provides a detailed derivation of the Kalman Filter and applies it to digital twin monitoring, showcasing its practical utility for anomaly detection.

Findings

KF successfully detects anomalies during monitoring

Effective for real-time state estimation in digital twins

Demonstrated on an incubator system

Abstract

A digital twin (DT) monitors states of the physical twin (PT) counterpart and provides a number of benefits such as advanced visualizations, fault detection capabilities, and reduced maintenance cost. It is the ability to be able to detect the states inside the DT that enable such benefits. In order to estimate the desired states of a PT, we propose the use of a Kalman Filter (KF). In this tutorial, we provide an introduction and detailed derivation of the KF. We demonstrate the use of KF to monitor and anomaly detection through an incubator system. Our experimental result shows that KF successfully can detect the anomaly during monitoring.