## 3 Related Works
### Artificial Intelligence-Based Anomaly Detection Models for Digital Twins  
Digital twin technology is increasingly recognized as a vital tool in real-time anomaly detection and classification within CPSs, smart manufacturing, industrial plants, aircraft systems [3; 22] and other complex control environments [3; 4; 5; 22; 23; 24; 25; 26]. The integration of digital twin allows for the seamless transition from static to dynamic, real-time data processing, enhancing the detection and classification of anomalies before they escalate into more severe issues. This shift is crucial due to the growing complexity and integration of modern systems, which exposes them to new vulnerabilities and attacks. In building and civil infrastructure management, the authors of [27] developed digital twins for anomaly detection, using Bayesian online change-point detection for asset management. A method to detect anomalies by comparing digital twin fingerprints generated from side-channel emissions (acoustic, vibration data) with physical system features was developed [2], but this method depends on the presence of detectable side-channel emissions. [PERSON] et al. [28] introduced a digital twin-driven anomaly detection framework utilizing edge intelligence for early failure detection and maintenance management in industrial systems. In [29], the authors developed a two-phase fault diagnosis method assisted by digital twins, employing deep transfer learning to enhance fault diagnosis in both the development and maintenance phases of manufacturing. [PERSON] et al. [30] explored a deep digital twin (DDT) for predictive health monitoring and automating maintenance scheduling using operational data directly. In another work [31], the authors present a case study on the application of a machine learning-based digital twin for anomaly detection in small-scale systems, focusing on the omni wheels of soccer robots. They demonstrate that even with limited data, ML-based digital twins can effectively identify anomalies, showcasing their potential beyond large-scale industrial applications. State-of-the-art deep neural network models such as Autoencoders, Multi-Layer Perceptrons, Transforms and CNNs have been heavily explored for anomaly detection and classification tasks in time-series sensor data [6; 32; 33]. These models can capture complex patterns and improve the accuracy of anomaly detection  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **SSA Model [13]** & **Isolation Forest [21]** & **TWIN-ADAPT** \\ \hline Online & ✓ & X & ✓ \\ \hline Concept drift adaptation & X & X & ✓ \\ \hline Model evaluation on different anomaly distributions & X & X & ✓ \\ \hline Type of learning & Unsupervised & Unsupervised & Supervised 1  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of related works on application of anomaly detection and identification in smart cleanroom laboratories.  
in digital twin frameworks. Although these works demonstrate sophisticated anomaly detection and identification frameworks for digital twins by employing advanced machine learning or deep learning techniques, they do not explicitly discuss strategies for handling concept drift wherein new types of anomalies may emerge over time or new 'normality' patterns are introduced in the equipment's operational behavior. This opens avenues for further exploration within the context of digital twin technology, especially to maintain the robustness of anomaly detection systems in dynamic environments where system behavior and conditions evolve over time.