###### Abstract
For example, a cleanroom's critical infrastructure components such as Fumehoods and high-end scientific equipment such as Electron Microscopes, Vacuum Pumps, and high-resolution Photolithography Units like the Karl Suss MJB3 Contact Mask Aligner [12], all require continuous monitoring to maintain the laboratory's operational standards. Digital twins facilitate this by enabling predictive, preventive and reactive maintenance within industrial laboratories as follows: (1) by continuously monitoring system conditions to predict potential failures before they occur, (2) through advanced analytics to anticipate equipment malfunctions and optimize maintenance schedules and (3) by generating real-time updates and alerts to control the physical environment. Figure 1 shows each of the three critical components of cleanrooms that require real-time monitoring: (a) Fumehood (b) Vacuum Pump and (c) the Karl Suss Lithography Unit. Existing deployments of cyber-physical systems (CPSs) in cleanrooms enhance laboratory functionality by deploying sensors, actuators and edge devices with the physical infrastructure and scientific equipment of the cleanroom labs. For example, Senselet++ [13] uses cost-effective sensors to monitor airflow in Fumehoods and track calibration drifts caused by environmental changes to ensure it remains within safe limits [13]. Similarly, the sensors within the Fumehood are used to monitor air leakage or chemical spillage causing changes in air pressure or hazardous vapor concentrations. Edge devices, which may be microcontrollers or microcomputers such as Raspberry Pis, facilitate the communication of sensor data to the cloud. These edge devices also host the complex data processing algorithms for real-time anomaly detection and identification. Actuators, then, provide real-time alerts to lab managers (in the form of alert messages and alarms), informing them of deviations so they can take immediate action to maintain the required cleanroom  
Figure 1: Critical components of Cleanroom: (**a**) Fumehood, (**b**) Vacuum Pump and (**c**) Lithography Unit. standards. Similarly, to monitor Vacuum Pump malfunctions, temperature and vibration sensors are deployed to detect any changes caused by overheating from nearby thermal furnaces. These sensors generate critical data that are required to be analyzed using various anomaly detection and fault classification models to extract meaningful insights from the data. Despite the advancements in wireless communications and Internet of Things (IoT) technologies facilitating real-time data acquisition in cyber-physical systems, significant challenges persist in capturing and integrating this information. This is largely due to the complexities involved in transforming vast quantities of data into actionable insights. Since digital twins are the virtual representation of cyber-physical systems, they are enabled with real-time monitoring and analytics. Digital twins, serving as virtual representations of these CPSs, utilize data-driven models equipped with various machine learning and deep learning techniques. These models are trained on extensive historical data to provide robust anomaly detection and classification capabilities, ensuring continuous monitoring and analytics within these dynamic environments. However, despite their advanced monitoring capabilities, the effectiveness of these digital twins largely depends on the underlying anomaly detection models that are used for analysis. The static ML models used for data analysis and anomaly detection face several of the following limitations, which can hinder their effectiveness in such controlled IoT-driven smart laboratories:  
* **Concept drift:** Traditional ML models are typically trained on historical data representing specific, static conditions. These pre-trained models operate under the assumption that the training data accurately represent the relationships within all data from the target variables (anomaly or normal). This requires data stationarity throughout the model training, testing and deployment phases.