###### Abstract
However, this assumption is often not met in practical scenarios, particularly in CPS applications that involve streaming data analysis. In numerous industrial contexts, data gathered from manufacturing and operational processes inherently exhibit non-stationary characteristics [1]. For example, over time, the physical components of Vacuum Pumps may wear down, subtly changing their operational efficiency and characteristics. This wear and tear can alter the vibration signatures, heat emissions, or other measurable parameters that were initially used to train the anomaly classification models. Similarly, changes in the operational parameters of the Pump, such as adjustments to speed, pressure settings, or duty cycles to accommodate different scientific experimental tasks in cleanrooms, can lead to concept drift. These operational changes can create new data patterns that the original model was not trained to recognize or handle. This can lead the model performance to deteriorate over time on new, unseen data of which its statistical characteristics have changed significantly from the training dataset. This shift can make the separation curve ambiguous for pre-trained models for correctly distinguishing the normal and anomalous data instance, affecting performance. This phenomenon, known as **concept drift**, can render previously trained models outdated or irrelevant. Figure 2 illustrates the challenge of concept drift in a data-driven digital twin model that has been trained on historical data distributions for anomaly classification. When the real-time data conforms to the identical and independently distributed (iid) model, the performance remains significantly high. However, as operational changes or sensor variations lead to drifts in data distribution (due to the existence of concept drift), the model's performance drastically decreases. This highlights the critical need for adaptive learning mechanisms within digital twins to fit the current concepts of the new data streams. * **Lack of continuous learning:** Traditional ML models require manual retraining and fine-tuning to adapt to new data or changes within the environment, a process that is resource-intensive and impractical for real-time applications. These models typically do not support continuous learning, which is essential for adapting to unpredictable changes within the non-stationary streams of data from CPSs within cleanrooms. * **Data quality and volume:** The performance of these models heavily depends on the quality and volume of the data that they were trained on, often compromised by missing values or noise due to the complex equipment and multidimensional features involved. To overcome these challenges, the integration of digital twins as adaptive anomaly identification systems represents a transformative solution. We propose, **TWIN-ADAPT**, a continuous learning framework for digital twin-enabled real-time anomaly classification in IoT-driven smart manufacturing laboratories (cleanrooms). The digital twin framework of TWIN-ADAPT consists of a continual learning-based gradient boosting classifier, LightGBM, known for efficiently handling complex data structures in dynamic environments [14]. These models are deployed on edge devices within the cyber-physical systems directly connected to the sensors. These sensors continuously produce real-time data streams that are fed into the adaptive anomaly classification model of the digital twin operating on the edge devices. The TWIN-ADAPT framework of the digital twin leverages a dual-window strategy combining an adaptive sliding window and a fixed size sliding window for concept drift detection, adaptation and data learning. The sizes of these windows are determined based on the arrival of the data streams. When new data arrive, both the adaptive and fixed windows slide forward, and the system evaluates the classification accuracy of data within the current window against the previous window.