###### Abstract  
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as \"Cleanrooms\"). Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT's continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT's adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT's robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings. t 202416623  
the robustness of system models by identifying conditions where the models are valid and stable. Any deviations from these established norms are leveraged for predictive monitoring, enhancing system reliability through proactive anomaly detection. However, these models require significant domain expertise and knowledge, which limits their application across heterogeneous environments. Additionally, their effectiveness is constrained by the accuracy of the initial assumptions and the feasibility of the working conditions derived from the analyses. Alternatively, data-driven approaches utilize real-world data to create virtual representations of physical systems [5; 11]. These models require substantial amounts of data to train effectively. Once deployed as a digital twin, they continuously analyze incoming data to identify and report any deviations, anomalies, or faults in the physical system. Both approaches aim to optimize system performance and reliability, which makes them powerful tools for enabling preventive, predictive and reactive maintenance in varied application domains through real-time monitoring as well as informed decision making. In the context of scientific manufacturing laboratories (commonly known as \"_Cleanrooms_\"), which are strictly controlled environments for industries like semiconductor, pharmaceuticals, microelectronics and nanotechnology, data-driven digital twins play a vital role. These digital twins provide a high level of data monitoring and operational control to prevent contamination and ensure the integrity of sensitive manufacturing processes.