A gradual disturbance detection model of manufacturing cell: A digital twin driven perspective

As a modular component of discrete shop-floors, the manufacturing cell offers specific strengths in detecting operation time fluctuations induced by gradual disturbances in the multi-variety, small-batch production mode. Traditional research on abnormal production state detection in shop-floors typically relies on statistical analysis, machine learning, and deep learning methods. However, these methods demonstrate limitations in both comprehensiveness and effectiveness when applied to gradual disturbance detection. Moreover, these studies could solely address the limitations of gradual disturbance detection, without providing insights into how such detection contributes to improvements in the production process. To this end, this study adopts a digital twin driven perspective to not only detect gradual disturbances, but also to associate disturbance detection with bottleneck alleviation and system performance enhancement. Grounded in the synchronization between the physical manufacturing cell in the physical space and its mirrored virtual counterpart in the virtual space, this study models production activities via actual and virtual dynamic graphs in the data space. Within the model space, we jointly employ the convolutional neural network and the graph convolutional network to extract both structured and graph features from production data. The integration across multiple spaces enables digital twin driven of gradual disturbance detection, contributing to bottleneck alleviation and performance enhancement at the system level. This study's comprehensiveness and effectiveness in detecting gradual disturbances are validated on both simulation and actual datasets. Additionally, experiments that inject gradual disturbances into real production scenarios verify that disturbance detection supports both bottleneck alleviation and overall system enhancement.