Continual Learning Enabled Smart Industrial Edge Computing Platforms

Industries increasingly depend on deep learning-based predictive quality systems to anticipate product quality using process data. However, the dynamic nature of production processes poses a significant challenge. These changes can render previously trained models ineffective, and thus, Memory Aware Synapses (MAS)-based continual learning approach for edge-enabled industries is adopted. This work extends MAS with knowledge transfer and a time-efficient MAS hyperparameter selection mechanism using a hybrid grid search and Particle Swarm Optimization mechanism. The experimental analysis demonstrates that the proposed mechanism reduces the overall model loss by 39.03% and 27.18% compared to MAS mechanisms, respectively, for two real-world use cases. An Android-based mobile application is designed as a ready-to-use solution for the mobile edge device.