Orthogonal Decoupled Continual Dictionary Learning for Multimode Process Monitoring

Résumé fourni par la source

Modern industrial processes are highly complex and dynamically evolving, with new modes emerging due to variations in raw materials, production environments, and other factors. Traditional monitoring methods often suffer from catastrophic forgetting during updates, where old knowledge is overridden. Although continual learning offers an effective solution, existing methods tend to overprotect historical knowledge, limiting the adaptability to new modes. To address the above problems, we propose orthogonal decoupled continual dictionary learning (ODCDL), which orthogonally decouples the dictionary space into stability and plasticity subspaces. The stability space preserves representations of historical modes to mitigate forgetting, while the plasticity space allows flexible updates for learning new modes. To balance the tradeoff between stability and plasticity, we impose dynamic constraints with varying strengths on the two subspaces. This design enhances both retention of old knowledge and learning of new knowledge, ensuring accurate monitoring across all operating conditions. Extensive experiments have demonstrated that the proposed method outperforms several state-of-the-art methods, exhibiting superior continual learning capability and process monitoring performance.

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