Incremental Rank Continual Dictionary Learning for Multimode Process Monitoring With Continually Emerging Operational Mode

Modern industrial processes typically operate under variable conditions due to fluctuations in raw materials and production loads. Consequently, accurate monitoring across multiple operational modes is essential for process optimization. Although some global modeling based methods have been proposed, they struggle to adapt to the continual emergence of new modes. On the other hand, online updating monitoring methods often suffer from “catastrophic forgetting”, i.e., they lose the ability to represent historical modes. To address these challenges, this article proposes a multimode process monitoring method based on Incremental Rank Continual Dictionary Learning (IRCDL). The method integrates the incremental learning framework based on low-rank matrix augmentation with the adaptive weight selector, allowing the dictionary to retain the knowledge of both historical and new modes. Specifically, to address the issue of information redundancy in overcomplete dictionaries, an innovative dictionary construction method based on low-rank matrices weighting is proposed. Using low-rank matrices as the fundamental units for dictionary construction, this method accurately captures the intrinsic features of different modes while significantly reducing redundant parameters. Then, to handle the continual emergence of new modes, an incremental learning framework based on low-rank matrix augmentation is developed. This framework continually adds new low-rank matrices to learn representations of new modes, while keeping the historical low-rank matrices intact to maintain “zero-forgetting” of historical modes, thereby enabling accurate monitoring of multimode processes. Finally, an adaptive weight selector is designed to automatically optimize the weights of low-rank matrices, thereby enhancing the representation of different modes and significantly improving the performance of multimode monitoring. Extensive experiments have demonstrated that the proposed method achieves state-of-the-art continual learning capabilities (i.e., “zero forgetting”) and exceptional process monitoring performance.