Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly depends on effective data augmentation and contrastive objective setting. However, most CAGC methods utilize edges as auxiliary information to obtain node-level embedding representation and only focus on node-level embedding augmentation. This approach overlooks edge-level embedding augmentation and the interactions between node-level and edge-level embedding augmentations across various granularity. Moreover, they often treat all contrastive sample pairs equally, neglecting the significant differences between hard and easy positive-negative sample pairs, which ultimately limits their discriminative capability. To tackle these issues, a novel robust attributed graph clustering (RAGC), incorporating hybrid-collaborative augmentation (HCA) and contrastive sample adaptive-differential awareness (CSADA), is proposed. First, node-level and edge-level embedding representations and augmentations are simultaneously executed to establish a more comprehensive similarity measurement criterion for subsequent contrastive learning. In turn, the discriminative similarity further consciously guides edge augmentation. Second, by leveraging pseudo-label information with high confidence, a CSADA strategy is elaborately designed, which adaptively identifies all contrastive sample pairs and differentially treats them by an innovative weight modulation function. The HCA and CSADA modules mutually reinforce each other in a beneficent cycle, thereby enhancing discriminability in representation learning. Comprehensive graph clustering evaluations over six benchmark datasets demonstrate the effectiveness of the proposed RAGC against several state-of-the-art CAGC methods. The code of RAGC could be available at https://github.com/TianxiangZhao0474/RAGC.git.

The emergence of new operating modes due to changes in manufacturing demands or production technologies leads to the occurrence of multimode industrial processes. To address the multimode problem, the dictionary learning method is widely utilized due to its excellent sparse representation technique capability. Despite the abundance of offline data enabling dictionary learning to model various modes, consolidating dictionaries from individual models considerably increases model complexity. Therefore, a method called adaptive importance coding dictionary learning is proposed in this study on the basis of dictionary learning. This approach utilizes dictionary learning techniques to determine the importance of dictionary atoms by encoding frequencies and selecting significant dictionary atoms to form a new dictionary, remarkably reducing the complexity of the dictionary model. In accordance with this idea, a comprehensive framework for monitoring multimode processes, including online fault detection, mode classification, fault isolation, and new mode updates, is introduced. Experimental validations are conducted through numerical examples, simulations of continuous stirred tank reactors, and application in an actual petrochemical process. The experimental results demonstrate the effectiveness and feasibility of the proposed method in monitoring multimode processes.