Multimode Monitoring Method Based on Adaptive Importance Coding Dictionary Learning

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.