Global Information-Based Lifelong Dictionary Learning for Multimode Process Monitoring

Multimode process monitoring plays a significant role in ensuring the stable operation of industrial processes under changing conditions. Due to the continuous emergence of new modes, some adaptive model updating methods are proposed. However, the updated model may forget important features learned in previous modes, thus reducing the monitoring performance. To address this problem, this article proposes a global information-based lifelong dictionary learning (GI-LDL) method for multimode process monitoring. Specifically, this article adopts dictionary atoms as the fundamental units for representing process data and proposes a method for measuring the importance of dictionary atoms based on global information. Then, to ensure that the dictionary retains its representational ability for both new and historical mode data during the mode updating process, a surrogate quadratic loss considering the importance is further proposed to penalize changes of important atoms. Compared with dictionary constraints, finer-grained atomic constraints ensure that the dictionary preserves important features of previous modes while learning features of new modes. Finally, considering that the number of modes in multimode industrial processes is often unknown in advance, this article explicitly derives analytical solutions for dictionary updating, thus it is capable of accommodating ever-increasing modes in real industrial processes. To verify the effectiveness and advancement of the proposed method, extensive experiments are elaborately designed, and experimental results indicate that the proposed method has precise monitoring capabilities for both historical and new modes.