Continual learning for multimode dynamic process monitoring with applications to an ultra–supercritical thermal power plant

Zhang, J., Zhou, D., Chen, M. and Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 (2023) Continual learning for multimode dynamic process monitoring with applications to an ultra–supercritical thermal power plant. IEEE transactions on Automation Science and Engineering, 20 (1). pp. 137-150. ISSN 1558-3783 doi: 10.1109/TASE.2022.3144288

Abstract/Summary

This paper introduces a novel sparse dynamic inner principal component analysis (SDiPCA) based monitoring for multimode dynamic processes. Different from traditional multimode monitoring algorithms, a model is updated for sequential modes by memorizing the significant features of existing modes. By adopting the concept of intelligent synapses in continual learning, a loss of quadratic term is introduced to penalize the changes of mode–relevant parameters, where modified synaptic intelligence (MSI) is proposed to estimate the parameter importance. Thus, the proposed algorithm is referred to as SDiPCA–MSI. When a new mode arrives, a set of normal samples should be collected. The previous significant features are consolidated without explicitly storing training samples, while extracting new information from the current mode. Consequently, SDiPCA– MSI can provide outstanding performance for successive modes. Characteristics of the proposed approach are discussed, including the computational complexity, advantages and potential limitations. Compared with several state-of-the-art monitoring methods, the effectiveness and superiorities of the proposed method are demonstrated by a continuous stirred tank heater case and a practical industrial system.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/102530
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	IEEE
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