Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring

• URL: http://arxiv.org/abs/2202.11295v1
• Date: Wed, 23 Feb 2022 03:57:59 GMT
• Title: Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring
• Authors: Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong
• Abstract summary: A novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.
• Score: 2.9631016562930546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.

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