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

TL;DR

This paper introduces a continual learning approach called PSFA-EWC that extends probabilistic slow feature analysis with elastic weight consolidation to improve multimode dynamic process monitoring, retaining knowledge across modes.

Contribution

It develops a novel continual learning method for multimode process monitoring by integrating EWC with PSFA, enabling knowledge retention and transfer across modes.

Findings

Effective in monitoring a stirred tank heater

Successfully applied to a coal pulverizing system

Retains significant features across multiple modes

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.