  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.
