  For multimode processes, one generally establishes local monitoring models
corresponding to local modes. However, the significant features of previous
modes may be catastrophically forgotten when a monitoring model for the current
mode is built. It would result in an abrupt performance decrease. It could be
an effective manner to make local monitoring model remember the features of
previous modes. Choosing the principal component analysis (PCA) as a basic
monitoring model, we try to resolve this problem. A modified PCA algorithm is
built with continual learning ability for monitoring multimode processes, which
adopts elastic weight consolidation (EWC) to overcome catastrophic forgetting
of PCA for successive modes. It is called PCA-EWC, where the significant
features of previous modes are preserved when a PCA model is established for
the current mode. The optimal parameters are acquired by differences of convex
functions. Moreover, the proposed PCA-EWC is extended to general multimode
processes and the procedure is presented. The computational complexity and key
parameters are discussed to further understand the relationship between PCA and
the proposed algorithm. Potential limitations and relevant solutions are
pointed to understand the algorithm further. Numerical case study and a
practical industrial system in China are employed to illustrate the
effectiveness of the proposed algorithm.
