  This paper considers the problem of nonstationary process monitoring under
frequently varying operating conditions. Traditional approaches generally
misidentify the normal dynamic deviations as faults and thus lead to high false
alarms. Besides, they generally consider single relatively steady operating
condition and suffer from the catastrophic forgetting issue when learning
successive operating conditions. In this paper, recursive cointegration
analysis (RCA) is first proposed to distinguish the real faults from normal
systems changes, where the model is updated once a new normal sample arrives
and can adapt to slow change of cointegration relationship. Based on the
long-term equilibrium information extracted by RCA, the remaining short-term
dynamic information is monitored by recursive principal component analysis
(RPCA). Thus a comprehensive monitoring framework is built. When the system
enters a new operating condition, the RCA-RPCA model is rebuilt to deal with
the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to
settle the `catastrophic forgetting' issue inherent in RPCA, where significant
information of influential parameters is enhanced to avoid the abrupt
performance degradation for similar modes. The effectiveness of the proposed
method is illustrated by a practical industrial system.
