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This paper considers the problem of nonstationary process monitoring underfrequently varying operating conditions. Traditional approaches generallymisidentify the normal dynamic deviations as faults and thus lead to high falsealarms. Besides, they generally consider single relatively steady operatingcondition and suffer from the catastrophic forgetting issue when learningsuccessive operating conditions. In this paper, recursive cointegrationanalysis (RCA) is first proposed to distinguish the real faults from normalsystems changes, where the model is updated once a new normal sample arrivesand can adapt to slow change of cointegration relationship. Based on thelong-term equilibrium information extracted by RCA, the remaining short-termdynamic information is monitored by recursive principal component analysis(RPCA). Thus a comprehensive monitoring framework is built. When the systementers a new operating condition, the RCA-RPCA model is rebuilt to deal withthe new condition. Meanwhile, elastic weight consolidation (EWC) is employed tosettle the `catastrophic forgetting' issue inherent in RPCA, where significantinformation of influential parameters is enhanced to avoid the abruptperformance degradation for similar modes. The effectiveness of the proposedmethod is illustrated by a practical industrial system.

Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation