期刊
AICHE JOURNAL
卷 61, 期 11, 页码 3666-3682出版社
WILEY
DOI: 10.1002/aic.14888
关键词
latent variable models; slow feature analysis; process monitoring; alarm removal; fault diagnosis
资金
- National Basic Research Program of China [2012CB720505]
- National Natural Science Foundation of China [61433001]
- Tsinghua University Initiative Scientific Research Program
- ERC AdG A-DATADRIVE-B
- IUAP-DYSCO
- GOAMANET
- OPTEC
- Tsinghua University
- BIL
Latent variable (LV) models have been widely used in multivariate statistical process monitoring. However, whatever deviation from nominal operating condition is detected, an alarm is triggered based on classical monitoring methods. Therefore, they fail to distinguish real faults incurring dynamics anomalies from normal deviations in operating conditions. A new process monitoring strategy based on slow feature analysis (SFA) is proposed for the concurrent monitoring of operating point deviations and process dynamics anomalies. Slow features as LVs are developed to describe slowly varying dynamics, yielding improved physical interpretation. In addition to classical statistics for monitoring deviation from design conditions, two novel indices are proposed to detect anomalies in process dynamics through the slowness of LVs. The proposed approach can distinguish whether the changes in operating conditions are normal or real faults occur. Two case studies show the validity of the SFA-based process monitoring approach. (c) 2015 American Institute of Chemical Engineers
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