Premium
Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis
Author(s) -
Shang Chao,
Yang Fan,
Gao Xinqing,
Huang Xiaolin,
Suykens Johan A.K.,
Huang Dexian
Publication year - 2015
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.14888
Subject(s) - slowness , process (computing) , feature (linguistics) , statistical process control , computer science , dynamics (music) , work in process , data mining , statistics , control theory (sociology) , engineering , mathematics , artificial intelligence , physics , linguistics , philosophy , acoustics , operating system , operations management , control (management) , quantum mechanics
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. © 2015 American Institute of Chemical Engineers AIChE J , 61: 3666–3682, 2015