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Risk‐based process system monitoring using self‐organizing map integrated with loss functions
Author(s) -
Yu Hongyang,
Khan Faisal,
Garaniya Vikram
Publication year - 2016
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22480
Subject(s) - computer science , benchmark (surveying) , process (computing) , fault detection and isolation , nonlinear system , hazard , data mining , dimensionality reduction , self organizing map , fault (geology) , curse of dimensionality , visualization , reliability engineering , artificial intelligence , engineering , artificial neural network , chemistry , physics , geodesy , organic chemistry , quantum mechanics , seismology , geology , actuator , geography , operating system
The conventional dynamic risk assessment technique does not consider the effect of nonlinear interactions among process variables in its operational risk estimation. Thus, this type of technique fails to provide a realistic estimation of the operational risk of complex industrial processes. To address this issue, a multivariate risk‐based process monitoring technique is proposed. This technique takes advantage of the powerful nonlinear dimensionality reduction and visualization power of the self‐organizing map to identify the origin and propagation path of the fault. Through integration with the inverted normal loss function, a robust estimation of the hazard potential and operational risk of process operations can be achieved. The proposed technique is tested with two fault conditions in the benchmark Tennessee Eastman chemical process. The results show promising performance.