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Risk‐based fault diagnosis and safety management for process systems
Author(s) -
Bao Huizhi,
Khan Faisal,
Iqbal Tariq,
Chang Yanjun
Publication year - 2011
Publication title -
process safety progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.378
H-Index - 40
eISSN - 1547-5913
pISSN - 1066-8527
DOI - 10.1002/prs.10421
Subject(s) - reliability engineering , univariate , engineering , control chart , fault (geology) , fault detection and isolation , process (computing) , data mining , multivariate statistics , computer science , machine learning , electrical engineering , seismology , actuator , geology , operating system
An innovative methodology of risk‐based fault diagnosis and its integration with safety instrumented system (SIS) is proposed in this article. The proposed methodology uses control chart technique to distinguish abnormal situation from normal operation based on three‐sigma rule and linear trend forecast. Time series moving average techniques are used to perform real‐time monitoring and noise filtering in fault diagnosis processes. Furthermore, risk indicators are used to identify and determine potential fault(s) to minimize the number of false alarms. The proposed methodology is implemented in G2 development environment. Two case studies of a tank filling system and a steam power plant system with SIS1s and SIS2s are conducted in G2 environment. A technique breakthrough from univariate monitoring to multivariate monitoring for fault diagnosis has been achieved during the verification in the steam power plant system. © 2010 American Institute of Chemical Engineers Process Saf Prog, 2011