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A novel data‐driven methodology for fault detection and dynamic risk assessment
Author(s) -
Amin Md. Tanjin,
Khan Faisal,
Ahmed Salim,
Imtiaz Syed
Publication year - 2020
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.23760
Subject(s) - fault tree analysis , event tree analysis , computer science , fault detection and isolation , data mining , naive bayes classifier , bayesian network , reliability engineering , bayes' theorem , multivariate statistics , classifier (uml) , bayes classifier , process (computing) , bayesian probability , artificial intelligence , machine learning , engineering , support vector machine , operating system , actuator
Abstract This paper presents a novel methodology for dynamic risk analysis, integrating the multivariate data‐based process monitoring and logical dynamic failure prediction model. This concept for dynamic risk analysis is comprised of the fault assessment and dynamic failure prognosis modules. A combination of the naïve Bayes classifier, Bayesian network, and event tree analysis is utilized to manifest the concept. The naïve Bayes classifier is used for fault detection and diagnosis; it also generates a multivariate probability for a fault class in each time‐step, which is used for dynamic failure prognosis by different paths a fault can lead a process to failure. The proposed framework has been applied to two process systems: a binary distillation column and the RT 580 experimental setup in four fault scenarios, and it is found the developed technique can effectively monitor the process and predict the failure.