Dynamic failure analysis of process systems using principal component analysis and Bayesian network

S.A. Adedigba, Faisal Khan, Ming Yang

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA; while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.
Original languageEnglish
Pages (from-to)2094-2106
JournalIndustrial & Engineering Chemistry Research
Volume56
Publication statusPublished - 2017

Fingerprint

Bayesian networks
Principal component analysis
Failure analysis
Petroleum
Distillation
Crude oil
Decision making

Cite this

Dynamic failure analysis of process systems using principal component analysis and Bayesian network. / Adedigba, S.A. ; Khan, Faisal; Yang, Ming.

In: Industrial & Engineering Chemistry Research, Vol. 56, 2017, p. 2094-2106.

Research output: Contribution to journalArticle

@article{4b60a6edc42e47a0ba43c36ec4837826,
title = "Dynamic failure analysis of process systems using principal component analysis and Bayesian network",
abstract = "Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA; while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.",
author = "S.A. Adedigba and Faisal Khan and Ming Yang",
year = "2017",
language = "English",
volume = "56",
pages = "2094--2106",
journal = "Industrial & Engineering Chemistry Research",
issn = "0888-5885",
publisher = "American Chemical Society",

}

TY - JOUR

T1 - Dynamic failure analysis of process systems using principal component analysis and Bayesian network

AU - Adedigba, S.A.

AU - Khan, Faisal

AU - Yang, Ming

PY - 2017

Y1 - 2017

N2 - Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA; while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.

AB - Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA; while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.

M3 - Article

VL - 56

SP - 2094

EP - 2106

JO - Industrial & Engineering Chemistry Research

JF - Industrial & Engineering Chemistry Research

SN - 0888-5885

ER -